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 introduction to data warehousing and mining
 

introduction to data warehousing and mining

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data warehousing and mining introduction class from kl university

data warehousing and mining introduction class from kl university

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     introduction to data warehousing and mining introduction to data warehousing and mining Presentation Transcript

    • DATAWAREHOUSING AND MINING BY G.RAJESH CHANDRA
    • EVOLUTION OF DATABASE TECHNOLOGY  1960s (Primitive File Processing)   1970s to early 1980s (DBMS)   Data collection, database creation, IMS and network DBMS Relational data model, relational DBMS implementation ,SQL, OLTP,User Interfaces.etc 1980s: to Present (Advanced Data Bases)    RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: (Advanced Data Analysis)   Data mining, data warehousing, multimedia databases, and Web databases 2000s  Stream data management and mining  Data mining and its applications
    • WHY MINE DATA? COMMERCIAL VIEWPOINT  Lots of data is being collected and warehoused     Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Competitive Pressure is Strong  Provide better, customized services for an edge (e.g. in Customer Relationship Management)
    • WHAT IS DATA MINING…..?  • Data mining (sometimes called data Discovery or Knowledge Discovery Data) is the process of analyzing data from different perspectives and summarizing it into useful information. Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data
    • WHY MINE DATA? SCIENTIFIC VIEWPOINT  Data collected and stored at enormous speeds (GB/hour)       remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists   in classifying and segmenting data in Hypothesis Formation
    • EXAMPLES: WHAT IS (NOT) DATA MINING?  What is not Data  What is Data Mining? Mining? – Look up phone – Certain names are more number in phone directory prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) – Query a Web – Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) search engine for information about ―Amazon‖
    • DATA MINING IS ALSO CALLED AS..? • • Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Real Time Example Gold Mining
    • DATA WARE HOUSE = COLLECTION OF DATA BASES
    • WE HAVE TO USE DIFFERENT METHODS
    • RAW DATA =DATA BASES + NOISE DATA
    • DATA SELECTION AND TRANSFORMATION
    • DATA CLEANING AND INTEGRATION
    • DATA MINING
    • PATTERN EVALUATION
    • KNOWLEDGE REPRASENTATION
    • KNOWLEDGE REPRASENTATION
    • December 26, 2013 KNOWLEDGE DISCOVERY (KDD) PROCESS  Data mining—core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases Selection