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this will cover the whole course about advanced querying of DBMS TU 4th semester

this will cover the whole course about advanced querying of DBMS TU 4th semester

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  • 2. INTRODUCTION OF DATABASE First used by William Inmon in the early 1980s. A data warehouse is a subject oriented,integrated,time variant, and volatile collection of data in support of management decision making process.
  • 3.  Supports Decison Supports System(DDS). It is more than just data,it is also the processes involved in getting that data from source to table and in getting the data from table to analysts. It is the data and the process managers(load/query/warehouse) that make information available enabling people to make informed decisions.
  • 5. STRUCTURE OF DATA WAREHOUSETIME –VARIANT• Contain information collected over time.• Decisions are made by analyzing past trends in companies performance.NON VOLATILE• Data never updated but used only for queries.• Change,update,delete,etc is done to only operational data.• i.e it is filled only with the historical data.
  • 6. INTEGRATED• Contains various types of data anddatabase integrated to make it consistent.SUBJECT-ORIETNED• Provides simple and concise collection of data• Is built around all the existing applications of operational data.
  • 7. COMPONENTS OF DATA WAREHOUSE• Data sources• Data transformations• Reporting• Metadata• Operations• Other components
  • 8. • Data Sources Data Sources refers to any electronic repository of information where data is passed from these systems to the data ware house either on a transaction-by transaction basis for real time data warehouses or on a regular cycle.• Data Transformation The Data Transformation layer receives data from the data sources,cleans and standarizes it, and loads it in the data repository.•. Data Warehouse The data warehouse is a relational database organized to hold information in a structure that best supports reporting and analysis.
  • 9. • Reporting The data in the data ware house must be available to all the users if the data warehouse is to be useful.• Metadata Metadata or “DATA ABOUT DATA” is used to inform users of the data warehouse about its status ans the information held within the data warehouse.• Operations: Data warehouse operations comprises of theprocesses of loading, manipulating and extracting datafrom the data warehouse. Operations also covers usermanagement, security, capacity management and relatedfunctions.
  • 10. In addition, the following components alsoexist in some data warehouses:1. Dependent Data Marts: A dependent data mart is a physical database (either on the same hardware as the data warehouse or on a separate hardware platform) that receives all its information from the data warehouse.2. Logical Data Marts: A logical data mart is a filtered view of the main data warehouse but does not physically exist as a separate data copy.3. Operational Data Store: An ODS is an integrated database of operational data. Its sources include legacy systems and it contains current or near term data.
  • 11. APPLICATIONS OF DATA WAREHOUSESafety• under the control of data ware houseusers so information can be stored safely for past.Fast retrieval of data•Separate from operational systems,so itprovide retrieval of data without slowind downoperation.
  • 12. Facilitate decision support system Facilitate DSS such as trend reports,exception reports, and reports that show actual performance versus goals.Common data model Provide common data model for all data of interest regardless of the data’s source .Easy to report and analyze information such as sales invoices,order receipts,general ledger etc
  • 13. Analytical processing multidimensional analysis of data warehouse data supports basic OLAP operations, slice-dice, drilling, pivotingInformation processing supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphsData mining knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools
  • 14. Slide 15Chapter 10 Decision Support SystemsWell, Sort-of
  • 15. Slide 16 Chapter 10 Decision Support Systems How do we define a ‘decision’? A position or opinion or judgment reached after consideration The act of making up your mind about something The commitment to irrevocably allocate valuable resources. A decision is a commitment to act. Action is therefore the irrevocable allocation of valuable resources. A determination of future action The main function of a manager
  • 16. Slide 17 Chapter 10 Decision Support Systems What Types of decisions are there? Structured Decisions: For ax + bx + c = 0, the value of x is given by: 2 Situations where the procedures to follow when a decision is needed can be specified in advance Semi-structured Decisions: Decision procedures that can be pre- specified, but not enough to lead to a definite recommended decision Unstructured Decisions: Decision situations where it is not possible to specify in advance most of the decision procedures to follow
  • 17. Slide 18 Chapter 10 Decision Support Systems What is a decision support system? Computer-based information systems that supports business or organizational decision making activities. DSSs serve the management, operations and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. DSS include knowledge-based systems. A properly designed DSS is and interactive software –based system intended to help decision makers compiled useful information from a combination of raw data documents or personal knowledge or business model to identify and solve problem and make decision.
  • 18. Slide 19 Chapter 10 Decision Support Systems What doesn’t a decision support system do? Provide the solution (it is only tool) Be used over and over again (It was designed for unique decision making) Always use the same analytical models and tools (The decision maker chooses the models and tools based on the problem at hand)
  • 19. Slide 20 Chapter 10 Decision Support Systems What types of DSS analysis are there? What-if Analysis: User make changes to variables, or relationships among variables, and observe the resulting changes Sensitivity Analysis: The value of only one variable is changed repeatedly and the resulting changes in other variables are observed Goal-Seeking: The value of only one variable is changed repeatedly and the resulting changes in other variables are observed Optimization: Find the optimum value for target variables given certain constraints
  • 20. Slide 21Chapter 10 Decision Support Systems What are the components of a DSS?
  • 21. Slide 22 Chapter 10 Decision Support SystemsDatabase ConceptThe database concept has evolved since the 1960s to ease increasingdifficulties in designing, building, and maintaining complex informationsystems (typically with many concurrent end-users, and with a large amountof diverse data).Database is a collection of interrelated data that are organized so that it’scontents can be easily managed accessed and updated.A database contains a collection of related items or facts arranged in aspecific structured. The simple example of non computerized database is atelephone directory.
  • 22. Slide 23 Chapter 10 Decision Support SystemsModel Management SystemA model management subsystem contains completed modelsand the building blocks necessary to develop DSS applications.This includes standard software with financial, statistical,management science, or other quantitative models.An example is Excel, with its many mathematical and statisticalfunctions.
  • 23. Slide 24 Chapter 10 Decision Support SystemsThe User InterfaceThe term user interface covers all aspects of the communicationsbetween a user and the DSS.Some DSS experts feel that the user interface is the mostimportant DSS component because much of thepower, flexibility, and ease of use of the DSS are derived from thiscomponent.For example, the ease of use of the interface in the Guinness DSSenables, and encourages, managers and sales people to use thesystem.
  • 24. Slide 25 Chapter 10 Decision Support SystemsCharacteristics of DSS1. Facilitation. DSS facilitate and support specific decision-making activities and/or decision processes.2. Interaction. DSS are computer-based systems designed forinteractive use by decision makers or staff users who control thesequence of interaction and the operations performed.3. Ancillary. DSS can support decision makers at any level in anorganization. They are NOT intended to replace decision makers.
  • 25. Slide 26 Chapter 10 Decision Support Systems4. Repeated Use. DSS are intended for repeated use. A specific DSS may be usedroutinely or used as needed for ad hoc decision support tasks.5. Task-oriented. DSS provide specific capabilities that support one or more tasksrelated to decision-making, including: intelligence and data analysis;identification and design of alternatives; choice among alternatives; and decisionimplementation.6. Identifiable. DSS may be independent systems that collect or replicate datafrom other information systems OR subsystems of a larger, more integratedinformation system.7. Decision Impact. DSS are intended to improve the accuracy, timeliness, qualityand overall effectiveness of a specific decision or a set of related decisions.
  • 26. Slide 27 Chapter 10 Decision Support SystemsFunctions of DSS1. Information Retrieval:Information retrieval in DSS environment refers to the act ofextracting information from a database for the purpose of makingdecisions.2. Data Reconfiguration:Often managers using a DSS want information in a form otherthat in which the data are logically represented within thecomputer system.a) Sortingb) Joining
  • 27. Slide 28 Chapter 10 Decision Support Systems3. Calculator activities:Calculator activities refer to the set of tasks that normally can bedone with a calculator.a) Functionsb) Analysisc) Statistical Toold) Optimizing Toolse) What-if analysis(Sensitivity Analysis)
  • 28. OLAP (online analytical processing)OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view. OLAP can be used for data mining or the discovery of previously undiscerned relationships between data items.An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis
  • 29. Usage of OLAP
  • 30. OLTP (online transaction process)• OLTP (online transaction process) System deals with operational data. Operational data are those data involved in the operation of a particular system.• Example: In a banking System, you withdraw amount through an ATM. Then account Number, ATM PIN Number, Amount you are withdrawing, Balance amount in account etc. are operational data elements. Operational Data Operational data are local relevance Frequent Updates Normalized Tables Point Query• Examples for OLTP Queries:• What is the Salary of Mr.John?• What is the address and email id of the person who is the head of maths department?
  • 31. Compression between OLTP and OLAP
  • 32. Data mining• Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), is a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets. • It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems• The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use • The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies
  • 33. Application of data miningData understandingData preparationModelingEvaluationDeployment
  • 34. Data warehouse • A data warehouse (DW or DWH) is a database used for reporting and data analysis. • The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational for additional operations before they are used in the DW for reporting• A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. The integrated data source systems may be considered to be a part of a distributed operational data store layer
  • 35. Application of warehouse Develop Design update Developstaging area