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Database Concept Map http://wikieducator.org/CCNC/CCNC_Module_5/The_database_application/Database_Concepts/How_a_Database_is_Organized .
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Database reports provide us with the ability to further analyze ou.docx
Database reports provide us with the ability to further analyze our data, and provide it in a format that can be used to make business decisions. Discuss the steps that you would take to ensure that we create an effective report. What questions would you ask of the users?
Data presentation should be designed to display correct conclusions. What issues should we think about as we prepare data for presentation? Discuss the different methods that we can use to present data in a report. What role does the audience play in selecting how we present the data?
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DataInformationKnowledge1. Discuss the relationship between.docx
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1. Discuss the relationship between data, information, and knowledge. Support your discussion with at least 3 academically reviewed articles.
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DataHole 12 Score6757555455555455575775655565656555655656556566643.docx
DataHole 12 Score67575554555554555757756555656565556556565565666434686664656566664555575656546555557554556655655465555565546555655467555646457664545665556555644554585456546654565546664566665566666675675665665656766555565486555567676645645575555575665455554655556556575555555455654555655666667665654655556657656558536666536755465655455755755666665545656565655555545545666564656443545655646445567547565654565545565676456544455446455755645655665567565554465466665
State Legislatures
(Part I)
POLS 2212
Legislatures, Policy-Making, and Political Science
• Legislative process is only one part of policy-making
• States are better venue for understanding policy-making
process overall
• Interactions between components are more transparent
• Less ‘political theater’ than national level
• More cases, more variation, more data
• What role do legislatures play in the overall policy-making
process??
• How do legislative-executive relations affect policy outcomes??
Agenda Setting
Formulation /
Negotiation
Adoption /
Enactment
Implementation
Evaluation
Revision /
Termination
• Public attention is focused on an issue
• Collective recognition of problem
Agenda Setting
• Potential solutions are offered
• Some public discourse over options
Formulation / Negotiation
•
Solution
is agreed upon and made into official policy /
law
Adoption / Enactment
• Policy is converted into actionable rules
Implementation
• Fairness, effectiveness, efficiency of policy and rules are
evaluated
Evaluation
• Improvements or changes to policy are made
Revision / Termination
Agenda Setting
• Parties
• Public opinion
• Advocacy groups /
entrepreneurs
Formulation /
Negotiation
• Party leadership
• Interest groups
• Legislature type
• Legislative-executive
relations
Adoption / Enactment
• Legislative-executive
relations
Implementation
• Type of executive
• Bureaucracy
Evaluation
• Social scientists
• Advocacy groups
• Legislative
committees
• State courts
Revision / Termination
• State courts
• Federal courts
‘Professional’
Model
‘Citizen-
Legislator’
Model
Work Load
Nearly full-
time
Part-time
Session
Year-round,
annual
Short-term,
possibly
biannual
Compensation
Medium-high
(over median
for state
employees)
Fairly low
Staff
Large, semi-
permanent
Small, likely
shared
Conceptualizing State Legislatures
Professional Hybrid / Mixture Citizen
State Legislatures
• GA Legislature
• $17k base +per
diem
• $22k – $24k total
Discussion Question
• What are some of the potential benefits /
drawbacks of each of these two models??
State Legislatures and Political Careers (Peverill Squire)
• ‘Career’ Legislatures (Congress)
• Sufficiently high pay
• Minimal incentive to ‘move up’
• Expectation of long tenure
• Heavy time commitment
• ‘Springboard’ Legislatures
• Other positions have higher pay, more prestige
• Expectation of limited tenure
• May be term lim.
DataDestination PalletsTotal CasesCases redCases whiteCases organi.docx
DataDestination PalletsTotal CasesCases redCases whiteCases organicWeek of OrderPallets (Bins)Oakland CA4853762648.002648.0080172Portland OR4853762438.002438.00500148Portland OR4853762438.002438.00500124Riverside CA4853762673.002673.0030118Seattle WA4853762638.002638.00100112Seattle WA4853762638.002638.0010016Oakland CA2831361518.001518.0010013Oakland CA2831361518.001518.0010011Riverside CA2831361512.001512.001121Oakland CA2426881288.001288.001121Oakland CA2426881324.001324.00401Oakland CA2426881288.001288.001121Oakland CA2426881324.001324.00401Oakland CA2426881294.001294.001001Riverside CA2426881288.001288.001121Riverside CA2426881288.001288.001121Riverside CA2426881329.001329.00301Riverside CA2426881288.001288.001121Riverside CA2426881288.001288.001121Riverside CA2426881329.001329.00301Seattle WA2426881294.001294.001001Oakland CA2224641192.001192.00801Riverside CA2224641176.001176.001121Riverside CA2224641176.001176.001121Riverside CA2224641202.001202.00601Seattle WA2224641120.001120.002241Seattle WA2224641176.001176.001121Portland OR121344422.00422.005001Portland OR121344616.00616.001121Portland OR111232560.00560.001121Portland OR111232560.00560.001121Portland OR111232491.00491.002501Oakland CA3336112.00112.001121Oakland CA3336112.00112.001121Oakland CA3336118.00118.001001Portland OR333693.0093.001501Portland OR3336168.00168.0001Portland OR3336112.00112.001121Portland OR3336118.00118.001001Riverside CA3336112.00112.001121Riverside CA3336112.00112.001121Seattle WA3336163.00163.00101Seattle WA333656.0056.002241Seattle WA3336112.00112.001121Seattle WA333656.0056.002241Oakland CA11120.000.001121Portland OR1112-69.00-69.002501Seattle WA111253.0053.0061Seattle WA11126.006.001001Seattle WA111251.0051.00101Seattle WA11120.000.001121Oakland CA4853762588.002588.002002Oakland CA4853762638.002638.001002Oakland CA4853762632.002632.001122Oakland CA4853762638.002638.001002Oakland CA4853762632.002632.001122Portland OR4853762338.002338.007002Seattle WA4853762632.002632.001122Seattle WA4853762576.002576.002242Oakland CA2831361512.001512.001122Riverside CA2831361512.001512.001122Riverside CA2831361518.001518.001002Oakland CA2426881304.001304.00802Oakland CA2426881294.001294.001002Oakland CA2426881288.001288.001122Oakland CA2426881324.001324.00402Oakland CA2426881304.001304.00802Oakland CA2426881288.001288.001122Portland OR2426881294.001294.001002Riverside CA2426881329.001329.00302Riverside CA2426881288.001288.001122Riverside CA2426881329.001329.00302Riverside CA2426881329.001329.00302Riverside CA2426881288.001288.001122Riverside CA2426881288.001288.001122Seattle WA2426881288.001288.001122Seattle WA2426881232.001232.002242Seattle WA2426881294.001294.001002Seattle WA2426881288.001288.001122Seattle WA2426881232.001232.002242Seattle WA2426881232.001232.002242Seattle WA2426881294.001294.001002Seattle WA2426881339.001339.00102Seattle WA2426881304.001304.00802Portland OR2224641182.001182.001002Riverside CA2224641192.001192.00802Riverside CA2224641176.001176.001122R.
DataIllinois Tool WorksConsolidated Statement of Income($ in milli.docx
DataIllinois Tool WorksConsolidated Statement of Income($ in millions)Year 3Year 2Year1Sales946892939984Cost of goods sold593659106192Gross Profit353233833792Selling and administrative expenses172016911814Depreciation and amoritization306385414Operating Profit150613071564Interest Expense686572Nonoperating Expense4713Unusual Losses33700Pre-tac income109712351479Income taxes384429521 Net Income713806958Illinois Tool WorksConsolidated Statement of Shareholders Equity($ in millions)Year 3Year 2Year1Balance at beginning of year982396049061Net income (loss)713806958Stock issued (repurchased)361-337-192Common Stock dividends-273-250-223 Balance at end of year10624982396040
Q1 & Q2Abnormal Earning ValuationForcasted ResultsCost of Capital9%Intitial Book Value 12/31 Year 3$ 10,624.00Dividend Payout25%Forecasted ROCE on beginning equity9.50%1. Year 42. Year 53. Year 64. Year 75. Year 86. Year 97. Year 108. Year 119. Year 1210. Year 13(a) Earnings Forecasts$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12(b) Equity Book Value-Forecasts (per share)Equity book value at beginning$ 10,624.00$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11Earnings Forecasts$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12Stock Issued$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0Other Comprehensive Income$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0Dividend$ (252.32)$ (270.30)$ (289.56)$ (310.19)$ (332.29)$ (355.96)$ (381.33)$ (408.50)$ (437.60)$ (468.78)Equity book value at year end$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11$ 21,144.45ROE (Return on Equity)9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%(c) Abnormal EarningsEquity book value at beginning$ 10,624.00$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11Equity cost pf capital9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%Normal Earnings$ 956.16$ 1,024.29$ 1,097.27$ 1,175.45$ 1,259.20$ 1,348.92$ 1,445.03$ 1,547.98$ 1,658.28$ 1,776.43Actual or Forecasted Earnings$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12Abnormal Earnings$ 53.12$ 56.90$ 60.96$ 65.30$ 69.96$ 74.94$ 80.28$ 86.00$ 92.13$ 98.69Discount Factor (at 9%)91.74%84.17%77.22%70.84%64.99%59.63%54.70%50.19%46.04%42.24%Abnormal Earnings Discounted at present$ 48.73$ 47.90$ 47.07$ 46.26$ 45.47$ 44.68$ 43.92$ 43.16$ 42.42$ 41.69(c) Estimated share valueSum of discounted abnormal earnings over horizon$ 451.29Current equity book value$ 10,624.00 Estimated current value of Equity at Year 3$ 11,075.29MillionQ1N.
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME
Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab t.
DataCity1997 Median Price1997 Change1998 Forecast1993-98 Annualize.docx
DataCity1997 Median Price1997 Change1998 Forecast1993-98 Annualized ChangeAlbany,N.Y.$109,3332.412.31.9Albuquerque$128,2004.99.26.9Allentown,pa.$98,15512.57.72.1Anchorage,Alaska$142,93034.41.99.7Ann Arbor,Mich.$152,32717.313.49.2Atlanta$111,34610.513.96.71997 Median PriceAustin,Tex.$116,6627.88.66.8Baltimore$119,25668.12.2Mean122343.62Baton Rouge,La.$88,0140.47.74.6Standard Error4358.7350178596Bergen and Passaic Counties,N.J.$204,3072.88.61.9Median112393.5Billings, Mont.$114,628-3.38.11.7ModeERROR:#N/ABirmingham,Ala.$115,8351.47.75Standard Deviation43587.3501785956Boise,Idaho$105,8824.70.95.1Sample Variance1899857095.59152Boston$184,3114.53.24Kurtosis5.070322278Buffalo$85,7363.65.93.1Skewness2.046653093Burlington,Vt.$102,7208.310.4-0.3Range240031Casper,Wyo.$92,9248.177.6Minimum70464Charleston,W.Va.$90,3510.113.55.7Maximum310495Charlotte,N.C.$120,3913.27.83.7Sum12234362Chicago$157,9365.110.34.6Count100Cincinnati$112,0277.19.26.6Cleveland$117,8365.65.56.4Columbia,S.C.$98,7906.39.64.8Columbus,Ohio$120,02811.211.87.9Dallas$110,7307.29.15.6Dayton$98,3773.98.66.2Denver$144,4438.49.68.8Des Moines$96,2224.29.76.2Detroit$120,9859.110.39El Paso, Tex.$73,699-3.1-3.1-0.1Fargo, N.D.$85,8123.38.74.6Flint, Mich.$102,35114.26.86.6Fort Lauderdale,Fla.$120,4037.10.23.2Fort Wayne, Ind.$95,93265.13.6Fresno, Cal.$157,3830.73.80.4Gary, Ind.$95,0720.14.43.8Grand Rapids$94,7898.79.16.2Harrisburg, Pa.$117,58313.18.34.4Hartford$140,3410.97.82.2Honolulu$310,495-70-2.8Houston$90,2386.810.34.3Indoanapolis$102,0894.354.4Jackson, Miss.$97,693-3.90.43.5Jacksonville. Fla.$89,3591.37.34.6Kansas City, Mo.$96,207-2.6-3.22.3Knoxville, Tenn.$101,3612.65.14.6Las Vegas$119,4220.85.73.2Little Rock, Ark.$87,1514.29.63.8Los Angeles$174,24326.9-0.9Louisville, Ky.$97,4336.67.37.1Manchester, N.H.$124,04213.49.33.5Memphis$104,7528.975.3Miami$115,0651.7-5.22Milwaukee$127,2407.17.95.8Minneapolis-St.Paul$117,4812.38.25.4Mobile,Ala.$84,0141.25.85.5Nashville$116,3863.48.26.9Nassau and Suffolk counties, N.Y.$167,7165.19.62.9New Have, Conn.$140,0485.613.92.3New Orleans$88,4061.75.24New York City$179,1571.73.81.5Newark, N.J.$191,937-0.13.81.6Norfolk, Va.$106,766-1.66.82.9Oakland, Cal.$232,8642.38.83.2Oklahoma City, Okla.$70,464-5.21.42.1Omaha, Neb.$84,213-4.21.53.6Orange County, Cal.$229,2557.49.73Orlando$93,3721.33.31.4Peoria,Ill.$77,8905411.26.6Philadelphia$135,24812.133.5Phoenix$112,7607.18.76.6Pittsburgh$88,5004.75.62.8Portland, Maine$132,3531.78.42.1Portland, Ore.$152,1977.78.99.5Providence, R.I.$122,3164.17.52.5Raleigh-Durham, N.C.$149,84410.647.6Richmond$115,3926.512.56.8Riverside, Cal.$115,134011.9-0.9Rochester,N.Y.$87,7792.27.12.2Sacramento,Cal.$115,7830.79-0.5St. Louis, Mo.$89,513-1.6-1.30.9Salt Lake City$122,212-0.32.78.3San Antonio$85,9811.47.63.9San Diego$184,5945.88.72.6San Francisco$289,02399.64.8San Jose, Cal.$247,2254.810.64.6Sarasota, Fla.$102,7380.57.94.1Scranton, Pa.$98,27914.774.2Seattle$173,1105.37.54.5Sioux Falls, S.D.$90,9404.110.86.2Springfield, Mass.$105,9570.99.20.7Syra.
DataClientRoom QualityFood QualityService Quality1GPG2GGG3GGG4GPG5.docx
DataClientRoom QualityFood QualityService Quality1GPG2GGG3GGG4GPG5GGG6PGG7GGG8GPG9PGP10GGG11GGG12PPP13GGG14GGG15GGP16PPP17GGG18GGG19PGP20PGP21GGG22PGP23PPP24GGG25GGG26GPP27GPG28GGG29PPP30PGG31GGG32PPP33PGG34PGP35GGG36PGP37GGG38PGP39GGG40GPG41GPG42GGG43GGP44PGP45PGG46PGG47GPP48GGG49GPP50PPP51GGG52PPG53PPP54GGG55GGG56GGG57GGP58GGG59GPP60PGP61GPP62GGG63GPG64GGG65PPP66GPG67GGG68GGG69GGG70GGP71GGG72GGG73GGG74GGP75GGP76PPP77GGG78GGG79GGP80GGG81GGG82GGG83PGG84GGG85GGG86GPP87GGG88PPP89GGG90PGP91GGG92GGG93GPG94GGG95GPP96PPP97PPP98GPG99PGG100PPP101GPP102PGP103PPG104GPG105GPG106GGG107PGG108PPP109GGG110GGG111GGG112GGG113GGG114GGG115GGG116GGG117GGG118PPP119PPG120GGG121GGG122PPP123GGG124GGG125GGG126GGG127GGG128GGG129PPP130GGG131GGP132PPP133GGG134GGG135GGG136GGG137GGG138GPG139PPP140GGG141PPP142GGG143GGG144PPP145GGG146GGG147GGG148GGG149GGG150GGP151GGG152GGG153GGG154GGG155GGG156GGG157PPP158GPG159GGG160GGG161GGG162PPG163GGP164GGG165PGP166GPG167GGP168PGG169GGG170GGP171GGG172PPP173GGG174GGG175GPG176GGG177GGG178PPG179GGG180GGP181GGG182GGG183GGG184GGG185GPP186GGG187GGG188GGG189GGG190GGG191GGG192GGG193GGG194GGG195GGP196GGG197GGG198GGG199GPP200GGG
Sheet1Room QualityFood QualityService QualityMeanMedianRangeSTDCoefficientVariationComparision
Corporal Punishment: Legal Reform as a Route to Changing
Norms
Jo Becker
Social Research: An International Quarterly, Volume 85, Number 1, Spring
2018, pp. 255-271 (Article)
Published by Johns Hopkins University Press
For additional information about this article
Access provided by Ebsco Publishing (8 May 2018 07:55 GMT)
https://muse.jhu.edu/article/692752
https://muse.jhu.edu/article/692752
social research Vol. 85 : No. 1 : Spring 2018 255
Jo Becker
Corporal Punishment:
Legal Reform as a Route
to Changing Norms
THE TERM “HARMFUL TRADITIONAL PRACTICES” TYPICALLY BRINGS TO MIND CHILD
marriage, female genital mutilation/cutting, and so-called “honor kill-
ings,” but rarely corporal punishment. Yet corporal punishment is
arguably the most pervasive harmful traditional practice children expe-
rience today. In nearly every part of the world, parents use physical
punishment to “discipline” their children. Such corporal punishment
typically takes the form of hitting a child with a bare hand or an object
such as a stick or paddle. A 2014 survey found that four of every five
children between the ages of two and 14—an estimated 1 billion glob-
ally—experience physical punishment in their home on a regular basis
(UNICEF 2014, 96).
The practice of corporal punishment is rooted in both cultural
norms and religious belief. Parents often believe that corporal pun-
ishment will teach children good behavior. They hit their children be-
cause it is socially accepted and because they themselves often were
hit growing up. Some religious teachings appear to justify the prac-
tice.1 The adage “spare the rod, spoil the child,” rooted in the Old Tes-
tament Book of Proverbs, suggests not only that corporal pun.
Recommended
Database reports provide us with the ability to further analyze ou.docx
Database reports provide us with the ability to further analyze our data, and provide it in a format that can be used to make business decisions. Discuss the steps that you would take to ensure that we create an effective report. What questions would you ask of the users?
Data presentation should be designed to display correct conclusions. What issues should we think about as we prepare data for presentation? Discuss the different methods that we can use to present data in a report. What role does the audience play in selecting how we present the data?
1 PAGE AND A HALF
.
DataInformationKnowledge1. Discuss the relationship between.docx
Data/Information/Knowledge
1. Discuss the relationship between data, information, and knowledge. Support your discussion with at least 3 academically reviewed articles.
2. Why do organization have information deficiency problem? Suggest ways on how to overcome information deficiency problem.
.
DataHole 12 Score6757555455555455575775655565656555655656556566643.docx
DataHole 12 Score67575554555554555757756555656565556556565565666434686664656566664555575656546555557554556655655465555565546555655467555646457664545665556555644554585456546654565546664566665566666675675665665656766555565486555567676645645575555575665455554655556556575555555455654555655666667665654655556657656558536666536755465655455755755666665545656565655555545545666564656443545655646445567547565654565545565676456544455446455755645655665567565554465466665
State Legislatures
(Part I)
POLS 2212
Legislatures, Policy-Making, and Political Science
• Legislative process is only one part of policy-making
• States are better venue for understanding policy-making
process overall
• Interactions between components are more transparent
• Less ‘political theater’ than national level
• More cases, more variation, more data
• What role do legislatures play in the overall policy-making
process??
• How do legislative-executive relations affect policy outcomes??
Agenda Setting
Formulation /
Negotiation
Adoption /
Enactment
Implementation
Evaluation
Revision /
Termination
• Public attention is focused on an issue
• Collective recognition of problem
Agenda Setting
• Potential solutions are offered
• Some public discourse over options
Formulation / Negotiation
•
Solution
is agreed upon and made into official policy /
law
Adoption / Enactment
• Policy is converted into actionable rules
Implementation
• Fairness, effectiveness, efficiency of policy and rules are
evaluated
Evaluation
• Improvements or changes to policy are made
Revision / Termination
Agenda Setting
• Parties
• Public opinion
• Advocacy groups /
entrepreneurs
Formulation /
Negotiation
• Party leadership
• Interest groups
• Legislature type
• Legislative-executive
relations
Adoption / Enactment
• Legislative-executive
relations
Implementation
• Type of executive
• Bureaucracy
Evaluation
• Social scientists
• Advocacy groups
• Legislative
committees
• State courts
Revision / Termination
• State courts
• Federal courts
‘Professional’
Model
‘Citizen-
Legislator’
Model
Work Load
Nearly full-
time
Part-time
Session
Year-round,
annual
Short-term,
possibly
biannual
Compensation
Medium-high
(over median
for state
employees)
Fairly low
Staff
Large, semi-
permanent
Small, likely
shared
Conceptualizing State Legislatures
Professional Hybrid / Mixture Citizen
State Legislatures
• GA Legislature
• $17k base +per
diem
• $22k – $24k total
Discussion Question
• What are some of the potential benefits /
drawbacks of each of these two models??
State Legislatures and Political Careers (Peverill Squire)
• ‘Career’ Legislatures (Congress)
• Sufficiently high pay
• Minimal incentive to ‘move up’
• Expectation of long tenure
• Heavy time commitment
• ‘Springboard’ Legislatures
• Other positions have higher pay, more prestige
• Expectation of limited tenure
• May be term lim.
DataDestination PalletsTotal CasesCases redCases whiteCases organi.docx
DataDestination PalletsTotal CasesCases redCases whiteCases organicWeek of OrderPallets (Bins)Oakland CA4853762648.002648.0080172Portland OR4853762438.002438.00500148Portland OR4853762438.002438.00500124Riverside CA4853762673.002673.0030118Seattle WA4853762638.002638.00100112Seattle WA4853762638.002638.0010016Oakland CA2831361518.001518.0010013Oakland CA2831361518.001518.0010011Riverside CA2831361512.001512.001121Oakland CA2426881288.001288.001121Oakland CA2426881324.001324.00401Oakland CA2426881288.001288.001121Oakland CA2426881324.001324.00401Oakland CA2426881294.001294.001001Riverside CA2426881288.001288.001121Riverside CA2426881288.001288.001121Riverside CA2426881329.001329.00301Riverside CA2426881288.001288.001121Riverside CA2426881288.001288.001121Riverside CA2426881329.001329.00301Seattle WA2426881294.001294.001001Oakland CA2224641192.001192.00801Riverside CA2224641176.001176.001121Riverside CA2224641176.001176.001121Riverside CA2224641202.001202.00601Seattle WA2224641120.001120.002241Seattle WA2224641176.001176.001121Portland OR121344422.00422.005001Portland OR121344616.00616.001121Portland OR111232560.00560.001121Portland OR111232560.00560.001121Portland OR111232491.00491.002501Oakland CA3336112.00112.001121Oakland CA3336112.00112.001121Oakland CA3336118.00118.001001Portland OR333693.0093.001501Portland OR3336168.00168.0001Portland OR3336112.00112.001121Portland OR3336118.00118.001001Riverside CA3336112.00112.001121Riverside CA3336112.00112.001121Seattle WA3336163.00163.00101Seattle WA333656.0056.002241Seattle WA3336112.00112.001121Seattle WA333656.0056.002241Oakland CA11120.000.001121Portland OR1112-69.00-69.002501Seattle WA111253.0053.0061Seattle WA11126.006.001001Seattle WA111251.0051.00101Seattle WA11120.000.001121Oakland CA4853762588.002588.002002Oakland CA4853762638.002638.001002Oakland CA4853762632.002632.001122Oakland CA4853762638.002638.001002Oakland CA4853762632.002632.001122Portland OR4853762338.002338.007002Seattle WA4853762632.002632.001122Seattle WA4853762576.002576.002242Oakland CA2831361512.001512.001122Riverside CA2831361512.001512.001122Riverside CA2831361518.001518.001002Oakland CA2426881304.001304.00802Oakland CA2426881294.001294.001002Oakland CA2426881288.001288.001122Oakland CA2426881324.001324.00402Oakland CA2426881304.001304.00802Oakland CA2426881288.001288.001122Portland OR2426881294.001294.001002Riverside CA2426881329.001329.00302Riverside CA2426881288.001288.001122Riverside CA2426881329.001329.00302Riverside CA2426881329.001329.00302Riverside CA2426881288.001288.001122Riverside CA2426881288.001288.001122Seattle WA2426881288.001288.001122Seattle WA2426881232.001232.002242Seattle WA2426881294.001294.001002Seattle WA2426881288.001288.001122Seattle WA2426881232.001232.002242Seattle WA2426881232.001232.002242Seattle WA2426881294.001294.001002Seattle WA2426881339.001339.00102Seattle WA2426881304.001304.00802Portland OR2224641182.001182.001002Riverside CA2224641192.001192.00802Riverside CA2224641176.001176.001122R.
DataIllinois Tool WorksConsolidated Statement of Income($ in milli.docx
DataIllinois Tool WorksConsolidated Statement of Income($ in millions)Year 3Year 2Year1Sales946892939984Cost of goods sold593659106192Gross Profit353233833792Selling and administrative expenses172016911814Depreciation and amoritization306385414Operating Profit150613071564Interest Expense686572Nonoperating Expense4713Unusual Losses33700Pre-tac income109712351479Income taxes384429521 Net Income713806958Illinois Tool WorksConsolidated Statement of Shareholders Equity($ in millions)Year 3Year 2Year1Balance at beginning of year982396049061Net income (loss)713806958Stock issued (repurchased)361-337-192Common Stock dividends-273-250-223 Balance at end of year10624982396040
Q1 & Q2Abnormal Earning ValuationForcasted ResultsCost of Capital9%Intitial Book Value 12/31 Year 3$ 10,624.00Dividend Payout25%Forecasted ROCE on beginning equity9.50%1. Year 42. Year 53. Year 64. Year 75. Year 86. Year 97. Year 108. Year 119. Year 1210. Year 13(a) Earnings Forecasts$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12(b) Equity Book Value-Forecasts (per share)Equity book value at beginning$ 10,624.00$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11Earnings Forecasts$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12Stock Issued$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0Other Comprehensive Income$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0$ - 0Dividend$ (252.32)$ (270.30)$ (289.56)$ (310.19)$ (332.29)$ (355.96)$ (381.33)$ (408.50)$ (437.60)$ (468.78)Equity book value at year end$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11$ 21,144.45ROE (Return on Equity)9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%9.50%(c) Abnormal EarningsEquity book value at beginning$ 10,624.00$ 11,380.96$ 12,191.85$ 13,060.52$ 13,991.09$ 14,987.95$ 16,055.84$ 17,199.82$ 18,425.31$ 19,738.11Equity cost pf capital9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%9.00%Normal Earnings$ 956.16$ 1,024.29$ 1,097.27$ 1,175.45$ 1,259.20$ 1,348.92$ 1,445.03$ 1,547.98$ 1,658.28$ 1,776.43Actual or Forecasted Earnings$ 1,009.28$ 1,081.19$ 1,158.23$ 1,240.75$ 1,329.15$ 1,423.86$ 1,525.30$ 1,633.98$ 1,750.40$ 1,875.12Abnormal Earnings$ 53.12$ 56.90$ 60.96$ 65.30$ 69.96$ 74.94$ 80.28$ 86.00$ 92.13$ 98.69Discount Factor (at 9%)91.74%84.17%77.22%70.84%64.99%59.63%54.70%50.19%46.04%42.24%Abnormal Earnings Discounted at present$ 48.73$ 47.90$ 47.07$ 46.26$ 45.47$ 44.68$ 43.92$ 43.16$ 42.42$ 41.69(c) Estimated share valueSum of discounted abnormal earnings over horizon$ 451.29Current equity book value$ 10,624.00 Estimated current value of Equity at Year 3$ 11,075.29MillionQ1N.
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME
Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab t.
DataCity1997 Median Price1997 Change1998 Forecast1993-98 Annualize.docx
DataCity1997 Median Price1997 Change1998 Forecast1993-98 Annualized ChangeAlbany,N.Y.$109,3332.412.31.9Albuquerque$128,2004.99.26.9Allentown,pa.$98,15512.57.72.1Anchorage,Alaska$142,93034.41.99.7Ann Arbor,Mich.$152,32717.313.49.2Atlanta$111,34610.513.96.71997 Median PriceAustin,Tex.$116,6627.88.66.8Baltimore$119,25668.12.2Mean122343.62Baton Rouge,La.$88,0140.47.74.6Standard Error4358.7350178596Bergen and Passaic Counties,N.J.$204,3072.88.61.9Median112393.5Billings, Mont.$114,628-3.38.11.7ModeERROR:#N/ABirmingham,Ala.$115,8351.47.75Standard Deviation43587.3501785956Boise,Idaho$105,8824.70.95.1Sample Variance1899857095.59152Boston$184,3114.53.24Kurtosis5.070322278Buffalo$85,7363.65.93.1Skewness2.046653093Burlington,Vt.$102,7208.310.4-0.3Range240031Casper,Wyo.$92,9248.177.6Minimum70464Charleston,W.Va.$90,3510.113.55.7Maximum310495Charlotte,N.C.$120,3913.27.83.7Sum12234362Chicago$157,9365.110.34.6Count100Cincinnati$112,0277.19.26.6Cleveland$117,8365.65.56.4Columbia,S.C.$98,7906.39.64.8Columbus,Ohio$120,02811.211.87.9Dallas$110,7307.29.15.6Dayton$98,3773.98.66.2Denver$144,4438.49.68.8Des Moines$96,2224.29.76.2Detroit$120,9859.110.39El Paso, Tex.$73,699-3.1-3.1-0.1Fargo, N.D.$85,8123.38.74.6Flint, Mich.$102,35114.26.86.6Fort Lauderdale,Fla.$120,4037.10.23.2Fort Wayne, Ind.$95,93265.13.6Fresno, Cal.$157,3830.73.80.4Gary, Ind.$95,0720.14.43.8Grand Rapids$94,7898.79.16.2Harrisburg, Pa.$117,58313.18.34.4Hartford$140,3410.97.82.2Honolulu$310,495-70-2.8Houston$90,2386.810.34.3Indoanapolis$102,0894.354.4Jackson, Miss.$97,693-3.90.43.5Jacksonville. Fla.$89,3591.37.34.6Kansas City, Mo.$96,207-2.6-3.22.3Knoxville, Tenn.$101,3612.65.14.6Las Vegas$119,4220.85.73.2Little Rock, Ark.$87,1514.29.63.8Los Angeles$174,24326.9-0.9Louisville, Ky.$97,4336.67.37.1Manchester, N.H.$124,04213.49.33.5Memphis$104,7528.975.3Miami$115,0651.7-5.22Milwaukee$127,2407.17.95.8Minneapolis-St.Paul$117,4812.38.25.4Mobile,Ala.$84,0141.25.85.5Nashville$116,3863.48.26.9Nassau and Suffolk counties, N.Y.$167,7165.19.62.9New Have, Conn.$140,0485.613.92.3New Orleans$88,4061.75.24New York City$179,1571.73.81.5Newark, N.J.$191,937-0.13.81.6Norfolk, Va.$106,766-1.66.82.9Oakland, Cal.$232,8642.38.83.2Oklahoma City, Okla.$70,464-5.21.42.1Omaha, Neb.$84,213-4.21.53.6Orange County, Cal.$229,2557.49.73Orlando$93,3721.33.31.4Peoria,Ill.$77,8905411.26.6Philadelphia$135,24812.133.5Phoenix$112,7607.18.76.6Pittsburgh$88,5004.75.62.8Portland, Maine$132,3531.78.42.1Portland, Ore.$152,1977.78.99.5Providence, R.I.$122,3164.17.52.5Raleigh-Durham, N.C.$149,84410.647.6Richmond$115,3926.512.56.8Riverside, Cal.$115,134011.9-0.9Rochester,N.Y.$87,7792.27.12.2Sacramento,Cal.$115,7830.79-0.5St. Louis, Mo.$89,513-1.6-1.30.9Salt Lake City$122,212-0.32.78.3San Antonio$85,9811.47.63.9San Diego$184,5945.88.72.6San Francisco$289,02399.64.8San Jose, Cal.$247,2254.810.64.6Sarasota, Fla.$102,7380.57.94.1Scranton, Pa.$98,27914.774.2Seattle$173,1105.37.54.5Sioux Falls, S.D.$90,9404.110.86.2Springfield, Mass.$105,9570.99.20.7Syra.
DataClientRoom QualityFood QualityService Quality1GPG2GGG3GGG4GPG5.docx
DataClientRoom QualityFood QualityService Quality1GPG2GGG3GGG4GPG5GGG6PGG7GGG8GPG9PGP10GGG11GGG12PPP13GGG14GGG15GGP16PPP17GGG18GGG19PGP20PGP21GGG22PGP23PPP24GGG25GGG26GPP27GPG28GGG29PPP30PGG31GGG32PPP33PGG34PGP35GGG36PGP37GGG38PGP39GGG40GPG41GPG42GGG43GGP44PGP45PGG46PGG47GPP48GGG49GPP50PPP51GGG52PPG53PPP54GGG55GGG56GGG57GGP58GGG59GPP60PGP61GPP62GGG63GPG64GGG65PPP66GPG67GGG68GGG69GGG70GGP71GGG72GGG73GGG74GGP75GGP76PPP77GGG78GGG79GGP80GGG81GGG82GGG83PGG84GGG85GGG86GPP87GGG88PPP89GGG90PGP91GGG92GGG93GPG94GGG95GPP96PPP97PPP98GPG99PGG100PPP101GPP102PGP103PPG104GPG105GPG106GGG107PGG108PPP109GGG110GGG111GGG112GGG113GGG114GGG115GGG116GGG117GGG118PPP119PPG120GGG121GGG122PPP123GGG124GGG125GGG126GGG127GGG128GGG129PPP130GGG131GGP132PPP133GGG134GGG135GGG136GGG137GGG138GPG139PPP140GGG141PPP142GGG143GGG144PPP145GGG146GGG147GGG148GGG149GGG150GGP151GGG152GGG153GGG154GGG155GGG156GGG157PPP158GPG159GGG160GGG161GGG162PPG163GGP164GGG165PGP166GPG167GGP168PGG169GGG170GGP171GGG172PPP173GGG174GGG175GPG176GGG177GGG178PPG179GGG180GGP181GGG182GGG183GGG184GGG185GPP186GGG187GGG188GGG189GGG190GGG191GGG192GGG193GGG194GGG195GGP196GGG197GGG198GGG199GPP200GGG
Sheet1Room QualityFood QualityService QualityMeanMedianRangeSTDCoefficientVariationComparision
Corporal Punishment: Legal Reform as a Route to Changing
Norms
Jo Becker
Social Research: An International Quarterly, Volume 85, Number 1, Spring
2018, pp. 255-271 (Article)
Published by Johns Hopkins University Press
For additional information about this article
Access provided by Ebsco Publishing (8 May 2018 07:55 GMT)
https://muse.jhu.edu/article/692752
https://muse.jhu.edu/article/692752
social research Vol. 85 : No. 1 : Spring 2018 255
Jo Becker
Corporal Punishment:
Legal Reform as a Route
to Changing Norms
THE TERM “HARMFUL TRADITIONAL PRACTICES” TYPICALLY BRINGS TO MIND CHILD
marriage, female genital mutilation/cutting, and so-called “honor kill-
ings,” but rarely corporal punishment. Yet corporal punishment is
arguably the most pervasive harmful traditional practice children expe-
rience today. In nearly every part of the world, parents use physical
punishment to “discipline” their children. Such corporal punishment
typically takes the form of hitting a child with a bare hand or an object
such as a stick or paddle. A 2014 survey found that four of every five
children between the ages of two and 14—an estimated 1 billion glob-
ally—experience physical punishment in their home on a regular basis
(UNICEF 2014, 96).
The practice of corporal punishment is rooted in both cultural
norms and religious belief. Parents often believe that corporal pun-
ishment will teach children good behavior. They hit their children be-
cause it is socially accepted and because they themselves often were
hit growing up. Some religious teachings appear to justify the prac-
tice.1 The adage “spare the rod, spoil the child,” rooted in the Old Tes-
tament Book of Proverbs, suggests not only that corporal pun.
Database Project CharterBusiness CaseKhalia HartUnive.docx
Database Project Charter/Business Case
Khalia Hart
University of Maryland Global Campus
February 21, 2020
Introduction
A database is an electronic collection of data that is built by a user so that they can access, update particular information in the database coherently or rapidly. Today firms employ integrated technology to increase their capacity to serve more clients, keep information well or effectively, organize activities according to the urgency or priorities, accounting records (Tüttelmann F, 2015). Most of the integrated technology depends on multiple databases that supply information relevant in making the decision. Since the business started using databases, their performance increase because the business decisions they make are sound and practical.
Business Problem
The supply chain management is one of the most complicated processes in the business and often at times due to need of detail it gets hard for the supply chain manager to keep the record of the work covered effectively, have enough data to make the decision and also have enough data to monitor the chain of operation (William, 2019). The supply chain has been so crucial for the business because it determines the performance of the company in the industry by assessing the quality of the product produced in the organization, cost of production, the time and effectiveness of distribution network, and overall production operation of the organization.
Operation management has been named as the leading cause of business failure caused by a lack of a system, which the manager or the supervisor can use to monitor the whole system. This is the problem to solve using the database (William, 2019). Using a database, the manager can observe or watch the entire chain from their office, make better decisions by fore- planning approach of the database also make changes within the system when there is the need to cut costs or making the process effective.
Project Scope
Most business organizations are spread in operation, and this is the challenge that makes the supply chain management complex (Tüttelmann F, 2015). This is because the chain is in different localities, and therefore, coordination of operation among the user or the workers becomes a challenge. Through the database system, the business will enjoy proper coordination using the wide Area Network (LAN). Through the LAN network, the company can link computers and cost-effectively share data and communication. Through this system, the company will have a connection and coordination of the processes within the organization. The number of connected devices will range from 10 to 1000, depending on the type of tools and system that is set to facilitate this connection.
Goals and objectives of the system
The purpose of the system that I want to install in the supply chain management is to;
· Monitoring of the supply chain- the system will enable the manager to monitor the system and every process in the order (Gattor.
Databases selected Multiple databases...Full Text (1223 .docx
Databases selected: Multiple databases...
Full Text (1223 words)
Kraft Reformulates Oreo, Scores in China
Julie Jargon. Wall Street Journal. (Eastern edition). New York, N.Y.: May 1, 2008. pg. B.1
Abstract (Summary)
Kraft, the world's second largest food company by revenue, reported a 13% drop in first-quarter net income Wednesday
because of high commodity costs and increased spending on product research and marketing. Television commercials
showed kids twisting apart Oreo cookies, licking the cream center and dipping the chocolate cookie halves into glasses of
milk.
(c) 2008 Dow Jones & Company, Inc. Reproduced with permission of copyright owner. Further reproduction or distribution
is prohibited without permission.
Unlike its iconic American counterpart, the Oreo sold in China is frequently long, thin, four-layered and coated in chocolate. But
both kinds of cookies have one important thing in common: They are now best sellers.
The Oreo has long been the top-selling cookie in the U.S. market. But Kraft Foods Inc. had to reinvent the Oreo to make it sell
well in the world's most populous nation. While Chinese Oreo sales represent a tiny fraction of Kraft's $37.2 billion in annual
revenue, the cookie's journey in China exemplifies the kind of entrepreneurial transformation that Chief Executive Irene
Rosenfeld is trying to spread throughout the food giant.
Kraft, the world's second largest food company by revenue, reported a 13% drop in first-quarter net income Wednesday
because of high commodity costs and increased spending on product research and marketing. Its international business,
which now represents 40% of Kraft's revenue thanks to the company's recent acquisition of Groupe Danone's biscuits
business, was a bright spot in the quarter, aided by the weak dollar. Kraft's profit in the European Union rose 48%, excluding
special charges, and its profit in developing markets rose 57%.
To try to increase growth at the company, Ms. Rosenfeld has been putting more power in the hands of Kraft's various
business units around the globe, telling employees that decisions about Kraft products shouldn't all be made by people at the
Northfield, Ill., headquarters.
To take advantage of the European preference for dark chocolate, Kraft is introducing dark chocolate in Germany under its
Milka brand. Research in Russia showed that consumers there like premium instant coffee, so Kraft is positioning its Carte
Noire freeze-dried coffee as upscale by placing it at film festivals, fashion shows and operas. And in the Philippines, where
iced tea is popular, Kraft last year launched iced-tea-flavored Tang. Ms. Rosenfeld has also been encouraging marketers to
"reframe" product categories, no longer thinking, for example, that an Oreo has to be a round sandwich cookie.
Oreos were first introduced in 1912 in the U.S., but it wasn't until 1996 that Kraft introduced Oreos to Chinese consumers.
Nine years later, a makeover began. Shawn Warren, a 37-year-old .
Database SystemsDesign, Implementation, and ManagementCo.docx
Database Systems
Design, Implementation, and Management
Coronel | Morris
11e
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Chapter 3
The Relational Database Model
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Learning ObjectivesIn this chapter, one will learn:That the relational database model offers a logical view of dataAbout the relational model’s basic component: relationsThat relations are logical constructs composed of rows (tuples) and columns (attributes)That relations are implemented as tables in a relational DBMS
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Learning ObjectivesIn this chapter, one will learn:About relational database operators, the data dictionary, and the system catalogHow data redundancy is handled in the relational database modelWhy indexing is important
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
A Logical View of DataRelational database model enables logical representation of the data and its relationshipsLogical simplicity yields simple and effective database design methodologies Facilitated by the creation of data relationships based on a logical construct called a relation
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Table 3.1 - Characteristics of a Relational Table
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
KeysConsist of one or more attributes that determine other attributesUsed to: Ensure that each row in a table is uniquely identifiableEstablish relationships among tables and to ensure the integrity of the dataPrimary key (PK): Attribute or combination of attributes that uniquely identifies any given row
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
DeterminationState in which knowing the value of one attribute makes it possible to determine the value of anotherIs the basis for establishing the role of a key Based on the relationships among the attributes
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
DependenciesFunctional dependence: Value of one or more attributes determines the value of one or more other attributesDeterminant: Attribute whose value determines another Dependent: Attribute whose value is determined by the.
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN1DATABASE SYS.docx
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN 1
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN 19
Table of Contents
1. Database System Overview 3
1.1 Business Environment 3
1.2 Database system goals and objective 4
2. Entity Relationship Model 7
2.1 Proposed entities 7
2.2 Business rules 8
2.3 Entity–Relationship Model 9
2.3.1 Relationship Types 9
2.3.2 Normalization form 12
2.3.3 Benefit of using database design 14
3. Structured Query Language (SQL) Scripts 15
3.1 Data definition language (DDL) 15
3.2 Data manipulation language (DML) 16
3.3 SQL report 17
3.4 Benefit of using database queries 19
4. Database Administration Plan 20
5. Future Database System Implementation Plan 21
6. References 22
1.
Database System Overview
1.1 Business Environment
Office Depot, Inc is an American retail store company founded in 1986 and headquartered in Florida, United States. The company provides office and school supplies with 1400 retail stores and e-commerce sites. The supply includes everything to their customer like latest technology, core school and office supplies, printing and documenting service, furniture and other services like cell phone repair, tech and marketing service etc.
Recently there were too many complaints from existing and new customer that the online site is super glitch and lagging. Another customer posted that the delivery did not come on the scheduled day. And they cannot track down the order because the website does not have tracking information. Also when the website is down, customer service cannot help to see the order details either and therefore, they feel it’s frustrating to order online and therefore want to cancel the order. One other customer posted in the website grievance section that the “label maker” showed available in the stock even though it was out of stock when verified with the customer service representative. With every product not in stock, we lose opportunity of sale which costs the store. This not only affect customer but also affect company. We are so dependent on the data, most of the time staff has to correct accounting report, sales estimates and invoice customer manually which is very time-consuming in an excel sheet.
In order to solve above issues and avoid sales loss, Office Depot must have a database to store and maintain correct count of the products. This database will help inventory management i.e. tracking products, update inventory, find popular or less popular item, loss prevention, track inventory status and perform data mining. The staff can access this database via a computerized database. (Gerald H., Importance of inventory database retail)1.2 Database system goals and objective
The mission of the company is to become number one retail company by creating inclusive environment and great shopping experience where both customer and employees are respected and valued. To achieve the retail store mission, we are committed to provide secure and robust data base system for ou.
Database Security Assessment Transcript You are a contracting office.docx
Database Security Assessment Transcript You are a contracting officer's technical representative, a Security System Engineer, at a military hospital. Your department's leaders are adopting a new medical health care database management system. And they've tasked you to create a request for proposal for which different vendors will compete to build and provide to the hospital. A Request For Proposal, or RFP, is when an organization sends out a request for estimates on performing a function, delivering a technology, or providing a service or augmenting staff. RFPs are tailored to each endeavor but have common components and are important in the world of IT contracting and for procurement and acquisitions. To complete the RFP, you must determine the technical and security specifications for the system. You'll write the requirements for the overall system and also provide evaluation standards that will be used in rating the vendor's performance. Your learning will help you determine your system's requirements. As you discover methods of attack, you'll write prevention and remediation requirements for the vendor to perform. You must identify the different vulnerabilities the database should be hardened against.
Modern healthcare systems incorporate databases for effective and efficient management of patient healthcare. Databases are vulnerable to cyberattacks and must be designed and built with security controls from the beginning of the life cycle. Although hardening the database early in the life cycle is better, security is often incorporated after deployment, forcing hospital and healthcare IT professionals to play catch-up. Database security requirements should be defined at the requirements stage of acquisition and procurement.
System security engineers and other acquisition personnel can effectively assist vendors in building better healthcare database systems by specifying security requirements up front within the request for proposal (RFP). In this project, you will be developing an RFP for a new medical healthcare database management system.
Parts of your deliverables will be developed through your learning lab. You will submit the following deliverables for this project:
Deliverables
• An RFP, about 10 to 12 pages, in the form of a double-spaced Word document with citations in APA format. The page count does not include figures, diagrams, tables, or citations. There is no penalty for using additional pages. Include a minimum of six references. Include a reference list with the report.
• An MS-Excel spreadsheet with lab results.
There are 11 steps in this project. You will begin with the workplace scenario and continue with Step 1: "Provide an Overview for Vendors."
Step 1: Provide an Overview for Vendors
As the contracting officer's technical representative (COTR), you are the liaison between your hospital and potential vendors. It is your duty to provide vendors with an overview of your organization. To do so, identify infor.
Data.docx
Data:
Hours
Period
Cycle Time
1
45
2
33
3
44
4
30
5
51
6
47
7
39
8
34
9
69
10
43
11
29
12
38
13
45
14
49
15
73
16
38
17
50
18
40
19
57
20
36
21
31
22
46
23
40
24
51
25
48
26
37
27
42
28
50
1. Calculate R-Bar?
2. Upper Control Limit for the Range?
3 Upper control limit for the individuals?
4 Lower control limit for the individuals?
5. What is the control chart telling you?
6. Is there adequate discrimination?
.
Database Design Mid Term ExamSpring 2020Name ________________.docx
Database Design Mid Term Exam
Spring 2020
Name: ____________________________
1. What is a data model?
A. method of storing files on a disk drive
B. simple representation of complex real-world data structures
C. name of system for designing software
D. method of designing invoices for customers
2. A Relationship Database system consists of 3 parts: a client front end for sending information to a command processor, a middle tier that interprets user commands, and a management frame work for storing, organizing and securing data.
a. True
b. False
3. What are the 3 components of a table:
A. Row, column, value
B. Row, top, bottom
C. Column, row, top
D. Top, middle, end
4. What does the column represent in a table?
a. Attribute of the table records
b. A complete record in the table
c. The system log from the database
d. A list of database tables
5. What does a row in the table represent?
a. A complete data record
b. List of system logs
c. A list of file systems on database server
d. The primary keys from all the tables.
6. Which of the following is an example of data definition language (DDL)?
a. UPDATE
b. V$SYSLOG
c. CREATE
d. DETAIN
7 . Which of the following is an example of data manipulation language (DML)?
A. SELECT
B. ABORT
C. GRANT
D. REVOKE
8. A _______ key is an attribute that uniquely identifies a record in a table.
9. A _______ key is an attribute that is a primary key in one table and is used as a reference in a second table to establish a relationship between the two tables.
10. When running a ‘SELECT’ join, what is returned from the table:
A. ROW
B. Column
C. single attribute
D. all tables in the database
11. When running a ‘PROJECT’ join, what is returned from the table:
A. COLUMN
B. ROW
C. Single Attribute
D. a list of tables in the database
12. What are the 3 types of relationships commonly shown on an entity relationship diagram?
A. 1 to 1
B. 1 to Many
C. Many to Many
D. All the above
E. None of the above
13. What is an entity relationship diagram (ERD)?
A. graphical representation of all entities in a database and how the entities are related
b. list of the log files in the database.
C. list of all the tablespace names in a database
D. A diagram that shows how data is written to a physical disk drive.
14. The definition of an attribute in a table that has no value is:
A. ZERO
b. NULL
c. ZILTCH
D. NONE
15. A ____________ attribute can either be stored on retrieve on an ad hoc basis.
16. Briefly describe the advantages and disadvantages of storing a derived attribute?
17. A database can process many types of data classifications. Which of the following is not a data classification or architecture that databases can process:
A. Structured
B. Semi-structured
C. undelimited
D. Unstructured
18. The process by which functional/partial dependency and transitive dependency is removed from a database table is called:
a. sharding
b. normalization
c. defragmentation
d. reallocation
.
Database Justification MemoCreate a 1-page memo for the .docx
Database Justification Memo
C
reate
a
1-page
memo for the project stakeholder
explaining
why they should migrate towards a database driven application system
rather
than a static website.
Discuss
the benefits and drawbacks of the proposed changes
AND
Web Services Memo
Create
a 1-page memo to the project stakeholder on the importance of web services including security considerations, scalability, and compatibility.
.
Database Dump Script(Details of project in file)Mac1) O.docx
Database Dump Script
(Details of project in file)
Mac:
1) Open up the terminal, or if already in MySQL, get out by typing "exit" and pressing enter.
2) Type:
/usr/local/mysql/bin/mysqldump -u root -p [database name] > /tmp/filename.txt
...where [database name] is the name of the database you want to export. When prompted, type the password. Check the /tmp file for your output.
.
Database Design 1. What is a data model A. method of sto.docx
Database Design
1. What is a data model?
A. method of storing files on a disk drive
B. simple representation of complex real-world data structures
C. name of system for designing software
D. method of designing invoices for customers
2. Which of the following are the most important elements of a security program for databases:
a. Integrity, referential index, user rights
b. Confidentiality. Integrity and Availability
c. Availability, multi-master replication, high-bandwidth
d. DBA, System Admin, and PMO
3. Suppose that you have a table with a number of product sales. The product code may repeat in the table as it is likely the same product could be sold multiple times. If you want to produce a list of the unique products that are sold, you could use which of the following keywords in the SELECT statement:
A. LIKE
B. ORDERED BY
C. DISTINCT
D. DIFFERENT
4. What does the column represent in a table?
a. Attribute of the table records
b. A complete record in the table
c. The system log from the database
d. A list of database tables
5. What does a row in the table represent?
a. A complete data record
b. List of system logs
c. A list of file systems on database server
d. The primary keys from all the tables.
6. Which of the following is an example of data definition language (DDL)?
a. UPDATE
b. V$SYSLOG
c. CREATE
d. DETAIN
7 . Which of the following is an example of data manipulation language (DML)?
A. SELECT
B. ABORT
C. GRANT
D. REVOKE
8. A _____________ key is an attribute that uniquely identifies a record in a table.
9. A _____________ key is an attribute that is a primary key in one table and is used as a reference in a second table to establish a relationship between the two tables.
10. When running a ‘SELECT’ join, what is returned from the table:
A. ROW
B. Column
C. single attribute
D. all tables in the database
11. When running a ‘PROJECT’ join, what is returned from the table:
A. COLUMN
B. ROW
C. Single Attribute
D. a list of tables in the database
12. What are the 3 types of relationships commonly shown on an entity relationship diagram?
A. 1 to 1
B. 1 to Many
C. Many to Many
D. All the above
E. None of the above
13. What is an entity relationship diagram (ERD)?
A. graphical representation of all entities in a database and how the entities are related
b. list of the log files in the database.
C. list of all the tablespace names in a database
D. A diagram that shows how data is written to a physical disk drive.
14. The definition of an attribute in a table that has no value is:
A. ZERO
b. NULL
c. ZILTCH
D. NONE
15. A __________ attribute can either be stored on retrieve on an ad hoc basis.
16. Which of the following is not considered a characteristic of distributed management systems:
a. Concurrency Control
b. Business intelligence
c. Transaction management
d. query optimization
17. A database can process many types of data classifications. Which of the following is not a data class.
DataAGEGENDERETHNICMAJORSEMHOUSEGPAHRSNEWSPAPTVHRSSLEEPWEIGHTHEIGH.docx
DataAGEGENDERETHNICMAJORSEMHOUSEGPAHRSNEWSPAPTVHRSSLEEPWEIGHTHEIGHTCARJOBUSBORNSTATEGREEKPMSMEDFEDSIBLINGSCALLDADRELMOMRELSIBRELFAMRELCLUBSVOLHOURSCLUBHRSDEGREEEFFORTSTUDYDIFFWRKOUTWOMOTPHYFITIMAGEWOPUBPHYFITIMPCALNUTRITIONHEALTHDECFRUITVEGSMOKEDRINKBLACKOUT201CaucHealth Management & Policy3On3.6152071105'3No0YesIN114413444432021B4121E342441000555131191CaucSPHS-S3Off3.53161281105'3Yes0YesPA2255274444000A464C144441000455121202CaucLaw and Public Policy3On3.551831161355'8No0YesIN21332123232712C371B432322500443112221CaucManagement7On3.6163481585'9Yes3.5YesIN1145364444329B452D241441670455121201LatinaArts Management3Off3.2172108.51105'1Yes0YesNJ22331104444000A4103C143341200441131232CaucSafty9Off3.3182981605'10Yes21YesIN2133184434009B363D333331000342122212CaucSpeech and Hearing5Off3.8418245.51505'9Yes0YesIN2344120344323020B4123D251442000555121201CaucSpeech and Hearing3Off3.861521.57.51505'4Yes0YesIN254523.53444109B483B232321500333122202African AmericanBusiness3Off312247.51805'11Yes0YesIN23423544441010B342E253443000322122212CaucMicrobiology5Off3.31711071255'9Yes0YesOH2155254333000C282E341231700433121201South AfricanManagement5Off2.95152381005'2Yes0NoNJ2135224234100A453A234121200343241211CaucNonprofit5Off3152471505'9Yes15YesIN22342714442100B4153D131241500344121201African AmericanHealth Admin5Off31511581505'4Yes25YesIN224534344412510B361B513321000121122211African AmericanComm Health7Off2.7141157.51805'2Yes0YesIN22145504434000A352A11244700232121191CaucBiology3On3.88142171215'3No0YesIN2355183444210.5C4403C343242000454112221Sri LankanExercise Science6Off3.4117227.51305'5No0NoSri Lanka21440144041020C4152D242441500454112201CaucEnvironmental Management3Off3.5212081405'3No10.5YesIN2545813424422B3173D242332500444112202CaucManagement3Off2.31512091606'0Yes0YesIN2124034404006A3124B343242500342121201YellowManagement4Off3.2152071155'2No0NoSichuan2125054414002A3153B33323300233112211CaucArts Management5Off2.91620.56.51446'3Yes20YesIN2243154444503A37.52A231332000444122201CaucManagement3Off3.5152381405'5No0YesIN21434503444101A4153C231331200443141211CaucExercise Science5Off3.621522181805'7Yes15 YesIN2243244433321B4353C222231400343122211CaucExercise Science5Off3.98142571705'10Yes0YesIN2133143424201C452B332441700342122202CaucExercise Sciene5Off3.61723082105'10Yes0YesIN21222103344000B4202C233322000445122201CaucArt History3Off3.5151261605'4Yes0YesIN2134234444201A372B132431300334121201CaucHuman Development & Family Studies3On3.6152981505'6Yes8YesIN21442104344013B473C5322318000333121212CaucManagement7Off3.51182781756'0Yes0NoSC1245254444201B453C542442000432122182CaucBSPA Management1On3.67154371405'10No0YesIN2355153434212B4251A232332200453122212CaucExercise Science5On3.5616294.52126'3Yes0YesIN1245274444213.511C4152D353443900443131201African AmericanNonprofit Management3Off3.215367.51345'4Yes10YesIN2322673444388B4103E24244300553221202CaucArts Administration3Off3.6172071405'10Yes0YesIN1154113333264A383B222221500433121211CaucHealth Admin5Off31511561455'5Yes5YesTX2344144444200B452D.
Database AdministrationObjectivesDiscus.docx
Database Administration
*
ObjectivesDiscuss the need for database administrationExplain the DBA’s responsibilities in formulating and enforcing database policies for access privileges, security, disaster planning, and archivingDiscuss the DBA’s administrative responsibilities for DBMS evaluation and selection, DBMS maintenance, data dictionary management, and trainingDiscuss the DBA’s technical responsibilities for database design, testing, and performance tuning
*
*
Introduction
FIGURE 8-1: DBA responsibilities
*
*
Database Policy Formulation and EnforcementDBAFormulates database policiesCommunicates policies to usersEnforces policiesPoliciesAccess privilegesSecurityDisaster planningArchiving
*
*
Access PrivilegesDBADetermines access privileges for all usersEnters appropriate authorization rules in DBMSSQL GRANT statementAccess privilege policyDocumented by DBAApproved by top-level managementCommunicated by DBA to all users
*
*
Access Privileges (continued)
FIGURE 8-2: Permitted and denied access privileges for Sam
*
*
Access Privileges (continued)
FIGURE 8-4: Permitted and denied access privileges for Valerie
*
*
SecurityPrevention of unauthorized access, intentional or accidental, to databaseDBACreates security policies and proceduresObtains management approval of policies and proceduresDistributes policies and procedures to authorized users
*
*
Security (continued)DBMS’s security featuresEncryptionAuthenticationAuthorizationsViewsAdditional security programs may be created or purchasedMonitoring of database usage to detect security violations
*
*
Security (continued)
FIGURE 8-5: Attempted security violation by Brady, who’s not an authorized user
*
*
Security (continued)
FIGURE 8-6: Attempted security violation by Paige, who’s authorized to access some customer data but is not authorized to access customer balances
*
*
Disaster PlanningDamage from physical incidentsSoftware/hardware/electricalNatural disastersDisaster recovery plan: ongoing and emergency actions and procedures to ensure data availability if a disaster occursHard drive failuresRedundant array of inexpensive/independent drives (RAID): database updates replicated to multiple hard drives
*
*
Disaster Planning (continued)Electrical power lossUninterruptible power supply (UPS): power source and power generatorDuplicate backup systemsHot site: completely equipped with duplicate hardware, software, and dataCan switch to hot site in minutes or hoursWarm site: duplicate hardware and software but not dataTakes longer to start processing
*
*
ArchivingGovernmental laws and regulations, for example:Sarbannes-Oxley ActPatriot ActHIPAAAuditing and financial requirementsData archive or archive: place where record of certain corporate data is keptStored on mass storage devicesCopies of archives and database backups must be stored off-site
*
*
Archiving (continued)
FIGURE 8-7: Movement of order 21617 from the.
dataactor.csvid,firstName,lastName,gender0,Mr,Ed,M933,Lew.docx
data/actor.csv
id,firstName,lastName,gender
0,Mr,Ed,M
933,Lewis,Abernathy,M
2547,Andrew,Adamson,M
2700,William,Addy,M
2898,"Seth (I)",Adkins,M
2925,"Charles (I)",Adler,M
3226,Casey,Affleck,M
4306,Shigekazu,Aida,M
4856,Julliet,Akinyi,M
6005,Henri,Alciatore,M
6990,Dean,Alexandrou,M
7124,Jim,Alfonso,M
7817,"Carl (I)",Allen,M
7979,"Ivan (I)",Allen,M
8161,"Richard (XV)",Allen,M
8409,Anthony,Allison,M
8412,"Bill (I)",Allison,M
8426,"Frankie J.",Allison,M
9184,Steve,Altes,M
9275,Walter,Altman,M
11489,Dion,Anderson,M
11547,"George (IV)",Anderson,M
11590,"J. Todd",Anderson,M
11865,"Scott G.",Anderson,M
12043,Kris,Andersson,M
12508,"David (I)",Andrews,M
12987,"Jack (I)",Angel,M
13553,Joey,Ansah,M
14741,"Royce D.",Applegate,M
14973,Julian,Arahanga,M
15068,Raul,Aranas,M
15854,Geoffrey,Arend,M
15921,"Guillaume (II)",Aretos,M
16669,Alun,Armstrong,M
16791,"Neil (I)",Armstrong,M
16844,"William (I)",Armstrong,M
17326,Abraham,Aronofsky,M
17433,Alexis,Arquette,M
17438,Richmond,Arquette,M
17946,Michael,Arturo,M
18534,Graham,Ashley,M
18638,"Richard (II)",Ashton,M
18826,Edward,Asner,M
19123,"David (I)",Aston,M
19873,Rene,Auberjonois,M
20565,"Mark (III)",Austin,M
21959,Ryuichiro,Baba,M
22188,James,Babson,M
22262,Mike,Bacarella,M
22489,Joe,Bacino,M
22591,Kevin,Bacon,M
22669,Michael,Badalucco,M
22723,Diedrich,Bader,M
23488,Eion,Bailey,M
24164,"Dylan (I)",Baker,M
24298,"Kenny (I)",Baker,M
24389,"Rick (I)",Baker,M
24660,Matthew,Balaban,M
25103,Christian,Bale,M
25293,Jeremy,Ball,M
25743,Kirk,Baltz,M
26018,John,Bandemer,M
26409,Ian,Bannen,M
28218,"Christopher Daniel",Barnes,M
28510,Roy,Barnitt,M
29603,Jason,Barry,M
29686,Thom,Barry,M
30298,Billy,Barty,M
31092,Dean,Batchelor,M
32474,Joe,Bays,M
32648,Scott,Beach,M
32842,Lightning,Bear,M
32884,"Kevin (I)",Beard,M
33024,Lou,"Beatty Jr.",M
33582,"William (II)",Beck,M
33865,Andy,Beckwith,M
35463,Ned,Bellamy,M
35741,Gil,Bellows,M
36005,John,Belushi,M
36390,Nat,Benchley,M
36472,Lawrence,Bender,M
36641,"Jay (I)",Benedict,M
37040,"David (I)",Benn,M
38306,"Jon (I)",Berg,M
38626,"Loys T.",Bergeron,M
38779,Jules,Bergman,M
38973,"Scott Clark",Beringer,M
39044,Xander,Berkeley,M
39553,Jay,Bernard,M
39860,Steve,Bernie,M
41012,Doug,Beswick,M
41156,Val,Bettin,M
41200,Jack,Betts,M
42278,Michael,Biehn,M
42960,Stephen,Billington,M
43791,"John (I)",Bishop,M
43802,Larry,Bishop,M
43842,"Stephen (I)",Bishop,M
43899,John,Bisom,M
43926,"Richard (II)",Bissell,M
43967,Joel,Bissonnette,M
44737,Abdul,Blackmanwest,M
45134,Geoffrey,Blake,M
45176,Matty,Blake,M
45193,"Paul (II)",Blake,M
45243,Don,Blakely,M
46035,Andrew,Blesser,M
46276,Bobby,Block,M
46793,Stuart,Blumberg,M
47366,Ben,Bode,M
47448,Wolfgang,Bodison,M
47548,Bain,Boehlke,M
47858,Hale,Boggs,M
48064,Bruce,Bohne,M
48468,Bill,Bolender,M
48630,Diedrich,Bollman,M
49086,Josh,Bond,M
49108,Rudy,Bond,M
49661,Bruce,Bonnheim,M
49922,Mark,"Boone Junior",M
50085,"Tim (I)",Booth,M
50561,"Eric Bruno",Borgman,M
51588,Morne,Botes,M
52396,Anderson,Bourell,M
52913,David,Bowe,M
52927,Douglass,"Bowen Flynn",M
52941,C.
Data1. From the Table below prepare the following Financial Statem.docx
Data1. From the Table below prepare the following Financial Statements:and provide a descripition of the uses of each.A. Income StatementB. Balance Sheet2Explain the purpose of the Statement of Cash Flowand explain the three sections, give 2 examples of data whichappears in each section.FY 2017FY 2016Cash$4,000,000$3,700,000Short Term Investments$2,500,000$2,500,000Accounts Receivable$18,500,000$17,000,000Long Term Investments$12,500,000$10,000,000Property, Plant & Equipment (Net)$25,000,000$23,000,000Other Assets$1,500,000$1,250,000$57,450,000Current Portion of Long Term Debt$2,500,000$2,000,000Accounts Payable$4,500,000$4,000,000Long Term Debt$12,500,000$11,500,000Other Liabilities$2,000,000$1,500,000Unrestricted Net Asdets$30,000,000$28,000,000Temporily Restricted Assets$5,000,000$3,000,000Premenently Restricted Assets$7,500,000$7,500,000$57,500,000$64,000,000Net Patient Revenue (Before Bad Debt)$90,000,000$86,000,000Other Operating Revenue$2,000,000$2,000,000Salaries and Wages$45,000,000$44,000,000Benefits$9,000,000$8,500,000Supply Expenses12,500,00012,000,000Depreciation Expense$6,500,000$6,000,000Interest Expense$2,000,000$1,800,000Bad Debt$6,000,000$5,300,000General Expenses$5,000,000$4,500,000Insurance Expenses$1,500,000$1,250,000$371,500,000$286,300,000
Sheet21. From the Table below prepare the following Financial Statements:and provide a descripition of the uses of each.A. Income StatementB. Balance Sheet2Explain the purpose of the Statement of Cash Flowand explain the three sections, give 2 examples of data whichappears in each section.FY 2017FY 2016Cash$4,000,000$3,700,000Short Term Investments$2,500,000$2,500,000Accounts Receivable$18,500,000$17,000,000Long Term Investments$12,500,000$10,000,000Property, Plant & Equipment (Net)$25,000,000$23,000,000Other Assets$1,500,000$1,300,000Current Portion of Long Term Debt$2,500,000$2,000,000Accounts Payable$4,500,000$4,000,000Long Term Debt$12,500,000$11,500,000Other Liabilities$2,000,000$1,500,000Unrestricted Net Assets$30,000,000$28,000,000Temporily Restricted Assets$5,000,000$3,000,000Premenently Restricted Assets$7,500,000$7,500,000Net Patient Revenue (Before Bad Debt)$90,000,000$86,000,000Other Operating Revenue$2,000,000$2,000,000Salaries and Wages$45,000,000$44,000,000Benefits$9,000,000$8,500,000Supply Expenses12,500,00012,000,000Depreciation Expense$6,500,000$6,000,000Interest Expense$2,000,000$1,800,000Bad Debt$6,000,000$5,300,000General Expenses$5,000,000$4,500,000Insurance Expenses$1,500,000$1,250,000$307,500,000$286,350,000
Income StatementIncome StatementOperating Revenue and other supportsFY 2017FY 2016
Balance SheetBalance SheetFY 2017FY 2016
Cash Flow Statement
1
Running Head: OPTIMIZING EMPLOYEE PERFORMANCE
Olena Spears
HRMT300-1803B
Professor Washington
6 Sept 2018
Introduction
· One of the key aspects of business success is the employees’ performance. This means that when a business aims at optimizing the performance of the employees most probably the b.
Data Wrangling for Big Data Challenges andOpportunities.docx
Data Wrangling for Big Data: Challenges and
Opportunities
Tim Furche
Dept. of Computer Science
Oxford University
Oxford OX1 3QD, UK
[email protected]
Georg Gottlob
Dept. of Computer Science
Oxford University
Oxford OX1 3QD, UK
[email protected]
Leonid Libkin
School of Informatics
University of Edinburgh
Edinburgh EH8 9AB, UK
[email protected]
Giorgio Orsi
School. of Computer Science
University of Birmingham
Birmingham, B15 2TT, UK
[email protected]
Norman W. Paton
School of Computer Science
University of Manchester
Manchester M13 9PL, UK
[email protected]
ABSTRACT
Data wrangling is the process by which the data required by an ap-
plication is identified, extracted, cleaned and integrated, to yield a
data set that is suitable for exploration and analysis. Although there
are widely used Extract, Transform and Load (ETL) techniques and
platforms, they often require manual work from technical and do-
main experts at different stages of the process. When confronted
with the 4 V’s of big data (volume, velocity, variety and veracity),
manual intervention may make ETL prohibitively expensive. This
paper argues that providing cost-effective, highly-automated ap-
proaches to data wrangling involves significant research challenges,
requiring fundamental changes to established areas such as data ex-
traction, integration and cleaning, and to the ways in which these
areas are brought together. Specifically, the paper discusses the im-
portance of comprehensive support for context awareness within
data wrangling, and the need for adaptive, pay-as-you-go solutions
that automatically tune the wrangling process to the requirements
and resources of the specific application.
1. INTRODUCTION
Data wrangling has been recognised as a recurring feature of big
data life cycles. Data wrangling has been defined as:
a process of iterative data exploration and transforma-
tion that enables analysis. ([21])
In some cases, definitions capture the assumption that there is sig-
nificant manual effort in the process:
the process of manually converting or mapping data
from one “raw” form into another format that allows
for more convenient consumption of the data with the
help of semi-automated tools. ([35])
c©2016, Copyright is with the authors. Published in Proc. 19th Inter-
national Conference on Extending Database Technology (EDBT), March
15-18, 2016 - Bordeaux, France: ISBN 978-3-89318-070-7, on OpenPro-
ceedings.org. Distribution of this paper is permitted under the terms of the
Creative Commons license CC-by-nc-nd 4.0
The general requirement to reorganise data for analysis is noth-
ing new, with both database vendors and data integration compa-
nies providing Extract, Transform and Load (ETL) products [34].
ETL platforms typically provide components for wrapping data
sources, transforming and combing data from different sources,
and for loading the resulting data into data warehouses, along with
some means of orchestrating the components, such as a work.
Data warehouse design from XML sourcesMatte0 Golfarelli Stef.docx
Data warehouse design from XML sources
Matte0 Golfarelli Stefano Rizzi
DEIS - University of Bologna DEIS - University of Bologna
Viale Risorgimento, 2 Viale Risorgimento, 2
40136 Bologna - Italy 40136 Bologna - Italy
+39-051-642862 +39-051-2093542
[email protected][email protected]
Boris Vrdoljak
FER - University of Zagreb
Unska 3
10000 Zagreb - Croatia
+385-(O)l -6129756
[email protected]
ABSTRACT
A large amount of data needed in decision-making processes is
stored in the XML data format, which is widely used for e-
commerce and Internet-based information exchange. Thus, as
more organizations view the web as an integral part of their
communication and business, the importance of integrating XML
data in data warehousing environments is becoming increasingly
high. In this paper we show how the design of a data mart can be
carried out starting directly from an XML source. Two main
issues arise: on the one hand, since XML models semi-structured
data, not all the information needed for design can be safely
derived; on the other, different approaches for representing
relationships in XML DTDs and Schemas are possible, each with
different expressive power. After discussing these issues, we
propose a semi-automatic approach for building the conceptual
schema for a data mart starting from the XML sources.
Keywords
Data warehouse design, Data warehousing and the web, XML
1. INTRODUCTION
A large amount of data needed in decision-making processes is
stored in the XML (Extensible Markup Language) data format.
The structure of XML, composed of nested custom-defined tags
that can describe the meaning of the content itself, makes it usable
as a semantic-preserving data exchange format on the web. As the
Internet has evolved into a global platform for e-commerce and
information exchange, the interest in XML has been growing and
large volumes of XML data already exist.
XML can be considered as a particular standard syntax for the
exchange of semi-structured data [l]. One common feature of
semi-structured data models is the lack of schema, so that the data
is self-describing. However, XML documents can be associated
with and validated against either a Document Type Definition
(DTD) or an XML Schema, both of which allow the structure of
XML documents to be described and their contents to be
constrained. DTDs are defined as a part of the XML 1.0
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies
are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists.
requires prior specific permission and/or a fee.
DOLAP ‘01, November 9,2001, Atlanta, Georgia, USA.
Copyright 2001 ACM l-581 l3-437-l/Ol/OOl I . ..$5.00.
Specification [15], while XML Schemas have recently become a
W3C Recommendation [16]. XML S.
Data Warehouse Architecture (2 pages) Explain the major components.docx
Data Warehouse Architecture" (2 pages): Explain the major components of a data warehouse architecture, including the various forms of data transformations needed to prepare data for a data warehouse. Also, describe in your own words current key trends in data warehousing
.
Data Visualization double-spaced, have at least 4 APA referenc.docx
Data Visualization
double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format).
Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
.
Data visualization has been around for some time. We remember in the.docx
Data visualization has been around for some time. We remember in the late 90's being struck by the pure awesomeness of “
XPLANATiONS
” infographics published in Business 2.0 magazine. Not long after, we discovered from the great Edward Tufte, the rich history of data visualizations going back perhaps to the earliest of times.
What are effective ways to leverage interactivity in data visualizations? Provide 3 examples of how you would effectively use interactivity in data visualization.
.
Data VisualizationPart C Developing Your Design Solution.docx
Data Visualization
Part C: Developing Your Design
Solution
Chapter 7
Interactivity
Interactivity
Expands physical limits
Increases the quantity
Facilitates manipulation of the data
Increase overall control
Amplifies your creative license
Interactivity cont’
Organize your thinking
Data adjustments: Affecting what data is displayed
Presentation adjustments: Affecting how the data is displayed
Interactivity cont’
Features of Interactivity: Data Adjustments
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Presentation Adjustments
Presentation adjustments: Affecting how the data is displayed
Focusing
Annotating
Orientating
.
Digital Tools and AI for Teaching Learning and Research
This Presentation in details discusses on Digital Tools and AI for Teaching Learning and Research
How to Create Map Views in the Odoo 17 ERP
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
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Database Project CharterBusiness CaseKhalia HartUnive.docx
Database Project Charter/Business Case
Khalia Hart
University of Maryland Global Campus
February 21, 2020
Introduction
A database is an electronic collection of data that is built by a user so that they can access, update particular information in the database coherently or rapidly. Today firms employ integrated technology to increase their capacity to serve more clients, keep information well or effectively, organize activities according to the urgency or priorities, accounting records (Tüttelmann F, 2015). Most of the integrated technology depends on multiple databases that supply information relevant in making the decision. Since the business started using databases, their performance increase because the business decisions they make are sound and practical.
Business Problem
The supply chain management is one of the most complicated processes in the business and often at times due to need of detail it gets hard for the supply chain manager to keep the record of the work covered effectively, have enough data to make the decision and also have enough data to monitor the chain of operation (William, 2019). The supply chain has been so crucial for the business because it determines the performance of the company in the industry by assessing the quality of the product produced in the organization, cost of production, the time and effectiveness of distribution network, and overall production operation of the organization.
Operation management has been named as the leading cause of business failure caused by a lack of a system, which the manager or the supervisor can use to monitor the whole system. This is the problem to solve using the database (William, 2019). Using a database, the manager can observe or watch the entire chain from their office, make better decisions by fore- planning approach of the database also make changes within the system when there is the need to cut costs or making the process effective.
Project Scope
Most business organizations are spread in operation, and this is the challenge that makes the supply chain management complex (Tüttelmann F, 2015). This is because the chain is in different localities, and therefore, coordination of operation among the user or the workers becomes a challenge. Through the database system, the business will enjoy proper coordination using the wide Area Network (LAN). Through the LAN network, the company can link computers and cost-effectively share data and communication. Through this system, the company will have a connection and coordination of the processes within the organization. The number of connected devices will range from 10 to 1000, depending on the type of tools and system that is set to facilitate this connection.
Goals and objectives of the system
The purpose of the system that I want to install in the supply chain management is to;
· Monitoring of the supply chain- the system will enable the manager to monitor the system and every process in the order (Gattor.
Databases selected Multiple databases...Full Text (1223 .docx
Databases selected: Multiple databases...
Full Text (1223 words)
Kraft Reformulates Oreo, Scores in China
Julie Jargon. Wall Street Journal. (Eastern edition). New York, N.Y.: May 1, 2008. pg. B.1
Abstract (Summary)
Kraft, the world's second largest food company by revenue, reported a 13% drop in first-quarter net income Wednesday
because of high commodity costs and increased spending on product research and marketing. Television commercials
showed kids twisting apart Oreo cookies, licking the cream center and dipping the chocolate cookie halves into glasses of
milk.
(c) 2008 Dow Jones & Company, Inc. Reproduced with permission of copyright owner. Further reproduction or distribution
is prohibited without permission.
Unlike its iconic American counterpart, the Oreo sold in China is frequently long, thin, four-layered and coated in chocolate. But
both kinds of cookies have one important thing in common: They are now best sellers.
The Oreo has long been the top-selling cookie in the U.S. market. But Kraft Foods Inc. had to reinvent the Oreo to make it sell
well in the world's most populous nation. While Chinese Oreo sales represent a tiny fraction of Kraft's $37.2 billion in annual
revenue, the cookie's journey in China exemplifies the kind of entrepreneurial transformation that Chief Executive Irene
Rosenfeld is trying to spread throughout the food giant.
Kraft, the world's second largest food company by revenue, reported a 13% drop in first-quarter net income Wednesday
because of high commodity costs and increased spending on product research and marketing. Its international business,
which now represents 40% of Kraft's revenue thanks to the company's recent acquisition of Groupe Danone's biscuits
business, was a bright spot in the quarter, aided by the weak dollar. Kraft's profit in the European Union rose 48%, excluding
special charges, and its profit in developing markets rose 57%.
To try to increase growth at the company, Ms. Rosenfeld has been putting more power in the hands of Kraft's various
business units around the globe, telling employees that decisions about Kraft products shouldn't all be made by people at the
Northfield, Ill., headquarters.
To take advantage of the European preference for dark chocolate, Kraft is introducing dark chocolate in Germany under its
Milka brand. Research in Russia showed that consumers there like premium instant coffee, so Kraft is positioning its Carte
Noire freeze-dried coffee as upscale by placing it at film festivals, fashion shows and operas. And in the Philippines, where
iced tea is popular, Kraft last year launched iced-tea-flavored Tang. Ms. Rosenfeld has also been encouraging marketers to
"reframe" product categories, no longer thinking, for example, that an Oreo has to be a round sandwich cookie.
Oreos were first introduced in 1912 in the U.S., but it wasn't until 1996 that Kraft introduced Oreos to Chinese consumers.
Nine years later, a makeover began. Shawn Warren, a 37-year-old .
Database SystemsDesign, Implementation, and ManagementCo.docx
Database Systems
Design, Implementation, and Management
Coronel | Morris
11e
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Chapter 3
The Relational Database Model
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Learning ObjectivesIn this chapter, one will learn:That the relational database model offers a logical view of dataAbout the relational model’s basic component: relationsThat relations are logical constructs composed of rows (tuples) and columns (attributes)That relations are implemented as tables in a relational DBMS
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Learning ObjectivesIn this chapter, one will learn:About relational database operators, the data dictionary, and the system catalogHow data redundancy is handled in the relational database modelWhy indexing is important
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
A Logical View of DataRelational database model enables logical representation of the data and its relationshipsLogical simplicity yields simple and effective database design methodologies Facilitated by the creation of data relationships based on a logical construct called a relation
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Table 3.1 - Characteristics of a Relational Table
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
KeysConsist of one or more attributes that determine other attributesUsed to: Ensure that each row in a table is uniquely identifiableEstablish relationships among tables and to ensure the integrity of the dataPrimary key (PK): Attribute or combination of attributes that uniquely identifies any given row
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
DeterminationState in which knowing the value of one attribute makes it possible to determine the value of anotherIs the basis for establishing the role of a key Based on the relationships among the attributes
*
©2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
DependenciesFunctional dependence: Value of one or more attributes determines the value of one or more other attributesDeterminant: Attribute whose value determines another Dependent: Attribute whose value is determined by the.
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN1DATABASE SYS.docx
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN 1
DATABASE SYSTEMS DEVELOPMENT & IMPLEMENTATION PLAN 19
Table of Contents
1. Database System Overview 3
1.1 Business Environment 3
1.2 Database system goals and objective 4
2. Entity Relationship Model 7
2.1 Proposed entities 7
2.2 Business rules 8
2.3 Entity–Relationship Model 9
2.3.1 Relationship Types 9
2.3.2 Normalization form 12
2.3.3 Benefit of using database design 14
3. Structured Query Language (SQL) Scripts 15
3.1 Data definition language (DDL) 15
3.2 Data manipulation language (DML) 16
3.3 SQL report 17
3.4 Benefit of using database queries 19
4. Database Administration Plan 20
5. Future Database System Implementation Plan 21
6. References 22
1.
Database System Overview
1.1 Business Environment
Office Depot, Inc is an American retail store company founded in 1986 and headquartered in Florida, United States. The company provides office and school supplies with 1400 retail stores and e-commerce sites. The supply includes everything to their customer like latest technology, core school and office supplies, printing and documenting service, furniture and other services like cell phone repair, tech and marketing service etc.
Recently there were too many complaints from existing and new customer that the online site is super glitch and lagging. Another customer posted that the delivery did not come on the scheduled day. And they cannot track down the order because the website does not have tracking information. Also when the website is down, customer service cannot help to see the order details either and therefore, they feel it’s frustrating to order online and therefore want to cancel the order. One other customer posted in the website grievance section that the “label maker” showed available in the stock even though it was out of stock when verified with the customer service representative. With every product not in stock, we lose opportunity of sale which costs the store. This not only affect customer but also affect company. We are so dependent on the data, most of the time staff has to correct accounting report, sales estimates and invoice customer manually which is very time-consuming in an excel sheet.
In order to solve above issues and avoid sales loss, Office Depot must have a database to store and maintain correct count of the products. This database will help inventory management i.e. tracking products, update inventory, find popular or less popular item, loss prevention, track inventory status and perform data mining. The staff can access this database via a computerized database. (Gerald H., Importance of inventory database retail)1.2 Database system goals and objective
The mission of the company is to become number one retail company by creating inclusive environment and great shopping experience where both customer and employees are respected and valued. To achieve the retail store mission, we are committed to provide secure and robust data base system for ou.
Database Security Assessment Transcript You are a contracting office.docx
Database Security Assessment Transcript You are a contracting officer's technical representative, a Security System Engineer, at a military hospital. Your department's leaders are adopting a new medical health care database management system. And they've tasked you to create a request for proposal for which different vendors will compete to build and provide to the hospital. A Request For Proposal, or RFP, is when an organization sends out a request for estimates on performing a function, delivering a technology, or providing a service or augmenting staff. RFPs are tailored to each endeavor but have common components and are important in the world of IT contracting and for procurement and acquisitions. To complete the RFP, you must determine the technical and security specifications for the system. You'll write the requirements for the overall system and also provide evaluation standards that will be used in rating the vendor's performance. Your learning will help you determine your system's requirements. As you discover methods of attack, you'll write prevention and remediation requirements for the vendor to perform. You must identify the different vulnerabilities the database should be hardened against.
Modern healthcare systems incorporate databases for effective and efficient management of patient healthcare. Databases are vulnerable to cyberattacks and must be designed and built with security controls from the beginning of the life cycle. Although hardening the database early in the life cycle is better, security is often incorporated after deployment, forcing hospital and healthcare IT professionals to play catch-up. Database security requirements should be defined at the requirements stage of acquisition and procurement.
System security engineers and other acquisition personnel can effectively assist vendors in building better healthcare database systems by specifying security requirements up front within the request for proposal (RFP). In this project, you will be developing an RFP for a new medical healthcare database management system.
Parts of your deliverables will be developed through your learning lab. You will submit the following deliverables for this project:
Deliverables
• An RFP, about 10 to 12 pages, in the form of a double-spaced Word document with citations in APA format. The page count does not include figures, diagrams, tables, or citations. There is no penalty for using additional pages. Include a minimum of six references. Include a reference list with the report.
• An MS-Excel spreadsheet with lab results.
There are 11 steps in this project. You will begin with the workplace scenario and continue with Step 1: "Provide an Overview for Vendors."
Step 1: Provide an Overview for Vendors
As the contracting officer's technical representative (COTR), you are the liaison between your hospital and potential vendors. It is your duty to provide vendors with an overview of your organization. To do so, identify infor.
Data.docx
Data:
Hours
Period
Cycle Time
1
45
2
33
3
44
4
30
5
51
6
47
7
39
8
34
9
69
10
43
11
29
12
38
13
45
14
49
15
73
16
38
17
50
18
40
19
57
20
36
21
31
22
46
23
40
24
51
25
48
26
37
27
42
28
50
1. Calculate R-Bar?
2. Upper Control Limit for the Range?
3 Upper control limit for the individuals?
4 Lower control limit for the individuals?
5. What is the control chart telling you?
6. Is there adequate discrimination?
.
Database Design Mid Term ExamSpring 2020Name ________________.docx
Database Design Mid Term Exam
Spring 2020
Name: ____________________________
1. What is a data model?
A. method of storing files on a disk drive
B. simple representation of complex real-world data structures
C. name of system for designing software
D. method of designing invoices for customers
2. A Relationship Database system consists of 3 parts: a client front end for sending information to a command processor, a middle tier that interprets user commands, and a management frame work for storing, organizing and securing data.
a. True
b. False
3. What are the 3 components of a table:
A. Row, column, value
B. Row, top, bottom
C. Column, row, top
D. Top, middle, end
4. What does the column represent in a table?
a. Attribute of the table records
b. A complete record in the table
c. The system log from the database
d. A list of database tables
5. What does a row in the table represent?
a. A complete data record
b. List of system logs
c. A list of file systems on database server
d. The primary keys from all the tables.
6. Which of the following is an example of data definition language (DDL)?
a. UPDATE
b. V$SYSLOG
c. CREATE
d. DETAIN
7 . Which of the following is an example of data manipulation language (DML)?
A. SELECT
B. ABORT
C. GRANT
D. REVOKE
8. A _______ key is an attribute that uniquely identifies a record in a table.
9. A _______ key is an attribute that is a primary key in one table and is used as a reference in a second table to establish a relationship between the two tables.
10. When running a ‘SELECT’ join, what is returned from the table:
A. ROW
B. Column
C. single attribute
D. all tables in the database
11. When running a ‘PROJECT’ join, what is returned from the table:
A. COLUMN
B. ROW
C. Single Attribute
D. a list of tables in the database
12. What are the 3 types of relationships commonly shown on an entity relationship diagram?
A. 1 to 1
B. 1 to Many
C. Many to Many
D. All the above
E. None of the above
13. What is an entity relationship diagram (ERD)?
A. graphical representation of all entities in a database and how the entities are related
b. list of the log files in the database.
C. list of all the tablespace names in a database
D. A diagram that shows how data is written to a physical disk drive.
14. The definition of an attribute in a table that has no value is:
A. ZERO
b. NULL
c. ZILTCH
D. NONE
15. A ____________ attribute can either be stored on retrieve on an ad hoc basis.
16. Briefly describe the advantages and disadvantages of storing a derived attribute?
17. A database can process many types of data classifications. Which of the following is not a data classification or architecture that databases can process:
A. Structured
B. Semi-structured
C. undelimited
D. Unstructured
18. The process by which functional/partial dependency and transitive dependency is removed from a database table is called:
a. sharding
b. normalization
c. defragmentation
d. reallocation
.
Database Justification MemoCreate a 1-page memo for the .docx
Database Justification Memo
C
reate
a
1-page
memo for the project stakeholder
explaining
why they should migrate towards a database driven application system
rather
than a static website.
Discuss
the benefits and drawbacks of the proposed changes
AND
Web Services Memo
Create
a 1-page memo to the project stakeholder on the importance of web services including security considerations, scalability, and compatibility.
.
Database Dump Script(Details of project in file)Mac1) O.docx
Database Dump Script
(Details of project in file)
Mac:
1) Open up the terminal, or if already in MySQL, get out by typing "exit" and pressing enter.
2) Type:
/usr/local/mysql/bin/mysqldump -u root -p [database name] > /tmp/filename.txt
...where [database name] is the name of the database you want to export. When prompted, type the password. Check the /tmp file for your output.
.
Database Design 1. What is a data model A. method of sto.docx
Database Design
1. What is a data model?
A. method of storing files on a disk drive
B. simple representation of complex real-world data structures
C. name of system for designing software
D. method of designing invoices for customers
2. Which of the following are the most important elements of a security program for databases:
a. Integrity, referential index, user rights
b. Confidentiality. Integrity and Availability
c. Availability, multi-master replication, high-bandwidth
d. DBA, System Admin, and PMO
3. Suppose that you have a table with a number of product sales. The product code may repeat in the table as it is likely the same product could be sold multiple times. If you want to produce a list of the unique products that are sold, you could use which of the following keywords in the SELECT statement:
A. LIKE
B. ORDERED BY
C. DISTINCT
D. DIFFERENT
4. What does the column represent in a table?
a. Attribute of the table records
b. A complete record in the table
c. The system log from the database
d. A list of database tables
5. What does a row in the table represent?
a. A complete data record
b. List of system logs
c. A list of file systems on database server
d. The primary keys from all the tables.
6. Which of the following is an example of data definition language (DDL)?
a. UPDATE
b. V$SYSLOG
c. CREATE
d. DETAIN
7 . Which of the following is an example of data manipulation language (DML)?
A. SELECT
B. ABORT
C. GRANT
D. REVOKE
8. A _____________ key is an attribute that uniquely identifies a record in a table.
9. A _____________ key is an attribute that is a primary key in one table and is used as a reference in a second table to establish a relationship between the two tables.
10. When running a ‘SELECT’ join, what is returned from the table:
A. ROW
B. Column
C. single attribute
D. all tables in the database
11. When running a ‘PROJECT’ join, what is returned from the table:
A. COLUMN
B. ROW
C. Single Attribute
D. a list of tables in the database
12. What are the 3 types of relationships commonly shown on an entity relationship diagram?
A. 1 to 1
B. 1 to Many
C. Many to Many
D. All the above
E. None of the above
13. What is an entity relationship diagram (ERD)?
A. graphical representation of all entities in a database and how the entities are related
b. list of the log files in the database.
C. list of all the tablespace names in a database
D. A diagram that shows how data is written to a physical disk drive.
14. The definition of an attribute in a table that has no value is:
A. ZERO
b. NULL
c. ZILTCH
D. NONE
15. A __________ attribute can either be stored on retrieve on an ad hoc basis.
16. Which of the following is not considered a characteristic of distributed management systems:
a. Concurrency Control
b. Business intelligence
c. Transaction management
d. query optimization
17. A database can process many types of data classifications. Which of the following is not a data class.
DataAGEGENDERETHNICMAJORSEMHOUSEGPAHRSNEWSPAPTVHRSSLEEPWEIGHTHEIGH.docx
DataAGEGENDERETHNICMAJORSEMHOUSEGPAHRSNEWSPAPTVHRSSLEEPWEIGHTHEIGHTCARJOBUSBORNSTATEGREEKPMSMEDFEDSIBLINGSCALLDADRELMOMRELSIBRELFAMRELCLUBSVOLHOURSCLUBHRSDEGREEEFFORTSTUDYDIFFWRKOUTWOMOTPHYFITIMAGEWOPUBPHYFITIMPCALNUTRITIONHEALTHDECFRUITVEGSMOKEDRINKBLACKOUT201CaucHealth Management & Policy3On3.6152071105'3No0YesIN114413444432021B4121E342441000555131191CaucSPHS-S3Off3.53161281105'3Yes0YesPA2255274444000A464C144441000455121202CaucLaw and Public Policy3On3.551831161355'8No0YesIN21332123232712C371B432322500443112221CaucManagement7On3.6163481585'9Yes3.5YesIN1145364444329B452D241441670455121201LatinaArts Management3Off3.2172108.51105'1Yes0YesNJ22331104444000A4103C143341200441131232CaucSafty9Off3.3182981605'10Yes21YesIN2133184434009B363D333331000342122212CaucSpeech and Hearing5Off3.8418245.51505'9Yes0YesIN2344120344323020B4123D251442000555121201CaucSpeech and Hearing3Off3.861521.57.51505'4Yes0YesIN254523.53444109B483B232321500333122202African AmericanBusiness3Off312247.51805'11Yes0YesIN23423544441010B342E253443000322122212CaucMicrobiology5Off3.31711071255'9Yes0YesOH2155254333000C282E341231700433121201South AfricanManagement5Off2.95152381005'2Yes0NoNJ2135224234100A453A234121200343241211CaucNonprofit5Off3152471505'9Yes15YesIN22342714442100B4153D131241500344121201African AmericanHealth Admin5Off31511581505'4Yes25YesIN224534344412510B361B513321000121122211African AmericanComm Health7Off2.7141157.51805'2Yes0YesIN22145504434000A352A11244700232121191CaucBiology3On3.88142171215'3No0YesIN2355183444210.5C4403C343242000454112221Sri LankanExercise Science6Off3.4117227.51305'5No0NoSri Lanka21440144041020C4152D242441500454112201CaucEnvironmental Management3Off3.5212081405'3No10.5YesIN2545813424422B3173D242332500444112202CaucManagement3Off2.31512091606'0Yes0YesIN2124034404006A3124B343242500342121201YellowManagement4Off3.2152071155'2No0NoSichuan2125054414002A3153B33323300233112211CaucArts Management5Off2.91620.56.51446'3Yes20YesIN2243154444503A37.52A231332000444122201CaucManagement3Off3.5152381405'5No0YesIN21434503444101A4153C231331200443141211CaucExercise Science5Off3.621522181805'7Yes15 YesIN2243244433321B4353C222231400343122211CaucExercise Science5Off3.98142571705'10Yes0YesIN2133143424201C452B332441700342122202CaucExercise Sciene5Off3.61723082105'10Yes0YesIN21222103344000B4202C233322000445122201CaucArt History3Off3.5151261605'4Yes0YesIN2134234444201A372B132431300334121201CaucHuman Development & Family Studies3On3.6152981505'6Yes8YesIN21442104344013B473C5322318000333121212CaucManagement7Off3.51182781756'0Yes0NoSC1245254444201B453C542442000432122182CaucBSPA Management1On3.67154371405'10No0YesIN2355153434212B4251A232332200453122212CaucExercise Science5On3.5616294.52126'3Yes0YesIN1245274444213.511C4152D353443900443131201African AmericanNonprofit Management3Off3.215367.51345'4Yes10YesIN2322673444388B4103E24244300553221202CaucArts Administration3Off3.6172071405'10Yes0YesIN1154113333264A383B222221500433121211CaucHealth Admin5Off31511561455'5Yes5YesTX2344144444200B452D.
Database AdministrationObjectivesDiscus.docx
Database Administration
*
ObjectivesDiscuss the need for database administrationExplain the DBA’s responsibilities in formulating and enforcing database policies for access privileges, security, disaster planning, and archivingDiscuss the DBA’s administrative responsibilities for DBMS evaluation and selection, DBMS maintenance, data dictionary management, and trainingDiscuss the DBA’s technical responsibilities for database design, testing, and performance tuning
*
*
Introduction
FIGURE 8-1: DBA responsibilities
*
*
Database Policy Formulation and EnforcementDBAFormulates database policiesCommunicates policies to usersEnforces policiesPoliciesAccess privilegesSecurityDisaster planningArchiving
*
*
Access PrivilegesDBADetermines access privileges for all usersEnters appropriate authorization rules in DBMSSQL GRANT statementAccess privilege policyDocumented by DBAApproved by top-level managementCommunicated by DBA to all users
*
*
Access Privileges (continued)
FIGURE 8-2: Permitted and denied access privileges for Sam
*
*
Access Privileges (continued)
FIGURE 8-4: Permitted and denied access privileges for Valerie
*
*
SecurityPrevention of unauthorized access, intentional or accidental, to databaseDBACreates security policies and proceduresObtains management approval of policies and proceduresDistributes policies and procedures to authorized users
*
*
Security (continued)DBMS’s security featuresEncryptionAuthenticationAuthorizationsViewsAdditional security programs may be created or purchasedMonitoring of database usage to detect security violations
*
*
Security (continued)
FIGURE 8-5: Attempted security violation by Brady, who’s not an authorized user
*
*
Security (continued)
FIGURE 8-6: Attempted security violation by Paige, who’s authorized to access some customer data but is not authorized to access customer balances
*
*
Disaster PlanningDamage from physical incidentsSoftware/hardware/electricalNatural disastersDisaster recovery plan: ongoing and emergency actions and procedures to ensure data availability if a disaster occursHard drive failuresRedundant array of inexpensive/independent drives (RAID): database updates replicated to multiple hard drives
*
*
Disaster Planning (continued)Electrical power lossUninterruptible power supply (UPS): power source and power generatorDuplicate backup systemsHot site: completely equipped with duplicate hardware, software, and dataCan switch to hot site in minutes or hoursWarm site: duplicate hardware and software but not dataTakes longer to start processing
*
*
ArchivingGovernmental laws and regulations, for example:Sarbannes-Oxley ActPatriot ActHIPAAAuditing and financial requirementsData archive or archive: place where record of certain corporate data is keptStored on mass storage devicesCopies of archives and database backups must be stored off-site
*
*
Archiving (continued)
FIGURE 8-7: Movement of order 21617 from the.
dataactor.csvid,firstName,lastName,gender0,Mr,Ed,M933,Lew.docx
data/actor.csv
id,firstName,lastName,gender
0,Mr,Ed,M
933,Lewis,Abernathy,M
2547,Andrew,Adamson,M
2700,William,Addy,M
2898,"Seth (I)",Adkins,M
2925,"Charles (I)",Adler,M
3226,Casey,Affleck,M
4306,Shigekazu,Aida,M
4856,Julliet,Akinyi,M
6005,Henri,Alciatore,M
6990,Dean,Alexandrou,M
7124,Jim,Alfonso,M
7817,"Carl (I)",Allen,M
7979,"Ivan (I)",Allen,M
8161,"Richard (XV)",Allen,M
8409,Anthony,Allison,M
8412,"Bill (I)",Allison,M
8426,"Frankie J.",Allison,M
9184,Steve,Altes,M
9275,Walter,Altman,M
11489,Dion,Anderson,M
11547,"George (IV)",Anderson,M
11590,"J. Todd",Anderson,M
11865,"Scott G.",Anderson,M
12043,Kris,Andersson,M
12508,"David (I)",Andrews,M
12987,"Jack (I)",Angel,M
13553,Joey,Ansah,M
14741,"Royce D.",Applegate,M
14973,Julian,Arahanga,M
15068,Raul,Aranas,M
15854,Geoffrey,Arend,M
15921,"Guillaume (II)",Aretos,M
16669,Alun,Armstrong,M
16791,"Neil (I)",Armstrong,M
16844,"William (I)",Armstrong,M
17326,Abraham,Aronofsky,M
17433,Alexis,Arquette,M
17438,Richmond,Arquette,M
17946,Michael,Arturo,M
18534,Graham,Ashley,M
18638,"Richard (II)",Ashton,M
18826,Edward,Asner,M
19123,"David (I)",Aston,M
19873,Rene,Auberjonois,M
20565,"Mark (III)",Austin,M
21959,Ryuichiro,Baba,M
22188,James,Babson,M
22262,Mike,Bacarella,M
22489,Joe,Bacino,M
22591,Kevin,Bacon,M
22669,Michael,Badalucco,M
22723,Diedrich,Bader,M
23488,Eion,Bailey,M
24164,"Dylan (I)",Baker,M
24298,"Kenny (I)",Baker,M
24389,"Rick (I)",Baker,M
24660,Matthew,Balaban,M
25103,Christian,Bale,M
25293,Jeremy,Ball,M
25743,Kirk,Baltz,M
26018,John,Bandemer,M
26409,Ian,Bannen,M
28218,"Christopher Daniel",Barnes,M
28510,Roy,Barnitt,M
29603,Jason,Barry,M
29686,Thom,Barry,M
30298,Billy,Barty,M
31092,Dean,Batchelor,M
32474,Joe,Bays,M
32648,Scott,Beach,M
32842,Lightning,Bear,M
32884,"Kevin (I)",Beard,M
33024,Lou,"Beatty Jr.",M
33582,"William (II)",Beck,M
33865,Andy,Beckwith,M
35463,Ned,Bellamy,M
35741,Gil,Bellows,M
36005,John,Belushi,M
36390,Nat,Benchley,M
36472,Lawrence,Bender,M
36641,"Jay (I)",Benedict,M
37040,"David (I)",Benn,M
38306,"Jon (I)",Berg,M
38626,"Loys T.",Bergeron,M
38779,Jules,Bergman,M
38973,"Scott Clark",Beringer,M
39044,Xander,Berkeley,M
39553,Jay,Bernard,M
39860,Steve,Bernie,M
41012,Doug,Beswick,M
41156,Val,Bettin,M
41200,Jack,Betts,M
42278,Michael,Biehn,M
42960,Stephen,Billington,M
43791,"John (I)",Bishop,M
43802,Larry,Bishop,M
43842,"Stephen (I)",Bishop,M
43899,John,Bisom,M
43926,"Richard (II)",Bissell,M
43967,Joel,Bissonnette,M
44737,Abdul,Blackmanwest,M
45134,Geoffrey,Blake,M
45176,Matty,Blake,M
45193,"Paul (II)",Blake,M
45243,Don,Blakely,M
46035,Andrew,Blesser,M
46276,Bobby,Block,M
46793,Stuart,Blumberg,M
47366,Ben,Bode,M
47448,Wolfgang,Bodison,M
47548,Bain,Boehlke,M
47858,Hale,Boggs,M
48064,Bruce,Bohne,M
48468,Bill,Bolender,M
48630,Diedrich,Bollman,M
49086,Josh,Bond,M
49108,Rudy,Bond,M
49661,Bruce,Bonnheim,M
49922,Mark,"Boone Junior",M
50085,"Tim (I)",Booth,M
50561,"Eric Bruno",Borgman,M
51588,Morne,Botes,M
52396,Anderson,Bourell,M
52913,David,Bowe,M
52927,Douglass,"Bowen Flynn",M
52941,C.
Data1. From the Table below prepare the following Financial Statem.docx
Data1. From the Table below prepare the following Financial Statements:and provide a descripition of the uses of each.A. Income StatementB. Balance Sheet2Explain the purpose of the Statement of Cash Flowand explain the three sections, give 2 examples of data whichappears in each section.FY 2017FY 2016Cash$4,000,000$3,700,000Short Term Investments$2,500,000$2,500,000Accounts Receivable$18,500,000$17,000,000Long Term Investments$12,500,000$10,000,000Property, Plant & Equipment (Net)$25,000,000$23,000,000Other Assets$1,500,000$1,250,000$57,450,000Current Portion of Long Term Debt$2,500,000$2,000,000Accounts Payable$4,500,000$4,000,000Long Term Debt$12,500,000$11,500,000Other Liabilities$2,000,000$1,500,000Unrestricted Net Asdets$30,000,000$28,000,000Temporily Restricted Assets$5,000,000$3,000,000Premenently Restricted Assets$7,500,000$7,500,000$57,500,000$64,000,000Net Patient Revenue (Before Bad Debt)$90,000,000$86,000,000Other Operating Revenue$2,000,000$2,000,000Salaries and Wages$45,000,000$44,000,000Benefits$9,000,000$8,500,000Supply Expenses12,500,00012,000,000Depreciation Expense$6,500,000$6,000,000Interest Expense$2,000,000$1,800,000Bad Debt$6,000,000$5,300,000General Expenses$5,000,000$4,500,000Insurance Expenses$1,500,000$1,250,000$371,500,000$286,300,000
Sheet21. From the Table below prepare the following Financial Statements:and provide a descripition of the uses of each.A. Income StatementB. Balance Sheet2Explain the purpose of the Statement of Cash Flowand explain the three sections, give 2 examples of data whichappears in each section.FY 2017FY 2016Cash$4,000,000$3,700,000Short Term Investments$2,500,000$2,500,000Accounts Receivable$18,500,000$17,000,000Long Term Investments$12,500,000$10,000,000Property, Plant & Equipment (Net)$25,000,000$23,000,000Other Assets$1,500,000$1,300,000Current Portion of Long Term Debt$2,500,000$2,000,000Accounts Payable$4,500,000$4,000,000Long Term Debt$12,500,000$11,500,000Other Liabilities$2,000,000$1,500,000Unrestricted Net Assets$30,000,000$28,000,000Temporily Restricted Assets$5,000,000$3,000,000Premenently Restricted Assets$7,500,000$7,500,000Net Patient Revenue (Before Bad Debt)$90,000,000$86,000,000Other Operating Revenue$2,000,000$2,000,000Salaries and Wages$45,000,000$44,000,000Benefits$9,000,000$8,500,000Supply Expenses12,500,00012,000,000Depreciation Expense$6,500,000$6,000,000Interest Expense$2,000,000$1,800,000Bad Debt$6,000,000$5,300,000General Expenses$5,000,000$4,500,000Insurance Expenses$1,500,000$1,250,000$307,500,000$286,350,000
Income StatementIncome StatementOperating Revenue and other supportsFY 2017FY 2016
Balance SheetBalance SheetFY 2017FY 2016
Cash Flow Statement
1
Running Head: OPTIMIZING EMPLOYEE PERFORMANCE
Olena Spears
HRMT300-1803B
Professor Washington
6 Sept 2018
Introduction
· One of the key aspects of business success is the employees’ performance. This means that when a business aims at optimizing the performance of the employees most probably the b.
Data Wrangling for Big Data Challenges andOpportunities.docx
Data Wrangling for Big Data: Challenges and
Opportunities
Tim Furche
Dept. of Computer Science
Oxford University
Oxford OX1 3QD, UK
[email protected]
Georg Gottlob
Dept. of Computer Science
Oxford University
Oxford OX1 3QD, UK
[email protected]
Leonid Libkin
School of Informatics
University of Edinburgh
Edinburgh EH8 9AB, UK
[email protected]
Giorgio Orsi
School. of Computer Science
University of Birmingham
Birmingham, B15 2TT, UK
[email protected]
Norman W. Paton
School of Computer Science
University of Manchester
Manchester M13 9PL, UK
[email protected]
ABSTRACT
Data wrangling is the process by which the data required by an ap-
plication is identified, extracted, cleaned and integrated, to yield a
data set that is suitable for exploration and analysis. Although there
are widely used Extract, Transform and Load (ETL) techniques and
platforms, they often require manual work from technical and do-
main experts at different stages of the process. When confronted
with the 4 V’s of big data (volume, velocity, variety and veracity),
manual intervention may make ETL prohibitively expensive. This
paper argues that providing cost-effective, highly-automated ap-
proaches to data wrangling involves significant research challenges,
requiring fundamental changes to established areas such as data ex-
traction, integration and cleaning, and to the ways in which these
areas are brought together. Specifically, the paper discusses the im-
portance of comprehensive support for context awareness within
data wrangling, and the need for adaptive, pay-as-you-go solutions
that automatically tune the wrangling process to the requirements
and resources of the specific application.
1. INTRODUCTION
Data wrangling has been recognised as a recurring feature of big
data life cycles. Data wrangling has been defined as:
a process of iterative data exploration and transforma-
tion that enables analysis. ([21])
In some cases, definitions capture the assumption that there is sig-
nificant manual effort in the process:
the process of manually converting or mapping data
from one “raw” form into another format that allows
for more convenient consumption of the data with the
help of semi-automated tools. ([35])
c©2016, Copyright is with the authors. Published in Proc. 19th Inter-
national Conference on Extending Database Technology (EDBT), March
15-18, 2016 - Bordeaux, France: ISBN 978-3-89318-070-7, on OpenPro-
ceedings.org. Distribution of this paper is permitted under the terms of the
Creative Commons license CC-by-nc-nd 4.0
The general requirement to reorganise data for analysis is noth-
ing new, with both database vendors and data integration compa-
nies providing Extract, Transform and Load (ETL) products [34].
ETL platforms typically provide components for wrapping data
sources, transforming and combing data from different sources,
and for loading the resulting data into data warehouses, along with
some means of orchestrating the components, such as a work.
Data warehouse design from XML sourcesMatte0 Golfarelli Stef.docx
Data warehouse design from XML sources
Matte0 Golfarelli Stefano Rizzi
DEIS - University of Bologna DEIS - University of Bologna
Viale Risorgimento, 2 Viale Risorgimento, 2
40136 Bologna - Italy 40136 Bologna - Italy
+39-051-642862 +39-051-2093542
[email protected][email protected]
Boris Vrdoljak
FER - University of Zagreb
Unska 3
10000 Zagreb - Croatia
+385-(O)l -6129756
[email protected]
ABSTRACT
A large amount of data needed in decision-making processes is
stored in the XML data format, which is widely used for e-
commerce and Internet-based information exchange. Thus, as
more organizations view the web as an integral part of their
communication and business, the importance of integrating XML
data in data warehousing environments is becoming increasingly
high. In this paper we show how the design of a data mart can be
carried out starting directly from an XML source. Two main
issues arise: on the one hand, since XML models semi-structured
data, not all the information needed for design can be safely
derived; on the other, different approaches for representing
relationships in XML DTDs and Schemas are possible, each with
different expressive power. After discussing these issues, we
propose a semi-automatic approach for building the conceptual
schema for a data mart starting from the XML sources.
Keywords
Data warehouse design, Data warehousing and the web, XML
1. INTRODUCTION
A large amount of data needed in decision-making processes is
stored in the XML (Extensible Markup Language) data format.
The structure of XML, composed of nested custom-defined tags
that can describe the meaning of the content itself, makes it usable
as a semantic-preserving data exchange format on the web. As the
Internet has evolved into a global platform for e-commerce and
information exchange, the interest in XML has been growing and
large volumes of XML data already exist.
XML can be considered as a particular standard syntax for the
exchange of semi-structured data [l]. One common feature of
semi-structured data models is the lack of schema, so that the data
is self-describing. However, XML documents can be associated
with and validated against either a Document Type Definition
(DTD) or an XML Schema, both of which allow the structure of
XML documents to be described and their contents to be
constrained. DTDs are defined as a part of the XML 1.0
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies
are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists.
requires prior specific permission and/or a fee.
DOLAP ‘01, November 9,2001, Atlanta, Georgia, USA.
Copyright 2001 ACM l-581 l3-437-l/Ol/OOl I . ..$5.00.
Specification [15], while XML Schemas have recently become a
W3C Recommendation [16]. XML S.
Data Warehouse Architecture (2 pages) Explain the major components.docx
Data Warehouse Architecture" (2 pages): Explain the major components of a data warehouse architecture, including the various forms of data transformations needed to prepare data for a data warehouse. Also, describe in your own words current key trends in data warehousing
.
Data Visualization double-spaced, have at least 4 APA referenc.docx
Data Visualization
double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format).
Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
.
Data visualization has been around for some time. We remember in the.docx
Data visualization has been around for some time. We remember in the late 90's being struck by the pure awesomeness of “
XPLANATiONS
” infographics published in Business 2.0 magazine. Not long after, we discovered from the great Edward Tufte, the rich history of data visualizations going back perhaps to the earliest of times.
What are effective ways to leverage interactivity in data visualizations? Provide 3 examples of how you would effectively use interactivity in data visualization.
.
Data VisualizationPart C Developing Your Design Solution.docx
Data Visualization
Part C: Developing Your Design
Solution
Chapter 7
Interactivity
Interactivity
Expands physical limits
Increases the quantity
Facilitates manipulation of the data
Increase overall control
Amplifies your creative license
Interactivity cont’
Organize your thinking
Data adjustments: Affecting what data is displayed
Presentation adjustments: Affecting how the data is displayed
Interactivity cont’
Features of Interactivity: Data Adjustments
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Features of Interactivity: Data Adjustments cont’
Presentation Adjustments
Presentation adjustments: Affecting how the data is displayed
Focusing
Annotating
Orientating
.
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This slide is prepared for master's students (MIFB & MIBS) UUM. May it be useful to all.Cambridge International AS A Level Biology Coursebook - EBook (MaryFosbery J...
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For more information, visit-www.vavaclasses.com
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special B.ed 2nd year old paper_20240531.pdf
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Digital Tools and AI for Teaching Learning and Research
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Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
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Chapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptx
Cambridge International AS A Level Biology Coursebook - EBook (MaryFosbery J...
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