1 / 16

Efficient Top-k Search Strategy for Multiple XML Data Sources

This paper presents a BT-based scheduling strategy for efficient top-k query evaluation across heterogeneous XML data sources. By determining data sources, calculating ranking scores, and refining candidate results, this strategy optimizes query processing. Experimental results demonstrate its effectiveness using XMark data sets.

cassie
Télécharger la présentation

Efficient Top-k Search Strategy for Multiple XML Data Sources

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. Efficient Top-k Search across Heterogeneous XML Data Sources Jianxin Li1 Chengfei Liu1 Jeffrey Xu Yu2 Rui Zhou1 1Swinburne University of Technology 2Chinese University of Hong Kong

  2. Outline • Motivation • Related Work • Preliminary and Problem Statement • BT-based Scheduling Strategy • Case Study • Experiments • Conclusions

  3. Motivation • Top-k queries • Approximate answers are required when exact results cannot be found. • Returning a large number of results is not desirable. • Multiple XML data sources • With the application of XML data, sometimes users are interested in the results retrieved from several data sources at the same time. • Answering top-k queries over multiple xml data sources is still open problem.

  4. Related Work • Top-k queries in XML • Amelie Marian etc. Adaptive processing of top-k queries in xml. ICDE2005. • Martin Theobald etc. An efficient and versatile query engine for topX search. VLDB2005. • Raghav Kaushik etc. On the integration of structure indexes and inverted lists. SIGMOD2004. • Top-k queries in Relational DB • Upper, MPro and TPUT etc. We focused on top-k queries over multiple XML data sources!

  5. Preliminary – XML Query Relaxation • XML data and relevant schemas Fig.1 bookshop S1 Fig.2 schema d1 of S1 Fig.3 bookshop S2 Fig.4 schema d2 of S2

  6. Preliminary – XML Query Relaxation • Relaxed results RankScore = 2.28 RankScore = 4.88 Fig.6 a relaxed query to d1 Fig.5 an original query q Fig.7 a relaxed query to d2 • We keep the changed weight for each edge in relaxed queries.

  7. Problem Statement • Given a weighted query q and a number of data sources {S1, S2, …, Sn} conforming to DTDs {d1, d2, …, dn}, let {q1, q2, …, qn} be the set of weighted relaxed query templates of q w.r.t. the set of DTDs, our aim is to efficiently search top k results by scheduling the evaluation of {q1, q2, …, qn} over {S1, S2, …, Sn}.

  8. BT-based Scheduling Strategy • Data source determination and switching • Result determination • Edge selection

  9. Data source determination and switching • Computing the ranking scores {U(1) … U(n)} of relaxed queries {q1, q2, …, qn} w.r.t. data sources {S1, S2, …, Sn}. • Sorting the ranking scores as U={U(k1), … U(kn)} . • Taking the data source Sk1 to be evaluated and U(k2) as the current threshold σ. U(1) = 2.28 Threshold σ= 2.28 The relaxed query q1 w.r.t. d1 U(2) = 4.88 The relaxed query q2 w.r.t. d2

  10. Result determination • We adjust the lower bound L and upper bound U during query evaluation. When L becomes equal to or larger than the current threshold, we can process the current candidates as follows: • The number of candidates is equal to k – Stop • The number of candidates is less than k – Continue to search • The number of candidates is larger than k – Refine candidates

  11. Edge selection • Random • Min_weight • Max_weight

  12. Case Study U(2) = 4.88 σ= 2.28 Top-2 result found! Top-1 result found! L(2)(G3) = 4.4 >σ book book L(2)(G1) = 3.5 >σ book B2 B1 title title B1, B2, B4 title info info info B4 B2, B4 L(2) = 1.70 <σ price year L(2)(G4) = 1.70 < σ U(2)(G4) = 2.18 < σ price L(2)(G2) = 1.70 < σ U(2)(G2) = 3.08 > σ Switching Data Source to search top-3 result!

  13. Experiments • Experimental setup • We run all algorithms in Java on an Intel P4 3GHz PC with 512M memory. Wutka DTD parser was used to analyze the structures of DTDs. • Dataset and selected queries • We used Xmark XML data generator to produce a set of data that were taken as dataset. • Three queries were designed: • q1: //item[./description/parlist] • q2: //item[./description/parlist/mailbox/mail[./text]] • q3: //item[./mailbox/mail/text[./keyword and ./xxx] and ./name and ./xxx]

  14. Experiments Static sort vs. Dynamic sort No schedule vs. BT schedule Varing top-k size Varing top-k size

  15. Conclusions • Contributions: • Proposed a BT-based scheduling strategy for evaluating top-k queries over multiple XML data sources; • Output results immediately without waiting for the end of query evaluation; • Implemented relevant algorithms and demonstrated its effectiveness and efficiency with XMark data sets.

  16. Thanks & Question

More Related