09/12/2003 09/12/2003 Peer-to-Peer Information Systems – Peer-to-Peer Information Systems – WS 03/04 WS 03/04 1 Piazza: Data Management Infrastructure for Semantic Web Applications Alon Y. Halevy, Zachary G. Ives, Alon Y. Halevy, Zachary G. Ives, Peter Peter Mork, Igor Tatarinov. Mork, Igor Tatarinov. Speaker: Sergey Chernov Speaker: Sergey Chernov Tutor: Jens Graupmann Tutor: Jens Graupmann
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Piazza: Data Management Infrastructure for Semantic Web Applications
Piazza: Data Management Infrastructure for Semantic Web Applications. Alon Y. Halevy, Zachary G. Ives, Peter Mork, Igor Tatarinov. Speaker: Sergey Chernov Tutor: Jens Graupmann. Outline. INTRODUCTION. SEMANTIC WEB. PIAZZA: SYSTEM OVERVIEW IMPLEMENTATION DETAILS 3.1 MAPPING LANGUAGE - PowerPoint PPT Presentation
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09/12/200309/12/2003 Peer-to-Peer Information Systems – WS 03/Peer-to-Peer Information Systems – WS 03/0404
11
Piazza: Data Management Infrastructure for Semantic Web
Applications
Alon Y. Halevy, Zachary G. Ives,Alon Y. Halevy, Zachary G. Ives, Peter Mork, Peter Mork, Igor Tatarinov.Igor Tatarinov.
Speaker: Sergey ChernovSpeaker: Sergey Chernov
Tutor: Jens GraupmannTutor: Jens Graupmann
09/12/2003 Peer-to-Peer Information Systems – WS 03/04 2
OutlineOutline
1. INTRODUCTION. SEMANTIC WEB.2. PIAZZA: SYSTEM OVERVIEW3. IMPLEMENTATION DETAILS
3.1 MAPPING LANGUAGE3.2 QUERY ANSWERING ALGORITHM
4. CONCLUSIONS.
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IntroductionIntroduction
►Goal:Goal: Data Integration and Knowledge Data Integration and Knowledge
ManagementManagement
►Problem:Problem: Web data lacks machine-understandable Web data lacks machine-understandable
semanticssemantics
►Solution:Solution: Semantic Web?Semantic Web?
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The Semantic WebThe Semantic Web**
► Web sites include structural annotations You can pose meaningful queries on them. Ontologies provide the semantic glue. Internal implementation of web sites left open.
► Agents perform tasks: Query one or more web sites Perform updates (e.g., set schedules) Coordinate actions Trust each other (or not).
► I.e., agents operating on a gigantic heterogeneous distributed database.
(*View by A. Halevy)(*View by A. Halevy)
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General requirementsGeneral requirements► Robust infrastructure for querying
Peer data management systems.
► Facilitate mapping between different structures. Need tools for: Locating relevant structures Easily joining the semantic web.
► Get data into structured form Should we worry about the legacy web?
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Using views for specifyingmappings
► Local-As-View (LAV). Data sources can be described as views over the mediated schema.
► Global-As-View (GAV). Mediated schema can
be described as a set of views over the data sources.
Mediated Schema
Site B
Site A
Site C
Mediated Schema
Site B
Site A
Site C
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Mapping
►Mapping AB Mapping AB specifies specifies representation representation of structured of structured data from data from scheme of scheme of node A into node A into scheme of scheme of node B node B
Mediated Schema
Site B
Site A
Site C
Mapping “AB”
Mapping “BA”
Mapping “BC”
Mapping “CB”
Mapping
“C-MS”
Mapping
“MS-C”
Mapping
“A-MS”Mapping
“MS-A”
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► ProblemProblem Evaluate query Q at PEvaluate query Q at P11 given a network of mappings given a network of mappings
► Reformulate the query over all relevant peersReformulate the query over all relevant peers Chaining of mappings using a combination of query Chaining of mappings using a combination of query
composition and query rewritingcomposition and query rewriting
where $advisee=$student :} { $name } <advisor> </student> </people> </S2>
<result> { for $faculty in /S1/people/faculty, $name in $faculty/name/text(), $advisee in $faculty/advisee/text() where $name = “Ullman” return <student> {$advisee} </student> }</result>
Query:
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Query Reformulation (2)Query Reformulation (2)
<result> { for $faculty in /S1/people/faculty, $name in $faculty/name/text(), $advisee in $faculty/advisee/text() where $name = “Ullman” return <student> {$advisee} </student> }</result>
Query:
<result>
name advisee$name = “Ullman”
<student> {$advisee}
S1
people
faculty
<S2>
S1<people> people
faculty name<faculty> {$name}
student<student>
<name> {$student}
faculty
name advisee$advisee=$student<advisor> {$name}
Query tree pattern:
Mapping tree pattern:
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Query Reformulation (3)Query Reformulation (3)
Query:
<result>
name advisee$name = “Ullman”
<student> {$advisee}
S1
people
faculty
<S2>
S1<people> people
faculty name<faculty> {$name}
student<student>
<name> {$student}
faculty
name advisee$advisee=$student<advisor> {$name}
Query tree pattern:
Mapping tree pattern:
<result> { for $faculty in /S2/people/student, $advisor in $student/advisor/text(), $name in $student/name/text() where $advisor = “Ullman” return <student> { $name } </student>}</result>
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Reformulation timesReformulation times
► Table 1: The test queries and their Table 1: The test queries and their respective running times.respective running times.
Query Description Reformulation time # of reformulations
Q1 XML-related projects. 0.5 sec 12
Q2Co-authors who reviewed
each other's work.0.9 sec 25
Q3PC members with a paper
at the same conference.0.2 sec 3
Q4PC chairs of recent
conferences + their projects.
0.5 sec 24
Q5Conflicts-of-interest of PC
members.0.7 sec 36
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Current and the FutureCurrent and the Future
► Current statusCurrent status Demo scenario using XML Demo scenario using XML Looking at real domains (Bio dbs, NASA dbs) Looking at real domains (Bio dbs, NASA dbs)
redundant mappings and inconsistent redundant mappings and inconsistent mappingsmappings
Query caching to speed up query evaluationQuery caching to speed up query evaluation
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ConclusionsConclusions
► Mapping language for mapping between sets of XML source nodes with different document structures
► Architecture that uses the transitive closure of mappings to answer queries
► Algorithm for query answering over this transitive closure of mappings, which is able to follow mappings in both forward and reverse directions
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Thank You!Thank You!
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Further literatureFurther literature
1.1. Alon Y. Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov: Alon Y. Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov: Schema Schema Mediation for Large-Scale Semantic Data SharingMediation for Large-Scale Semantic Data Sharing
2.2. Igor Tatarinov, Zachary Ives, Jayant Madhavan, Alon Halevy, Dan Suciu, Igor Tatarinov, Zachary Ives, Jayant Madhavan, Alon Halevy, Dan Suciu, Nilesh Dalvi, Xin (Luna) Dong, Yana Kadiyska, Gerome Miklau, Peter Mork: Nilesh Dalvi, Xin (Luna) Dong, Yana Kadiyska, Gerome Miklau, Peter Mork: The Piazza Peer Data Management ProjectThe Piazza Peer Data Management Project
3.3. Alon Y. Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov: Alon Y. Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov: Schema Schema Mediation in Peer Data Management SystemsMediation in Peer Data Management Systems
4.4. Alon Halevy, Oren Etzioni, AnHai Doan, Zachary Ives, Jayant Madhavan, Alon Halevy, Oren Etzioni, AnHai Doan, Zachary Ives, Jayant Madhavan, Luke McDowell, Igor Tatarinov: Luke McDowell, Igor Tatarinov: Crossing the Structure ChasmCrossing the Structure Chasm
5.5. Madhan Arumugam, Amit Sheth, and I. Budak Arpinar: Madhan Arumugam, Amit Sheth, and I. Budak Arpinar: Towards Peer-to-Towards Peer-to-Peer Semantic Web: A Distributed Environment for Sharing Semantic Peer Semantic Web: A Distributed Environment for Sharing Semantic Knowledge on the WebKnowledge on the Web
6.6. Hendler J., Berners-Lee T., Miller E.: Hendler J., Berners-Lee T., Miller E.: Integrating Applications on the Integrating Applications on the Semantic WebSemantic Web