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Integrating Structured & 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).
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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 II • Search reviews using Join(People.<fn,ln>, Papers.authors).keywords Q2: Folks in Almaden and Watson working on same topic • Join of Papers.text followed by joined with names in People Q3: Papers on privacy & data mining by Agarwal in Watson • Combine ranks of results from People and Papers Q4: Almaden authors whose papers had negative reviews • Infer sentiment of a review and interesting joins Q5: Crrent research topics in Almaden • Join 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 Papers on privacy & data mining by Agarwal in Watson Result Query Chopper Combiner DB IR
Result Result Query Query Chopper & Router Chopper & Router DB IR DB IR Query Processing
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