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Scenario Query Shipping vs. Data Shipping Solution Experiments Sebastian Obermeier, Stefan Böttcher University of Paderborn Germany ICEC 2008, Innsbruck,

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Presentation on theme: "Scenario Query Shipping vs. Data Shipping Solution Experiments Sebastian Obermeier, Stefan Böttcher University of Paderborn Germany ICEC 2008, Innsbruck,"— Presentation transcript:

1 Scenario Query Shipping vs. Data Shipping Solution Experiments Sebastian Obermeier, Stefan Böttcher University of Paderborn Germany ICEC 2008, Innsbruck, Austria Agenda:

2 ICEC 2008 2/19 Scenario Query Shipping vs. Data Shipping Solution Experiments

3 ICEC 2008 3/19 Scenario Query Shipping vs. Data Shipping Solution Experiments

4 ICEC 2008 4/19 Scenario Query Shipping vs. Data Shipping Solution Experiments

5 ICEC 2008 5/19 Scenario Query Shipping vs. Data Shipping Solution Experiments

6 ICEC 2008 6/19 Scenario Query Shipping vs. Data Shipping Solution Experiments

7 ICEC 2008 7/19 Scenario Query Shipping vs. Data Shipping Solution Experiments Query Q

8 ICEC 2008 8/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  Intermediate node N checks whether it can answer Q Only Q's result is transferred  Test can be complex and time consuming  Small missing parts of information lead to cache-misses:  Qcache = //restaurant[./@areaID<50]//description  Q = //restaurant[./@areaID<35]//description

9 ICEC 2008 9/19 Scenario Query Shipping vs. Data Shipping Solution Experiments {7} {1,2,3} {1,2} {2,3} {1,4,7}{1,2} Query Q: {1,3,4} {1,3,4,7}

10 ICEC 2008 10/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  Request parts of the document Combination of cached content can answer Q Tests are fast  Huge amount of overhead if read-set is large, e.g. if Q uses count()

11 ICEC 2008 11/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  No arbitrary queries  Query templates predefined  Mostly point and range queries including filters  Database can track queries  Focus on content, e.g. text, pictures, and videos  Database updates are rare  Egoistic node behavior  do not spend much energy to other node’s queries

12 ICEC 2008 12/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  Split XML document into disjoint fragments according to a Split Schema Graph (SSG)  Querying node determines by SSG necessary fragments to answer query Q  Q is executed locally on the read-set of Q (=merged segments) XML S1 S3 S4 S2 S6 S5 XML S1 S3 S4 S2 S6 S5 S3

13 ICEC 2008 13/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  XML document split into disjoint parts Segment 1 /1/2/2/1 Forester`s House Traditional … German Garden of Sun Large beer garden … Austrian... Forester`s House Traditional … German Garden of Sun Large beer garden … Austrian...

14 ICEC 2008 14/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  //restaurant[@areaID>13][@areaID<19]/name Required Segments 1 / */*/2/*/

15 ICEC 2008 15/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  1600 devices, logical clock  24MB Information Repository  Max. distance 5 hops  Individual query profiles  Each with 164 XPath queries  80% request hotspot data (5MB)  Hotspot changes during evaluation

16 ICEC 2008 16/19 Scenario Query Shipping vs. Data Shipping Solution Experiments XPath Query Shipping XPath Query Shipping 500kB Cache XPath Query Shipping XPath Query Shipping 500kB Cache

17 ICEC 2008 17/19 Scenario Query Shipping vs. Data Shipping Solution Experiments XPath Query Shipping XPath Query Shipping 500kB Cache XPath Query Shipping (GZIP) XPath Query Shipping (GZIP), 500kB Cache

18 ICEC 2008 18/19 Scenario Query Shipping vs. Data Shipping Solution Experiments XPath Query Shipping (GZIP) XPath Query Shipping (GZIP) 500kB Cache XPath Query Shipping (GZIP) 1000kB Cache XPath Query Shipping (GZIP) 2000kB Cache 500kb Cache 1000kb Cache 2000kb Cache

19 ICEC 2008 19/19 Scenario Query Shipping vs. Data Shipping Solution Experiments  Querying and caching mechanism that allows clients to execute queries locally  Application based fragmentation schema  Simple cache contribution tests by IDs  Coupes with egoistic node behavior  Reduces network traffic up to 88%  Improves query response time up to factor 5  Reduces bottlenecks  Can be individually used for each query type XML S1 S3 S4 S2 S6 S5 2 /4/*/1 == 2 /4/2/1


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