Information Mediation: Integrating Information from Multiple Information

Sources

Naveen Ashish

Amit P. Sheth

Department of Computer Science and

Large Scale Distributed Information Systems Lab

University of Georgia, Athens

What is an Information Agent/Mediator ?

A software system that provides integrated and structured query access to multiple distributed information sources

Sources may be databases of various kinds or Web sources Sources are autonomously created and heterogeneous Accessible via a network Mediator provides the illusion of a single information

source

Information Agents aka Mediators

Example: Restaurant and Theatre Info on the Web

AriadneMediator

Map Servers

Geocoders

Movies

Zagat Health Ratings

Why the Interest in Building Such Systems ?

MEDIATOR

Oracle

Legacy SystemIBM DB2

Sybase

Object-Oriented DB

Mediators on the Web

MEDIATOR

Wrapper

DB1DB2

Organization of Remainder of Talk

Introduction– Information Agents, System Architecture

Research Issues– Information Modeling– Query Planning– Semi-automatic Wrapper Generation– Performance Optimization by Materialization– Resolving Inconsistencies

Industry Products for Data Extraction and Integration Start-up Ventures

Representative Systems (Research Projects)

SIMS/Ariadne University of Southern California/ISI

TSIMMIS Stanford

Information Manifold AT&T Research

Garlic IBM Almaden

Tukwila University of Washington

InfoSleuth MCC

DISCO University of Maryland/INRIA

HERMES University of Maryland

InfoMaster Stanford

InfoQuilt University of Georgia

Information Modeling

Multiple, heterogeneous, autonomously created information sources

Users sees an integrated (global) view– Queries a “mediated schema”

A uniform model for all sources– Must be (at least) expressive enough to model the most

complex information source Each source provides a set of relations or classes

– Translation (model) is done by wrapper at each source Integration

– Global as view, Local as view

Name

FODORS Phone # Reviews

ZAGATName

AddressTelephone

RESTAURANT

ZAGAT FODORS

Global as View

DOH RatingsName

Rating

For each relation (class) in mediated schema we specify how to obtain its tuples from the sources

Name

Phonenumber

GEOCODER

Address Lat Lon

Heterogeneity Resolution

Sources may use different models– OO, Relational, Legacy, …..– May be Web sources– Wrapper “exports” contents in a uniform model

Structural and schematic differences

(name, address) (name, street, city, state, zip)

Semantic

(name, phonenumber) (name, telephone)

Global as View: Models

KR based models (SIMS, Ariadne, ….)– LOOM, CLASSIC

OO, based on ODMG (DISCO, Garlic …)

interface Restaurant {attribute string name;attribute string address;attribute string cuisine;attribute string review;

}

extent restaurant 0 of Restaurant wrapper w0 repository r0map ((zagts0=restaurant0) (name=n) (address=a)(cuisine=c))

Local as View

For every information source S describe it in terms of relations in the mediated schema

v1(name,address,cuisine,rating) :- Restaurant(name,address, cuisine,rating) ^ city = “Santa Monica”

v2(name, foodrating) :- Restaurant(name,address,cuisine,rating)

….

Query Planning and Optimization

Mediator must generate an information gathering plan Constraints on execution

– Binding patterns .... Optimization of query plans Current areas of work

– Optimization– Approximate answers (incomplete sources)– Query planning for other sources such as simulations,

computer programs etc. – Query execution engines

Query Plans and Plan Quality

Low-Quality Plan

High-Quality Plan

Accessing Sources via WrappersSELECT address, telFROM RestaurantWHERE cuisine = “chinese”

Chinois, 2720 Main St, 310-777-9876Peking Star, 1 Broad St, 213-999-7676.....

Semi-Automatic Wrapper Generation

Need wrappers for several sites– Building wrappers by hand is tedious and time consuming

Approaches to automating the process– Exploit format information (structure, HTML etc. )– Template based approaches– Machine learning techniques

XML

<name> Peking Star </name><address> 1 Broad Street, Los Angeles </address><phone>31-822-1511 </phone>

Wrappers .... Work in Progress

Database wrappers Variety of techniques for Web wrappers “Upmarking”

– To XML Building “Web-bases” Other Artificial Intelligence techniques

– Natural Language Processing– IR– Classifiers

Performance Issue

Query processing time is typically very high Despite the mediator generating efficient query plans Cost of fetching data and pages from remote sources dominates

– Have to typically fetch a large number of Web pages– The Web sources are not designed for database like query

access– The Web sources can be slow

Further improve performance by materializing data at the mediator side.

Store and Materialize Data Locally

MEDIATOR

Wrapped Web Source (SLOW)

Materialized Data (FAST)

Selective Materialization

Why not simply materialize all the data in all the Web sources being integrated and have a really fast mediator ??– Will not scale, amount of space needed may be too much– Web sources can get updated

Cost of keeping data consistent can get prohibitive– We are building a mediator, not a data warehouse !

Approach then is to selectively materialize data How do we automatically identify the portion of data most

useful to materialize ?

Selecting Data to Materialize

Distribution of User Queries(Identify frequently accessed classes)

Distribution of User Queries(Identify frequently accessed classes)

Structure of Sources(Prefetch data to speed up expensive queries)

Structure of Sources(Prefetch data to speed up expensive queries)

Updates(Have to consider maintenance cost)

Updates(Have to consider maintenance cost)

Classes ofData to Materialize

SELECTING CLASSES

Inconsistency Resolution

Same object in different formats

“United States” and “US”

“Red Lobster” and “The Red Lobster”

“John Smith”, “Smith, J.” , “J. Smith”, “Dr. John Smith” ... Has appeared in other database and IR contexts Solutions

– Mapping tables For finite domains (such as cities, countries, companies …) Simply maintain an enumerated list of possible formats for each

object (New York, N.Y., NYC, New York City, Big Apple)

Mapping Functions

Mapping functions– When domain is not finite (person names)– Domain specific mapping transformations

Stemming common words (Inc., Corp., The etc.) Matching full word and abbreviation Match 2 formats with a score

Current work– Learning mapping functions from example matches– IR based approaches– Building “metabases”

Mediator Prototypes and Software Software and tools from mediator research projects What may be available.

– Mediator kernels (integration engines)– Data modeling tools, Description Logic systems– Wrapper and extractor toolkits and software – Plenty of papers !

Ariadne, USC/ISI, http://www.isi.edu/ariadnehttp://www.isi.edu/ariadne TSIMMIS, Stanford, http://www-db.stanford.edu/tsimmis/http://www-db.stanford.edu/tsimmis/ MIX, UCSD, http://feast.ucsd.edu/Projects/MIX/http://feast.ucsd.edu/Projects/MIX/ InfoSleuth, MCC, http://www.mcc.com/projects/infosleuth/ http://www.mcc.com/projects/infosleuth/ DISCO, U Maryland, http://www.umiacs.umd.edu/labs/CLIP/im.htmlhttp://www.umiacs.umd.edu/labs/CLIP/im.html Garlic, IBM Almaden, http://www.almaden.ibm.com/cs/garlic.htmlhttp://www.almaden.ibm.com/cs/garlic.html Tukwila, U Washington, http://data.cs.washington.edu/integration/tukwila/http://data.cs.washington.edu/integration/tukwila/

Applications of Mediators

Heterogeneous and Distributed Database Integration– Legacy systems integration

Web Sources Integration Data Integration for E-commerce

– Integrating product catalogs, multiple vendors Data Warehousing

– For populating data warehouses Bioinformatics Information Management Environments

Digital Libraries Healthcare Information Systems

Industry Products (IBM DB2 DataJoiner)

IBM DB2 DataJoiner http://www-4.ibm.com/software/data/datajoiner/http://www-4.ibm.com/software/data/datajoiner/ Enterprise data integration middleware DataJoiner functionality now incorporated in IBM DB2 UDB http://www-4.ibm.com/software/data/db2/udb/about.htmlhttp://www-4.ibm.com/software/data/db2/udb/about.html Native support for popular relational data sources

– DB2, Informix, SQL Server, Sybase, Teradata and others– Supports non relational data sources– Support for Web data– Available on variety of platforms and OS

Start-up ventures: Junglee Corp

Website: www.amazon.com (Acquired) Researcher Founders: Rajaraman, Gupta, Harinarayanan, Mathur Products and Services:

– Tools for data extraction and integration– Building warehouse from multiple Web sources

Integrating apartment listings from multiple sources Integrating job postings from multiple online job sources

Market focus: Online shopping Current Status: Acquired by Amazon Similar ventures: Netbots Inc. (www.excite.com) Acquired by Excite

Cohera Website: www.cohera.com Researcher Founders: Stonebraker, Hellerstein Products and Services:

– Cohera E-Catalog System– Integrates product data from multiple sellers and product catalogs– Set of software servers and tools for building and running live “e-catalogs”

Market(s) Targetted: E-Commerce Customers: E-Commerce communities - ThomasNet, Trapezo, LiveListings,

FoodService.Com Current Status: Founded October 1997, Privately Held Similar ventures: Ensosys Markets Inc. (www.enosysmarkets.com)

Mergent Inc. (www.mergent.com)

Nimble Technology

Website: www.nimble.com Researcher Founders: Levy, Weld Products and Services:

– Nimble Data Integration Suite– XML base integration approach– Current focus on multiple information sources integration– Tools for data extraction and Data Integration Engine

Market focus: CRM, Business Intelligence, B2B, Portals Current Status: Founded June 1999, Privately Held

WhizbangLabs !

Website: www.whizbanglabs.com Researcher Founders: Quass, Geddes, Mitchell Products and Services:

– Technology for building “Webbases” - databases created by extracting data from Web pages

– Topic specific– Topic specific crawler for retrieving pages– Tools for extracting data from Web pages, cleaning data

and loading into database Market focus: Content providing portals Current Status: Founded March 1999, Privately held Similar ventures: Fetch Technologies (www.fetch.com)

Bioinformatics: A Data Integration Grand Challenge

Mapping of Human Genetic Code complete– New, revolutionary, computational approach to drug discovery

Huge amounts of genetic, chemical and biological data being generated at an exponential rate in biotech/pharma R&D

– Complex structures, maps, sequence data etc. Drug discovery scientists need integrated access to this data

– Look for patterns across data sources Need to integrate data from multiple labs Lab procedures (thus the data) keeps changing Good amount of genomic data is free text DiscoveryLink: State of the art Life Sciences data integration

middleware from IBM

http://www-4.ibm.com/software/webservers/lifesciences/discovery.htmlhttp://www-4.ibm.com/software/webservers/lifesciences/discovery.html

Conclusion

Information mediation Issues in building such systems Research projects Industry products Start-up ventures Applicable to wide areas such as E-commerce, database and

legacy systems integration, Web source extraction, content management, portals, digital libraries, bioinformatics.