1 / 13

The Threshold Join Algorithm for Top-k Queries in Distributed Sensor Networks

The Threshold Join Algorithm for Top-k Queries in Distributed Sensor Networks. D. Zeinalipour-Yazti , Z. Vagena, D. Gunopulos, V. Kalogeraki, V. Tsotras. Proceedings of the 2nd international workshop on Data Management for sensor networks (DMSN’05). Introduction.

garran
Download Presentation

The Threshold Join Algorithm for Top-k Queries in Distributed Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Threshold Join Algorithm for Top-k Queries in Distributed Sensor Networks D. Zeinalipour-Yazti , Z. Vagena, D. Gunopulos, V. Kalogeraki,V. Tsotras Proceedings of the 2nd international workshop on Data Management for sensor networks (DMSN’05)

  2. Introduction • Let R be a relation with n attributes s1,s2,…,sn, each featuring m objects o1,o2,…om. • Oij: The jth attribute of the ith object. • qj: The jth attribute. • 1>=sim(qj,oij)>=0 ; wj > 0

  3. Introduction

  4. Threshold Join Algorithm • Lower Bound: the querying node finds a lower bound on the lists by probing the nodes in a network. • Hierarchical Joining: each node uses the lower bound for eliminating the objects that are below this bound and join the qualifying the objects that are below this bound and join the qualifying objects with results coming from children nodes. • Clean-Up: the actual top-k results are identified.

  5. Lower Bound (LB) Phase • list(Vi) : each node Vi sort in descending similarity order the elements in list(Vi). • listk(Vi) : the objectIDs of the k local highest ranked objects. • listk(Vj) : listk(Vi) include all its children Vj.

  6. Hierarchical Join (HJ) Phase

  7. Clean-Up (CL) Phase • Computing the complete score or an upper bound of this score.

  8. Threshold Join Algorithm(Example) Find the time moment with the highest average temperature. O3:0.99 01:0.66 O1:0.91 O3:0.90 O3:0.74 O1:0.56 O1:0.92 O3:0.75 O3:0.67 O4:0.67 O1:0.58

  9. Threshold Join Algorithm(Example) V5 V4 V3 V2 V1 O4=0.67+0.56+0.75+0.90+0.66=3.54 O3=0.67+0.74+0.75+0.90+0.99=4.05 O1=0.58+0.56+0.92+0.91+0.66=3.63 The Querying node has calculated an upper bound of 3.54 for O4, which is less than the score of O3 (i.e. 4.05), and so the querying node does not have to execute the CL phase.

  10. Experimental Evaluation • CJA: Centralized Join Algorithm • SJA: Staged Join Algorithm • TJA: Threshold Join Algorithm • penalty(Oi)=realrank(Oi)-rank(Oi) • Average error function

  11. Experimental Evaluation

  12. Experimental Evaluation

  13. Conclusion • Hierarchical Join • Experimentation under Failures

More Related