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Collaborative joins in a pervasive computing environment

Filip Perich , Anupam Joshi, Yelena Yesha , Tim Finin The VLDB Journal (2005) 2008. 11. 17. Summarized & presented by Babar Tareen , IDS Lab., Seoul National University. Collaborative joins in a pervasive computing environment. Introduction. To obtains data

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Collaborative joins in a pervasive computing environment

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  1. FilipPerich, Anupam Joshi, Yelena Yesha, Tim FininThe VLDB Journal (2005) 2008. 11. 17. Summarized & presented by Babar Tareen, IDS Lab., Seoul National University Collaborative joins in a pervasive computing environment

  2. Introduction • To obtains data • Devices should not solely depend on centralized servers • Devices should not be required to pre-cache all required data • A device should utilize its vicinity by collaborating with peers

  3. Introduction (2) • Data • Static – User Profile • Dynamic – Context Sensitive Data • Data which is affected by change in context • Not the actual context data • For example: List of restaurants near to a user • In this paper, context also includes • Belief, Desire, Intentions • Stored in user profile • Based on MoGATU

  4. Contribution • Collaborative Query Protocol (CQP) • Based on Contract Nets • Enables a mobile device to query its vicinity for peers that can answer a given query • Allows two or more devices to cooperate • A realistic experimental model for simulating a city traffic scenario • Demonstrate the capability of CQP by implementing it in MoGATU and by evaluating its performance

  5. MoGATU Overview • Information Providers • Represent Data sources available in environment • Information Consumers • Entity that query an update data available in the environment • Information Managers (InforMa) • Responsible for network communication and for most of the data management functions

  6. Data Representation • Data Model • A set of ontologies • Define ontologies using DAML+OIL • Using ontologies because of reasoning • Do not take into account the time necessary for reasoning over the ontology knowledge

  7. Query Representation SELECT (select_list) FROM (ontology_list) WHERE (conjunct_disjunct_predicate_list) LIMIT [minCardinality, maxCardinality] TIME neededBy • Explicit Query • User generated query • Implicit Query • Device generated query, inferred from user profile • User takes lunch between 12:00 pm – 2:00 pm and prefers Chinese food • Queries are specified in DAML-S • For this paper, abstracting queries to select-from-where form • query = (O, σ, θ,Σ, τ) • O : A set of used ontologies • σ : Selection list • θ : Filtering statement • Σ : Cardianality • τ: Temporal constraints

  8. CQP

  9. CQP (2) • Call for query • Initially device attempts to satisfy query using local cache • If not possible, creates a call-for-query message • Message contains • Query or part of query • Cardinality requirements • Deadline for delivering the complete answer • Time when the winner will be announced • Device sends the message to its peers upto n-hops • And Starts its bid-submission timer • If device does not gets any bid-submission response then it starts to decompose the query

  10. CQP (3) • Bid Submission (Upon receipt of call-for-query) • A device decides if it should interact in the proposed collaboration based on inference • If device does not wishes to participate or can not provide data, it simply ignores call-for-query • If device wishes to collaborate then it calculates the size of the answer it can provide • Returns bid message including estimated size of its answer • Starts a timer awaiting a bid-award • Bid Award • Contractor waits for a predefined time period for any responses • When bid submission timer expires, the bidder which claims to deliver the most data in shortest time is selected as winner • Contractor sends a bid award message • Starts Ack Timer • If a bidder does not receives a bid-award message before its timer expires, the bidder resend its bid message n-1 more times

  11. CQP (4) • Acknowledgement • When the bidder receives bid award message it sends back an ack message • Starts an Acktimmer and waits for ack from Contractor • When contractor receives ack from Bidder it send ack message

  12. Join Query over two streams In case 1, querying device A asks its vicinity for one input stream only since it already holds the second stream. In case 2, A asks its vicinity for the final join result only. In case 3, A asks for each stream separately in order to perform the join locally. In case 4, A asks B to process the query, but B needs to first obtain the second stream from some other device C. In case 5, A “delegates” the task to C, which asks its vicinity for the input streams instead.

  13. Experimental Setup • Environment • Realistic model that mapped streets and intersections south of 72nd Street in Manhattan • Directed graph with 793 intersections (vertices) • 5000 x 9000 m • Each intersection was assigned an (x,y) coordinate • Each intersection was given a list of its neighboring intersections • Beacon entity • Assigned a stationary beacon to each intersection • Each beacon has knowledge about its vicinity (Resturants, Theaters, etc)

  14. Experimental Setup (2) • Car entity • Use 100 cars • Transmission distance 125 m • Maximum throughput 2 Mbps • Mobility model • Car driven randomly by tourists (50 Cars) • Car driven by taxi driver on shortest possible route (50 Cars)

  15. Profile accuracy vs Query success rate • Fig 7a,b. • a: Willingness to help = 0% • b: Willingness to help = 75%

  16. Profile accuracy vs Computing Cost Implicit Queries

  17. Willingness to help vs query success rate Profile Accuracy 80%

  18. Willingness to help vs. computing and network cost Profile Accuracy 80%

  19. Willingness to help vs. query success rate / computing cost Profile Accuracy 80%

  20. Review • Pros • CQP can be used to query data from multiple sources • CQP can be used in any environment not just mobile peer – peer scenario • Cons • CQP is not much useful if devices already have access to some fixed network • More on discussion slide

  21. Discussion • Matrices used for evaluation are not appropriate • No comparison with any existing system with similar architecture • No comparison with centralized server architecture • Any technical problems in device – device communication not specified • In the example scenario, at every intersection beacons were installed • Cost of installing such beacons not specified • Enhancing centralized system vs. installing beacons • Only 100 cars were used in a space of 5000 x 9000 m • What will be the performance of the protocol if number of devices increase • Cost of ontology reasoning not considered • I think there is a lot of packet over head for query and this protocol might not be practically usable • A combination of server and peer-peer querying might give better results

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