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Will Data Mining Change the Functions of DBMS?

Will Data Mining Change the Functions of DBMS?. Jiawei Han DAIS (Data And Information Systems) Lab University of Illinois at Urbana-Champaign. Will DM Be Integrated with DB Functions?. DM: Already a functional component of DBMS Microsoft/SQLServer: Analysis Manager

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Will Data Mining Change the Functions of DBMS?

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  1. Will Data Mining Change the Functions of DBMS? Jiawei Han DAIS (Data And Information Systems) Lab University of Illinois at Urbana-Champaign

  2. Will DM Be Integrated with DB Functions? • DM: Already a functional component of DBMS • Microsoft/SQLServer: Analysis Manager • IBM/DB2 & IntelligentMiner • Oracle: Data Mining Package • But will DM be “intruding” into DBMS, i.e., be integrated with essential DBMS functions? • Indexing • Data integration • Data cleaning • Query processing

  3. Indexing by Data Mining • Indexing graphs? ─ # of subgraphs: exponential! • Chemical Informatics/bioinformatics … • Discriminative frequent graph patterns (SIGMOD’04) • Indexing subsequences? • Shopping sequence, DNA/protein sequence (SDM’05) • When is discriminative frequent pattern indexing useful? • Complex objects, big (object) queries Sample database (a) (b) (c) Query graph

  4. Data Cleaning by Data Mining • Load messy data into a structured database? • Inconsistent data: age = “1946”? • Field mis-alignments • Glitches of data: completely messed up inputs • Missing/un-matching delimiters: XML, HTML data • Big field: BLOB, CLOB, multimedia and text • Data mining • Data cleaning by distribution/outlier analysis • Dependency/correlation analysis • Schema-directed or schema “discovery”

  5. Data Integration by Data Mining • Linking and mining cross-over multiple data relations • Cross-mine (Classification across multiple data relations: ICDE’04) • Search across heterogeneous databases • Object identification/merge, reference reconciliation (Alon’s group) • Mining across heterogeneous DBs • Personalizing data from heterogeneous sources

  6. Query Processing by Data Mining • Query plan refinement based on query execution history • Better query planning by investigating additional data statistics • Current optimizer: key/foreign key, cardinality, # distinct values • Additional information: • Strong dependency/correlation • Histogram, dense vs. sparse regions, etc.

  7. Conclusions • DBers have been “invading” into DM and made great contributions • It is time to consider that DM may invade DBMS to enhance its functionality • General philosophy • Invisible data mining • Google is doing this for page ranking successfully • Can we do it to enhance DBMS? • You can do better if you know your data better!

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