Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
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Capacity Planning For Your Growing MongoDB ClusterMongoDB
Your MongoDB deployment is growing, but are you prepared for that growth? Capacity planning is an essential practice when deploying any database system. You need to understand your usage patterns and determine the appropriate hardware based on your application's needs. Scaling reads and scaling writes will require different types of resources. With the proper tools in place, you can understand your working set, gain visibility into when it's time to add resources or start sharding and avoid performance issues. In this session, you'll learn how to use MongoDB Management Service and other tools to identify patterns and predict growth, ensuring your success with MongoDB.
What is Data ?
What is Information?
Data Models, Schema and Instances
Components of Database System
What is DBMS ?
Database Languages
Applications of DBMS
Introduction to Databases
Fundamentals of Data Modeling and Database Design
Database Normalization
Types of keys in database management system
Distributed Database
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Capacity Planning For Your Growing MongoDB ClusterMongoDB
Your MongoDB deployment is growing, but are you prepared for that growth? Capacity planning is an essential practice when deploying any database system. You need to understand your usage patterns and determine the appropriate hardware based on your application's needs. Scaling reads and scaling writes will require different types of resources. With the proper tools in place, you can understand your working set, gain visibility into when it's time to add resources or start sharding and avoid performance issues. In this session, you'll learn how to use MongoDB Management Service and other tools to identify patterns and predict growth, ensuring your success with MongoDB.
What is Data ?
What is Information?
Data Models, Schema and Instances
Components of Database System
What is DBMS ?
Database Languages
Applications of DBMS
Introduction to Databases
Fundamentals of Data Modeling and Database Design
Database Normalization
Types of keys in database management system
Distributed Database
Aggregating Data Using Group FunctionsSalman Memon
After completing this lesson, you should be able to
do the following:
Identify the available group functions
Describe the use of group functions
Group data using the GROUP BY clause
Include or exclude grouped rows by using the HAVING clause
http://phpexecutor.com
Introduction on aggregate impact testing machine pptAbhishek Sagar
Toughness is the property of a material to resist impact. Due to traffic loads, the road stones are subjected to the pounding action or impact and there is possibility of stones breaking into smaller pieces. The road stones should therefore be tough enough to resist fracture under impact. A test designed to evaluate the toughness of stones
Lesson: Concrete Technology - Building Materials
The quality of aggregate affect the durability and strength of concrete. Since about 3/4 of the volume of concrete is occupied by aggregate.
Pavement materials in Road Constructionsrinivas2036
Different pavement materials used in the road construction. Importance of soil, aggregate pavement materials. Tests on Soil for pavement construction. Tests on aggregate for pavement construction.
Requirements of soil and aggregates in pavement.
Recycle material used in road constructionpavan bathani
As the world population grows, so do the amount and type of waste being generated.Many of the waste produced today will remain in environment.The creation of non decaying waste material, combined with a growing consumer population, has resulted in a waste disposal crisis.
One solution to this crisis lies in recycling waste into useful products.
It is try to match society need for safe and economic disposal of waste material with highway industry need for better and more cost effective construction material.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Assessment of Cluster Tree Analysis based on Data Linkagesjournal ijrtem
Abstract: Details linkage is a procedure which almost adjoins two or more places of data (surveyed or proprietary) from different companies to generate a value chest of information which can be used for further analysis. This allows for the real application of the details. One-to-Many data linkage affiliates an enterprise from the first data set with a number of related companies from the other data places. Before performs concentrate on accomplishing one-to-one data linkages. So formerly a two level clustering shrub known as One-Class Clustering Tree (OCCT) with designed in Jaccard Likeness evaluate was suggested in which each flyer contains team instead of only one categorized sequence. OCCT's strategy to use Jaccard's similarity co-efficient increases time complexness significantly. So we recommend to substitute jaccard's similarity coefficient with Jaro wrinket similarity evaluate to acquire the team similarity related because it requires purchase into consideration using positional indices to calculate relevance compared with Jaccard's. An assessment of our suggested idea suffices as approval of an enhanced one-to-many data linkage system.
Index Terms: Maximum-Weighted Bipartite Matching, Ant Colony Optimization, Graph Partitioning Technique
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
www.irjes.com
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Novel Approach to Automatically Generate Feasible Assembly Sequenceishan kossambe
Today the assembly sequence for the items is regularly completed manually and its definition, typically, is extremely extravagant, not ensuring ideal arrangements. Gathering arrangement arranging utilizing a business framework regularly depends on a master assembly sequence organizer, and it is dominatingly done manually. The difficulties to consequently produce gathering arrangements utilizing CAD models lie as a part of smart thinking and investigation of the displayed assembly information. This work displays a programmed approach expected to characterize gathering sequences, based on the data containing the mates, obstruction and the volume information existing among the parts, which is acquired by the assembly CAD model of the item. This paper exhibits a framework that can examine and use assembly information accessible from a CAD model to produce gathering arrangements. The framework likewise considers client input as a kind of assembly obligation. The framework is equipped for creating a set of positioned attainable assembly arrangement plans for an administrator to assess. A matrix approach has been embraced to process the data held from a CAD model. Obstruction and volume studies are completed amid the formation of assembly sequence plans.
Hortizontal Aggregation in SQL for Data Mining Analysis to Prepare Data SetsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
PHP UK 2020 Tutorial: MySQL Indexes, Histograms And other ways To Speed Up Yo...Dave Stokes
Slow query? Add an index or two! But things are suddenly even slower! Indexes are great tools to speed data lookup but have overhead issues. Histograms don’t have that overhead but may not be suited. And how you lock rows also effects performance. So what do you do to speed up queries smartly?
DECISION TREE CLUSTERING: A COLUMNSTORES TUPLE RECONSTRUCTIONcscpconf
Column-Stores has gained market share due to promising physical storage alternative for analytical queries. However, for multi-attribute queries column-stores pays performance
penalties due to on-the-fly tuple reconstruction. This paper presents an adaptive approach for reducing tuple reconstruction time. Proposed approach exploits decision tree algorithm to
cluster attributes for each projection and also eliminates frequent database scanning.Experimentations with TPC-H data shows the effectiveness of proposed approach.
2. Using the Star Schema
The queries against a
star schema follow a
consistent pattern.
One or more facts
are typically
requested, along with
the dimensional
attributes that
provide the desired
context. The facts are
summarized as
appropriate, based on
the dimensions.
2
3. Aggregate tables
Aggregate tables improve data warehouse performance
by reducing the number of rows the RDBMS must access
when responding to a query
Base schema Aggregate schema
3
5. aggregate characteristic
The more highly summarized an aggregate table is, the
fewer queries it will be able to accelerate.
This means that choosing aggregates involves making careful
tradeoffs between the performance gain offered and the
number of queries that will benefit.
5
6. The Aggregate Navigator
To receive the performance benefit offered by an
aggregate schema, a query must be written to use the
aggregate.
aggregate navigator: A component of the data warehouse
infrastructure, the aggregate navigator assumes the task of
rewriting user queries to utilize aggregate tables.
6
7. Principles of Aggregation
An aggregate schema must always provide exactly the
same results as the base schema.
The attributes of each aggregate table must be a subset of
those from a base schema table.
The only exception to this rule is the surrogate key for an
aggregate dimension table.
7
9. Pre-Joined Aggregates
a pre-joined aggregate summarizes a fact across a set of
dimension values. But unlike the aggregate star schemas
the pre-joined aggregate places the results in a single
table.
By doing so, the pre-joined aggregate eliminates the need for
the RDBMS to perform a join operation at query time.
9
10. Derived Tables
alter the structure of the tables summarized or change
the scope of their content.
Types:
the merged fact table: combines facts from more than one fact
table at a common grain
the pivoted fact table: transforms a set of metrics in a single
row into multiple rows with a single metric, or vice versa.
the sliced fact table: contains a subset of the records of the
base fact table, usually in coordination with a specific
dimension attribute.
In all three cases, the derived fact tables are not expected
to serve as invisible stand-ins for the base schema.
10
11. Tables with New Facts
Semi-additive facts may not be added together across a
particular dimension; non-additive facts are never added
together. In these situations, you may choose to aggregate
by means other than summation.
11
12. Choosing Aggregates
One of the most vexing tasks in deploying dimensional
aggregates is choosing which aggregates to design and
deploy.
Your aim is to strike the correct balance between the
performance gain provided by aggregate schemas and their cost
in terms of resource requirements.
12
13. Choosing Aggregates
What Is a Potential Aggregate?
Identifying Potentially Useful Aggregates
Assessing the Value of Potential Aggregates
13
14. What Is a Potential Aggregate?
Aggregate Fact Tables: A Question of Grain
Aggregate Dimensions Must Conform
Pre-Joined Aggregates Have Grain Too
Enumerating Potential Aggregates
14
15. What Is a Potential Aggregate?
Express potential aggregates as fact table grain statements
Orders by day, salesperson and product
Orders by day, customer, and product
Orders by month, product, and salesperson
15
17. Identifying Potentially Useful Aggregates
Drawing on Initial Design
Design Decisions
Listening to Users
Where Subject Areas Meet
The Conformance Bus
Aggregates for Drilling Across
Query Patterns of an Existing System
Analyzing Reports for Potential Aggregates
Choosing Which Reports to Analyze
17
18. Identifying Potentially Useful Aggregates
Identify and document potential aggregates during schema
design, even if initial implementation will not include
aggregates. This information will be useful in the future.
Any decision to set the grain of a fact table at a finer level
reveals a potential aggregate.
Decisions about where to place groups of dimensional
attributes reveal potential levels of aggregation.
Discussion of hierarchies or drill paths point to potential
aggregates
User work products reveal potential aggregates. These may
include reports from operational systems, manually compiled
briefings, or spreadsheets. They will also be revealed by manual
processes and requirements not currently met.
18
19. Aggregates for Drilling Across
The process of combining
information from multiple fact
tables is called drilling across
Consult the conformance bus
to identify aggregates that will
be used in drill-across reports.
The lowest common
dimensionality between two fact
tables often suggests one or
more aggregates.
19
20. Analyzing Reports for Potential Aggregates
The detail rows
require order facts
by product and
month.
The summary rows
require order facts
by category and
month.
The grand total
requires order facts
by month.
20
22. Assessing the Value of Potential Aggregates
After identifying a pool of potential aggregates, the next
step is to sort through them and determine which ones
to build.
22
23. Assessing the Value of Potential Aggregates
Number of Aggregates
Presence of an Aggregate Navigator
Space Consumed by Aggregate Tables
How Many Rows Are Summarized
Examining the Number of Rows Summarized
The Cardinality Trap and Sparsity
Who Will Benefit from the Aggregate
23
24. Examining the Number of Rows
Summarized
A good starting rule of thumb is to identify aggregate fact
tables where each row summarizes an average of 20
rows.
The savings afforded by aggregates can be lopsided,
favoring a particular attribute value.
Remember that, like a base fact table, a dimensional
aggregate can be aggregated during a query. Aggregates
may be competing with other aggregates to offer
performance gains.
24
25. The Cardinality Trap and Sparsity
Cardinality:The number of distinct values taken on by a
given attribute
sparse:not all combinations of keys are present.
Don’t assume aggregate fact tables will exhibit the same
sparsity as the tables they summarize.
The higher the degree of summarization, the more dense the
aggregate fact table will be.
The best way to get an idea of the relative size of the
aggregate is to count the number of rows.
As before, count the distinct combination of keys and/or
summarized dimension attributes.
25
26. Who Will Benefit from the Aggregate
The first aggregates you add to your implementation are
those that offer benefits across the widest number of
user requirements. Aggregates that fall in the 20:1 range
of savings are compared with one another to identify
those that support the most common user requirements.
Start by selecting aggregates that provide solid
performance boosts for a wide number of common
queries. To this, add more powerful (but more narrowly
used) aggregates as space permits. Use the relative
importance of one aggregate over another in a tiebreaker
situation.
26
27. Bibliografía
Mastering Data Warehouse Aggregates.Solutions for Star
Schema Performance. Christopher Adamson.
27