Advanced Database Applications: Database Indexing and Data Mining CS562 -- Fall 2005

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Advanced Database ApplicationsDatabase Indexing
and Data MiningCS562 -- Fall 2005

• George Kollios
• Boston University

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• Prof. George Kollios
• Office MCS 288
• Office Hours Monday 230pm-400pm
• Thursday 1100am-1230pm
• Mailing List cs562
• Web http//www.cs.bu.edu/faculty/gkollios/ada05
• Book1 http//www.cs.bu.edu/faculty/gkollios/ada05
  /Book/

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History of Database Technology

• 1960s Data collection, database creation, IMS
  and network DBMS
• 1970s Relational data model, relational DBMS
  implementation
• 1980s RDBMS, advanced data models
  (extended-relational, OO, deductive, etc.) and
  application-oriented DBMS (spatial, scientific,
  engineering, etc.)
• 1990s2000s Data mining and data warehousing,
  multimedia databases, and Web databases

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Structure of a RDBMS
Modern Database Systems Extend these layers

• A DBMS is an OS for data!
• A typical RDBMS has a layered architecture.

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Index Methods for RDBMS

• Hashing Methods
• Linear Hashing, Extensible Hashing
• B-tree family
• B-trees and variations
• Both of them are one-dimensional

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Overview of the course

• Spatial Database Systems
• GIS, CAD/CAM, EOSDIS project NASA
• Manages points, lines and regions
• Temporal Database Systems
• Billing, medical records
• Spatio-temporal Databases
• Moving objects, changing regions, etc

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Overview of the course

• Multimedia databases
• A multimedia system can store and retrieve
  objects/documents with text, voice, images, video
  clips, etc
• Time series databases
• Stock market, ECG, trajectories, etc

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Multimedia databases

• Applications
• Digital libraries, entertainment, office
  automation
• Medical imaging digitized X-rays and MRI images
  (2 and 3-dimensional)
• Query by content (or QBE)
• Efficient
• Complete (no false dismissals)

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What is Data Mining?

• Data mining (knowledge discovery in databases)
  
• The efficient discovery of previously unknown,
  valid, potentially useful and understandable
  information or patterns from data in large
  databases
• Alternative names
• Knowledge discovery(mining) in databases (KDD),
  knowledge extraction, data/pattern analysis, data
  archeology, data dredging, etc.

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DM Applications

• Database analysis and decision support
• Market analysis target marketing, market basket
  analysis, market segmentation
• Fraud detection and management
• Biology and medicine
• Text mining (news group, email, documents) and
  Web analysis.

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Data Mining Confluence of Multiple Disciplines
Database Technology
Statistics
Data Mining
Machine Learning
Visualization
Information Science
Other Disciplines
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Overview of terms

• Data a set of facts (items) D, stored in a
  database
• Pattern an expression E in a language L, that
  describes a subset of facts
• Attribute a field in an item i in D.
• Interestingness a function ID,L that maps an
  expression to a measure space M

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The Data Mining Task

• For a given dataset D, language of facts L,
  interestingness function ID,L and threshold c,
  find the expressions E that
• ID,L(E) gt c efficiently.

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How Data Mining is used

• Identify the problem
• Use data mining techniques to transform the data
  into information
• Act on the information
• Measure the results

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DM Functionalities

• Concept description
• Generalize, summarize, and contrast data
  characteristics, e.g., dry vs. wet regions
• Association (correlation and causality)
• Multi-dimensional vs. single-dimensional
  association
• age(X, 20..29) income(X, 20..29K) à buys(X,
  PC) support 2, confidence 60
• contains(T, computer) à contains(x, software)
  1, 75

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DM Functionalities

• Cluster analysis
• Class label is unknown Group data to form new
  classes, e.g., cluster houses to find
  distribution patterns
• Clustering based on the principle maximizing the
  intra-class similarity and minimizing the
  interclass similarity

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DM Functionalities

• Classification and Prediction
• Finding models (functions) that describe and
  distinguish classes or concepts for future
  prediction
• E.g., classify countries based on climate, or
  classify cars based on gas mileage
• Presentation decision-tree, classification rule,
  neural network
• Prediction Predict some unknown or missing
  numerical values