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Integrating Structured Integrating Structured & Unstructured Data& Unstructured Data
Goals
Identify some applications that have crucial requirement for integration of unstructured and structured data
Identify key technical issues in integrating unstructured and structured data
Identify potential approaches
Definitions (simplified)
Structured object: – <oid, {<name, value>}>
Unstructured object:– <oid, {word}>– <oid, unknown/complex structure>
Semi-structured object– <oid, {<name, value>}, {word}>– <name, value> pairs may be
• Given (e.g. author, title, etc.)• Extracted (e.g. Date, Zipcode, etc.)• Inferred (e.g. Topic)
Representative Applications
BPI: Messasges- unstructured Web Applications: unstructured pages Corporate Portals: DSS involving Combination of simulation with database system News syndication: author etc + story Call centers: customer interaction + structured component of complaint Mail system/document systems Tourist information system Product catalogs/engineering spec sheets Patents/chenistry documents Matching Legal documents (with cross citations) with building codes ---
representative
Key Technical Issues
Query language & data model– Sharp vs fuzzy / complete vs best-effort– Boolean vs similarity queries (relationship to “value”)
Integration strategies– Loose vs. tight coupling Architectures (many possibilities)– Search engine into DBMS or DBMS into search engine– Late & early binding (warehousing vs virtual)– Integration vs articulation (union vs intersection)
Feature extraction from unstructured data Role of meta data & integrity constraints Inconsistency of data sources
– Priorty rules for mediation Management & data organization issues
– Version management , freshness, security Continuous queries over streams
Strucured:People(firstname, lastname, company, location)Semi-structured:Papers(title, {authors}, text)
Unstructured: Reviews
Q1: Reviews of papers by Almaden authors on IISearch reviews using Join(People.<fn,ln>, Papers.authors).keywords
Q2: Folks in Almaden and Watson working on same topicJoin of Papers.text followed by joined with names in People
Q3: Papers on privacy & data mining by Agarwal in WatsonCombine ranks of results from People and Papers
Q4: Almaden authors whose papers had negative reviewsInfer sentiment of a review and interesting joins
Q5: Crrent research topics in AlmadenJoin People and Papers followed by clustering
Combining Scores
DB:– Aggarwal, Watson, s1– Agarwal, Almaden, s2– Agrawal, Almaden, s3
IR– Sigmod 00 paper, r2– PODS 01 papers, r1– KDD00 paper, r3
Query
DB IR
Result
Chopper Combiner
Papers on privacy & data mining
by Agarwal in Watson
Query Processing
Query
Chopper & Router
DB IR
Result Query
Chopper & Router
DB IR
Result
Approaches (1)
Query Languages– XML-based extensions for queries
• W3C working group on Xquery considering extension for full text
• XXL (Weikum), XIRQL (Fuhr)– Specialized languages for highly structured data (e.g. chemical
molecules)?– Graph-based models & languages (RDF, Protégé – Stanford)– Extended relational (e.g. SQL/MM)– Inverse queries on business events– Reasoning systems– Statistical approaches (approximate/ data mining)
Approaches (2)
Pluses of tight coupling– Enforcement of ontologies, schemas– Security, management, query optimization, integriry
constraints Negatives of tight coupling
– Does not address federation issues/autonomy Pluses of loose coupling
– Flexibility Negatives of loose coupling
And the dinner bell rings …
Concluding Remarks
We need further discussion on issues and approaches during the rest of the workshop