1 Context-Aware Internet Sharma Chakravarthy UT Arlington December 19, 2008

2 InfoMosaic  We are working on a project termed InfoMosaic -- to integrate information from heterogeneous sources  One of the motivations is to extend the querying capability to internet instead of being satisfied with search  Today, we know how to search sites individually; but cannot do much when it comes to combining data/information from multiple sources (except in limited/customized ways)  The above requires lots of context information -- as we shall see

3 Motivation Find – Castles near London reachable by train in 2-3 hours Search: Castles near London Castle Results Train Schedules Schedules - Decision Making Process - Manually Integrate Results to arrive at a decision - Decision Making Process - Manually Integrate Results to arrive at a decision

4 Motivation  Example – 1 : Find – 3-Bedroom House in Austin, TX within 2 miles of an “exemplary” school and within 5 miles of Y highway and priced under Z dollar  Example – 2 : Find – A list of openings for Software Engineers in Companies having their stock price over ‘X’ dollars listed in Nasdaq  Example – 3 : Find – Prices of CDs or Records of the 1998 Grammy award winner for Folk Category  The GOAL is to integrate information from a small number of heterogeneous sources in different domains

5 Challenges  Query Specification/Refinement/feedback – adaptive capability  Query Planning & Optimization  Query Reformulation/matching to sources  Data Extraction  Data, Schema / Ontology Integration  Result ranking  Result presentation/Visualization  Inconsistency Management /confidence  Security & Privacy  Handling hidden web  Source and semantics Discovery  Generalization to arbitrary domains

6 Search and Querying Structured dataUnstructured data Querying Search Some work

7 Query/search specification  To specify the query, “Retrieve castles near London that are reachable by train in less than 2 hours”  Input  {castle, train, London} or  {train, from, London, to, castle} or  {train, castle, location, city, London, duration, 2 hours} or  {castle, reachable, train, from, London, 2 hours, or, less than}  Use context – profiles, semantics of usage, feedback and any other information to infer the above query or close to a meaningful query!  For instance, the above inputs can also mean:

8 Query/search specification  Input  {castle, train, London} or  {train, from, London, to, castle} or  {train, castle, location, city, London, duration, 2 hours} or  {castle, reachable, train, from, London, 2 hours, or, less than}  Retrieve Castles near London that are reachable by Train  Retrieve Hotels near London that are Castles and can be reached by a Train  Retrieve Books whose title contain the words `Castle' or `Train' written by an author whose name is `London'

9 Useful context information  Domain taxonomies  Attribute associations with concepts in the taxonomy  Types of attributes  operators and their classification – spatial, temporal, other, …  Dictionary ranking of the meaning of words  User feedback on the usage of words or combinations  Resolving ambiguities from the user  Various types of source semantics  Automatic or semi-automatic Discovery of this information is a separate (hard) problem.

10 Data Store Knowledge-base Query Refinement Module Query Planner and Optimizer Query Execution and Data Extraction Data Integration XML Repository Spatial Data Repository Query Plan Internet Spatial DB Web Pages …… XML Data Hidden Web Data Web Services Query Interfaces Domain Knowledge Source Semantics MetadataOntology DictionaryOperators I/O Attributes Statistics Schemas Users User Query/Inte nt Feed back User Query XQueries Final Result-set Query Results Query The InfoMosaic Framework Ranking

11 Current Status  Working on Query Specification  Query Planning and Execution  Identifying components of the KB  Identifying source semantics  Discovery of some of the above either automatically or semi-automatically

12 Thank you!