This document discusses data and information. It defines data as raw facts and figures that have not been processed, while information is processed data that is more meaningful. Different types of data are described such as numeric, alphabetic, alphanumeric, image, audio and video data. File processing systems are also discussed, noting their disadvantages including data redundancy, inconsistency, isolation, integrity problems, program dependency, lack of atomicity, security issues, and difficulty of program maintenance.
Whenever you make a list of anything – list of groceries to buy, books to borrow from the library, list of classmates, list of relatives or friends, list of phone numbers and so o – you are actually creating a database.
An example of a business manual database may consist of written records on a paper and stored in a filing cabinet. The documents usually organized in chronological order, alphabetical order and so on, for easier access, retrieval and use.
Computer database are those data or information stored in the computer. To arrange and organize records, computer databases rely on database software
Microsoft Access is an example of database software.
Entity Integrity Constraint:
It states that in a relation no attribute of a primary key (K) can have a null value. If a K consists of a single attribute, this constraint obviously applies on this attribute, so it cannot have the Null value. However, if a K consists of multiple attributes, then none of the attributes of this K can have the Null value in any of the instances.
Referential Integrity Constraint :
This constraint is applied to foreign keys. Foreign key is an attribute or attribute combination of a relation that is the primary key of another relation. This constraint states that if a foreign key exists in a relation, either the foreign key value must match the primary key value of some tuple in its home relation or the foreign key value must be completely null.
Whenever you make a list of anything – list of groceries to buy, books to borrow from the library, list of classmates, list of relatives or friends, list of phone numbers and so o – you are actually creating a database.
An example of a business manual database may consist of written records on a paper and stored in a filing cabinet. The documents usually organized in chronological order, alphabetical order and so on, for easier access, retrieval and use.
Computer database are those data or information stored in the computer. To arrange and organize records, computer databases rely on database software
Microsoft Access is an example of database software.
Entity Integrity Constraint:
It states that in a relation no attribute of a primary key (K) can have a null value. If a K consists of a single attribute, this constraint obviously applies on this attribute, so it cannot have the Null value. However, if a K consists of multiple attributes, then none of the attributes of this K can have the Null value in any of the instances.
Referential Integrity Constraint :
This constraint is applied to foreign keys. Foreign key is an attribute or attribute combination of a relation that is the primary key of another relation. This constraint states that if a foreign key exists in a relation, either the foreign key value must match the primary key value of some tuple in its home relation or the foreign key value must be completely null.
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Complete DBMS notes..with special attention to SQL commands and advanced SQL commands, Transaction management. The below post is notes prepared by me by studying the book "Database Systems Design, Implementation and Management" by Peter Rob and Carlos Coronel
Content, examples and diagrams are taken from that book.
1) 500 words or less, differentiate a Databases Management Systems.pdfmanjan6
1) 500 words or less, differentiate : a Databases Management Systems b. Data Warehousing c.
Business Intelligence d. Data Mining
Solution
DataBase Management:
A Data Base Management System is a technology to store our data with in a System.and also to
update modify and do certain manuplation to our data and also proivde security to our
information.
before we dont have these data base management system.if we want to retrive data from system
or if we want to perfome any manuplations we need to know the file type of particular file
system and we write a program.
and that particular program is not used for other file types.
so it causes data dependenciey.
in order to overcome these problems a database management system.lies between os and file
sytem.which retrives data and gives to user if we dirctly qury to it.
ealrier different data models have been developed:
1.Hyrarichal model
2.Network model
3.Relational model
Hyraichal model:in hirearical model it follows tree structure.
so we need to know trees to use it
Network model:network model is based on graphs.we need know graps on our finger tips.
Relational model
everything is in the form of tables ..
DataWarehouse:
DataWarehouse is a collection of data from different sources and integrated..for example
different data from different relational databases are integrated and used for analysis and making
decisons on it.
A data warehouse provides us data to be view in multidimensional way. along with general view
of data it also provides an olap tool. this toool is used for analysis of data in multidimension
manner.
datawarehouse is used in many areas:
Tuning productions statagies.
customer analysis
operational analysis
Data Mining:
Data Mining is extracting useful or required information from large data or large data sets .for
example a large data set of employees is taken and some algoithms applied on it to retrive data
from it.
the best part of it is not only it retrives data .it performs various operation
all the operations comes under kdd ..
kdd stands for knowledge discovry from data.
various steps are:
data cleaning.
integration
transformation
data maining
pattern evolutionn
data presenting
among them data mining is the import task in that.
data cleaning and integration and transformation comes as pre data mining steps and rest all
comes under post data mining steps.
Bussiness Intelligent:
Bussiness intelligent is a tool is used for analysis of data or information to provide benifits to
organizations.
busiiness intelligent consists of many tools for analysis of data like data with in organization and
outside the organizations.
this tools is used and provides many benifits like to make good decision making and
understanding and various other factors..
Computer Science 12th Topic- introduction to syllabus.pdfiqbalaabi01
Computer science 12th class,
It is an introduction to the sallybus of 12th class computer science. Also contains the notes of first chapter: Data basics
INTRODUCTION TO Database Management System (DBMS)Prof Ansari
shared collection of logically related data, designed to meet the information needs of multiple users in an organization. The term database is often erroneously referred to as a synonym for a “database management system DBMS)”. They are not equivalent and it will be explained in the next section.
Technical Documentation 101 for Data Engineers.pdfShristi Shrestha
Introduction to technical documentation for data engineers. This presentation contains details about metadata management software, documentation good practices, data documentation principles, data pipeline documentation practices and data pipeline documentation standards like CRISP-DM.
This is the Briefly introduction to Database system with the help of comparison with the previous technology file Base System. In this lecture reader will be able to understand the fundamental concept of the Database System and know about the all primer Concept of the Database system. This lecture will helpful for those, who are lack or less knowledge about the Database System.
Normalisation in Database management System (DBMS)Prof Ansari
Normalization is a technique to organize the contents of the table for transactional database and data warehouse.
First Normal Form :
Seeing the data in the example in the book or assuming otherwise that all attributes contain the atomic value, we find out the table is in the 1NF.
Second Normal Form :
Seeing the FDs, we find out that the K for the table is a composite one comprising of empId, projName. We did not include the determinant of fourth FD, that is, the empDept, in the PK because empDept is dependent on empId and empID is included in our proposed PK. However, with this PK (empID, projName) we have got partial dependencies in the table through FDs 1 and 3 where we see that some attributes are being determined by subset of our K which is the violation of the requirement for the 2NF. So we split our table based on the FDs 1 and 3 as follows :
HP Data Protector is the first data backup solution in the entire industry to provide a uniform deduplication solution from the source to the target, and protect mission-critical data in virtual and physical environments. More than 45,000 customers worldwide trust their HP Data Protector solution to protect their most valuable asset—information.
This white paper provides recommendations for the implementation of a backup and recovery strategy for HP Data
Protector that allows you to optimize the way you use Data Protector in each IT environment. The guide concludes with
references to other sources of information related to Data Protector and a glossary of important terms.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Contents
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Definition:
A collection of raw facts andfigures is called data. Theword Rawmeans
that thefacts have not yet been processedto get their exact meaning .Datais
collected from different sources. It is collected for different purposes. Datamay
consist of numbers, characters, symbols or pictures etc.
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1. When students get admission in college or universities, they have tofill out
an admission form. Theform consists of raw facts about thestudents.
Theseraw facts are student’s name, fathername, address etc.
Thepurpose of collecting datais tomaintain therecords of students during
study period in college or university.
2. During census, government of Pakistan collects the dataof all citizens.
Government stores this datapermanently touse it for different purposes
at different times.
3. Different organizations conduct surveys toknow theopinion of the people
about their product. In these surveys, people express their ideas and
opinions about different issues. Theseideas and opinion of thepeople are
3. storedad data. Theorganizations usethis data forthe improvement of
their products.
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Datamay be of thefollowing types:
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Numeric dataconsists of numeric digits form 0 to9 like10, 245or 5.
Thenumeric type of data may either be positive or negative.
AAAlllppphhhaaabbbeeetttiiiccc DDDaaatttaaa:::
Alphabetic data consists of alphabetic letters from A to Z, a toz and
blank space e.g. “IT series”, “computer”, and“Islam”etc.
AAALLLPPPHHHAAANNNUUUMMMEEERRRIIICCC DDDAAATTTAAA:::
Alphanumeric data consists of numericdigits (0 to9), letters
(A to Z) andall special characters like +, %and @ etc. like “87%”, “$300” and
“H#17”.
IIImmmaaagggeee DDDaaatttaaa:::
This type of Dataincludes charts, graphs, pictures, and drawings. This
form of data is more comprehensive. It can be transmittedas a set of bits.
AAAuuudddiiiooo DDDaaatttaaa:::
Soundis a representation of audio. Audiodata includes music, speech
or any typeof sound.
VVViiidddeeeooo DDDaaatttaaa:::
Videois set of full-motion images played at a high speed. Videois used
to display action and movements.
4. IIInnnfffooorrrmmmaaatttiiiooonnn
Theprocessed data is called information. Information is an organized and
processed from of data. It is moremeaningful than data andis usedfor making
decisions. Datais used as input for theprocessing and information is theoutput
of this processing. This information can be used again in some otherprocessing
and will be considered as datain that information.
ForExample, themarks of the student in different subject is thedata. To
calculate totalmarks, themarks of different subjects are usedas data and a total
mark is the information. Now, tocalculate theaverage marks of the student, this
information will be processed again. In this processing, theinformation is usedas
data andaverage marks will be theinformation.
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Some examples of information are as follows:
111... In colleges and universities, theraw facts about students arestoredon
admission forms. If wewant tofind out a list of all students wholivein
Faisalabad, wewill apply someprocessing and will be called information.
222... Thedata storedin census is usedtogenerate different type of information.
Forexample, government can useit to findtotal numberof graduates or
literacy rate in country etc. This information can be obtained by processing
storeddata. Government can usethis information totakeimportant
decisions toimprove literacy rate.
333... An organization can usetheopinion of thepeople as data andprocess it to
generate information of its interest.
Forexample, it can know that how manypeople of the country are satisfied
with thequality of its product andhow manyare unsatisfied. The
organization can usethis information for theimprovement of its product.
5. MMMeeetttaaadddaaatttaaa
Metadatacan be defined as dataabout data. It is usedtodescribe the properties
and characteristics of someother data. Metadatadescribes thesize, format and
other characteristics of data. It alsoincludes the rules andconstraints about data.
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Traditional or simple file processing is the first computer-basedmethodto
handle business application. In the past, manyorganization storeddata in files on
tape or disk. The datawas managedusing file-processing system. In atypical file
processing system, each department in an organization has its own set of files.
Thefiles are designed especially for their own applications. Therecords in one
file are not related totherecords in any other file.
Business organizations have usedfile-processing system formanyyears. But this
system has manydisadvantages.
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sssyyysssttteeemmm:::
someimportant disadvantages of file processing system
are as follows:
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In file processing system, thesamedatamay be duplicated in several files.
Supposethere are twofiles “students” and“Library”. Thefile “students”contains
theRoll No, name address andtelephone number andother details of all
students in acollege. The file “Library” contains theRoll No and nameof those
students whoget abook from library along with theinformation about the
rented books. Thedata of onestudent appears in twofiles. This is known as data
redundancy causes higher storage.
Thesituation can also result in datainconsistency. Inconsistency means that two
6. files may contain different data of the samestudent.
Forexample, if theaddress of a student is changed, it must bechanged in both
files. There is a possibility that it is changed in the“students” fileandnot from
“Library” file. Thedata become inconsistent in this situation.
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Thedata in file processing system is storedin various files. It becomes very
difficult towrite new application programs toretrieve theappropriate data.
Supposethat student’s emails are storedin “Students” fileand fee information is
storedin “Fee” file. The datafrom both files is required tosend an email message
to inform a student that thedataforfee payment is over. In file processing
system, it is difficult togenerate such typeof list from multiple files.
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Integrity data reliability and accuracy of data. Thestoreddata must
satisfycertain types of consistency constrains.
Forexample, Roll No and Marks of students shouldbenumericvalue. It is difficult
to apply theseconstrains on files in file processing system.
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program datadependency is a relationship between datain files
and program required toupdateand maintain thefiles. Application programs are
developed according toa particular file format in file processing system. If the
format of file is changed, theapplication program also needs tobe changed
accordingly.
ForExample, if there is change in thelength of postalcode, it requires changed in
theprogram. Thechanges may be costly toimplement.
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An operation on data may consist of different steps. A collection of
all steps required torequire completing a process is known as transaction. The
atomicity means that either one transaction shouldtake place as a whole orit
shouldnot take place at all. Suppose a userwants totransfermoney from
7. account A toaccount B. this process consists of twosteps:
Deduct themoney from account A
Add themoney to account B
Suppose that thesystem fails when thecomputerhas performed thefirst step.
It means that theamount has been deducted from account A but has not been
added to account B. this situation can make datainconsistent. File processing
system does not provide thefacility toensureatomicity of data.
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File processing system does not provide adequate security on data. In
somesituations, it is required toprovide different types of access to datafor
different users.
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The programs developed in file processing system are difficult to
maintain, mist of the budget may be spent on maintenance, it makes it difficult
to develop new applications.
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