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Interactive Query Formulation over Web Service-Accessed Sources

Interactive Query Formulation over Web Service-Accessed Sources. Michalis Petropoulos Alin Deutsch Yannis Papakonstantinou. ACM SIGMOD, June 2006. Large-Scale Data Integration Systems. . Web Domain. Web Forms & Reports. CNET’s Top Combinations CNET’s Search Desktops

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Interactive Query Formulation over Web Service-Accessed Sources

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  1. Interactive Query Formulationover Web Service-Accessed Sources Michalis Petropoulos Alin Deutsch Yannis Papakonstantinou ACM SIGMOD, June 2006

  2. Large-Scale Data Integration Systems  Web Domain Web Forms & Reports • CNET’s Top Combinations • CNET’s Search Desktops • PCWorld’s Product Finder End User  Application Domain   • CNET Computer • PCWorld Portals Developer Application Application  Integration Domain Mediator Integrated Schema Compatible Combinations of Computers, Routers and Printers Integration Engineer Source Domain • Dell Computers by CPU • Cisco Routers by Rate • HP Printers by Speed  Web Service Web Service Web Service … Source Owner Data Source Data Source • Dell Computers • Cisco Routers • HP Printers Source Schema Source Schema …

  3. Large-Scale Data Integration Systems  Web Domain Web Forms & Reports End User  Application Domain What queries can the mediator answer for me? CLIDE   Developer Application Application  Integration Domain Mediator Integrated Schema Integration Engineer Source Domain  Web Service Web Service Web Service … Source Owner Data Source Data Source Source Schema Source Schema …

  4. Running Example Parameterized Views Schema Computers(cid, cpu, ram, price) NetCards(cid, rate, standard, interface) Views V1ComByCpu(cpu)  (Computer)* SELECT DISTINCT Com1.* FROM Computers Com1 WHERE Com1.cpu=cpu V2 ComNetByCpuRate(cpu, rate)  (Computer, NetCard)* SELECT DISTINCT Com1.*, Net1.* FROM Computers Com1, Network Net1 WHERE Com1.cid=Net1.cid AND Com1.cpu=cpu AND Net1.rate=rate Dell Cisco Schema Routers(rate, standard, price, type) Views V3 RouWired()  (Router)* SELECT DISTINCT Rou1.* FROM Routers Rou1 WHERE Rou1.type='Wired' V4 RouWireless()  (Router)* SELECT DISTINCT Rou1.* FROM Routers Rou1 WHERE Rou1.type='Wireless' Wired Routers Computers for a givencpu Wireless Routers Computers & NetCards for a givencpu & rate Conjunctive Queries CQ • Equality & Comparison Conditions • Parameters

  5. Running Example Integrated Schema   Developer Application Mediator Integrated Schema • Integrated schema puts togetherthe Dell and Cisco schemas Attribute Associations • (Computers.cid, NetCards.cid) • (NetCards.rate, Routers.rate) • (NetCards.standard, Routers.standard) V1 V2 V3 V4 Dell Cisco

  6. Sophisticated Mediators MakeFeasible Queries Hard to Predict Feasible Queries FQ • Equivalent CQ query rewritings using the views • Might involve more than one views • Order might matter Query: Get all ‘P4’ Computers, together with their NetCards and their compatible ‘Wireless’ Routers Query: Get all Computers Feasible Infeasible E Mediator B D Mediator A C RouWireless() ComNetByCpuRate(‘P4’, ‘10’) V1 ComNetByCpuRate(‘P4’, ‘54’) V4 V2

  7. Problem • Large number of sources • Large number of views (web-services) • Mediator capabilities Developer formulates an application query • Is an application query feasible? • If not, how do I know which ones are feasible? Previous options: • The developer had to browse the view definitions and somehow formulate a feasible query • Or formulate queries until a feasible one is found(trial-and-error) No system-provided guidance

  8. The CLIDE Solution CLIDE • A query formulation interface, which interactivelyguides the developer toward feasible queries by employing a coloring scheme   Developer Application Mediator Integrated Schema V1 V2 V3 V4 Dell Cisco

  9. QBE-Like Interfaces Microsoft SQL-Server

  10. CLIDE Interface Feasibility Flag Selection Boxes • Table, selection, projection and join actions • Feasibility Flag • Color-based suggestions

  11. Example Interaction Snapshot 1 Yellow required action • All feasible queries require this action White optional action • Feasible queries can be formulatedw/ or w/o these actions

  12. Example Interaction Snapshot 2 Blue required choice of action • At least one of them is required to be taken in order to reach a feasible query Join Lines: • Only yellow and blue are displayed • Must appear in Attribute Associations

  13. CLIDE Properties • Completeness of Suggestions • Every feasible query can be formulated by performing yellow and blue actions at every step • Minimality of Suggestions • At every step, only a minimal number of actions is suggested, i.e., the ones that are needed to preserve completeness • Rapid Convergence By Following Suggestions • The shortest sequence of actions from a query to any feasible query consists of suggested actions

  14. Example Interaction Snapshot 3 • * any other constant • Red prohibited action • Does not appear in any feasible query • Lead to “Dead End” state

  15. Example Interaction Snapshot 4 F Mediator A D RouWireless() ComNetByCpuRate(‘P4’, rate) B E V4 V2

  16. CLIDE Properties • Completeness of Suggestions • Every feasible query can be formulated by performing yellow and blue actions at every step • Minimality of Suggestions • At every step, only a minimal number of actions is suggested, i.e., the ones that are needed to preserve completeness • Rapid Convergence By Following Suggestions • The shortest sequence of actions from a query to any feasible query consists of suggested actions

  17. Interaction Graph Selection Action Table Action Join Action Com1 Com1.ram Com1.price Com1.cpu=‘P4’ Net1 Com1.cid=Net1.cid Rou1 … … … … … … … … … … • Nodes are queries • One for each qCQ • Edges are actions • Table, selection, projection and join actions • Green nodes are feasible queries • Infinitely big structure • All CQ queries • All possible combinations of actions formulating them

  18. Interaction Graph: Colors • Yellow action  • Every path from current node n to a feasible node contains  • Blue action  • At least one path from current node n to a feasible node nF contains  • There is not path from n to nF that contains a feasible node • Red action  • No path to a feasible node contains  Current Node Com1.cid … Com1.cpu … Current Node Com1.cid=* … … Com1.cpu=* Com1.cpu=* Net1 Com1.cid=Net1.cid Com1.cid=Net1.cid Net1.rate=’54Mbps’ … … … … Com1.ram=* Com1.cid=Net1.cid … … Net1.rate=’54Mbps’ Net1.rate=’54Mbps’ Com1.price=* … … … Com1.cpu=* Rou1 Rou1 Com1.cid=Net1.cid Net1.rate=Rou1.rate Net1 … … … … … … Rou1 Com2 Com2 … Com2 Com2.cid=Net1.cid Com2.cpu=‘P4’ Net1.rate=‘54Mbps’ … … … … …

  19. CLIDE Properties • Rapid Convergence By Following Suggestions • The shortest path from the current node to any feasible node consists of suggested actions

  20. CLIDE Architecture  Actions • Back-End invoked every time the user performs an action • i.e., the user arrives at a new node in the interactions graph Front-End User Current Query Colored Actions + Feasibility Flag Back-End Color Algorithm Seed Queries SQ Parameters Algorithm Closest Feasible Queries FQC Closest Feasible Queries Algorithm Aliases Collapse Rule Minimal Feasible Extension Queries Maximally-Contained Rewriter Schemas Views Attribute Associations

  21. Color DeterminedBy a Finite Set of Feasible Queries Challenge: Infinitely Many Feasible Queries ? Radius … … … n … … Closest Feasible Queries FQC … Solution: • Finite Set of Feasible Queries • Closest Feasible Queries FQC • FQC is sufficient to color actions Challenge: How far can the Closest Feasible Queries FQC be?

  22. Closest Feasible Queries FQC Algorithm Solution: Based on Maximally Contained Queries FQMC Maximally Contained Queries FQMC pL Radius … … … n … … Closest Feasible Queries FQC … • Compute maximally contained queries FQMC • Radius pL is the longest path to a maximally contained query • Theorem: All FQC queries are reachable via a path of length p  pL Challenge: The set of nodes within pL can be too many to explore

  23. Closest Feasible Queries FQC Algorithm Solution: Find the Closest Feasible Queries Directly Maximally Contained Queries FQMC … … … n … … Closest Feasible Queries FQC … More feasible nodes • Theorem: All queries in FQMC are in FQC • But not all queries in FQC are in FQMC • Collapse Aliases to compute FQC \ FQMC

  24. Remaining Issues of the Algorithm • Color remaining actions white or red • Handling projections • Dealing with parameterized queries

  25. CLIDE Implementation Issues • Maximally contained queries need to be minimized(affect CLIDE’s rapid convergence and minimality)  Implemented the minimization module from scratch  MiniCon computes mappings between the query and the views  Exposed these mappings to guide minimization • Optimizations to bring the response time below 3 sec  Amortize the computation across multiple interactions steps by observing that query is changed minimally at every step  Instead of calling MiniCon from scratch at every step, we incrementally compute the closest feasible queries

  26. Thank you Play with our demo: http://www.clide.info

  27. Maximally Contained Queries FQMC Not Maximally Contained Query: Q3 Get all Computers with a given cpu & ram Maximally Contained Query Query: Q4 Get all Computers with a given ram Maximally Contained Query Query: Q2 Get all Computerswith a given cpu Query: Q1 Get all Computers • Assuming fixed SELECT clause (projection list) • Covered extensively in literature • MiniCon, Bucket, InverseRules Algorithms • FQMC is finite

  28. Closest Feasible Queries FQC Algorithm Solution: Collapse Aliases Maximally Contained Feasible Queries FQMC … Closest Feasible Queries FQC … n … … … … • Collapse Aliases to compute FQC \ FQMC • Check satisfiability

  29. Color Algorithm Yellow and Blue • An action  is colored based on which closest feasible queries it appear in • Yellow, if  appears in all queries in FQC • Blue, if  appears in at least one (but not all) query in FQC White and Red • Attach Maximum Projection Lists to Closest Feasible Queries • Projections that can be added to a feasible query, without compromising feasibility • Projection  is white if in the maximum projection list • Color selections based on projections

  30. CLIDE Implementation & Optimizations Front-End Maximally-Contained Rewriter Current Query Colored Actions + Feasibility Flag Minimal Feasible Extension Queries FQME + Maximum Projection Lists Back-End Redundant Actions Removal Color Algorithm Maximally-Contained Feasible Extension Queries + Maximum Projection Lists Seed Queries SQ Redundant Queries Removal Parameters Algorithm Feasible Extension Queries + Maximum Projection Lists Closest Feasible Queries FQC Closest Feasible Queries Algorithm Views Expansion Aliases Collapse Rule Maximally-Contained Feasible Queries over Views + Containment Mappings Minimal Feasible Extension Queries Maximally-Contained Rewriter MiniCon Containment Mappings Logging Schemas Views Attribute Associations • Views expansion introduce redundancy • Affects CLIDE’s rapid convergence and minimality • Efficient containment test crucial to redundancy removal

  31. CLIDE Performance • Views • Schema C1 C1 … … B1 B1 Ci Ci1 B1 C1 … … … … … CiM CL CL A A Bi Bi1 B2 C1 … … … … BiM BK BK Chains of Stars • Queries A-span = 7 B-span = 3 Selections = 4,6,8,10 A

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