scenario query shipping vs. data shipping solution experiments sebastian obermeier, stefan böttcher...
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Scenario Query Shipping vs. Data Shipping Solution Experiments
Sebastian Obermeier,Stefan Böttcher
University of PaderbornGermany
ICEC 2008, Innsbruck, Austria
XML Fragment Caching for Large-Scale Mobile Commerce Applications
Agenda:
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Scenario Query Shipping vs. Data Shipping Solution Experiments
Large Event Scenarios
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Use Case
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Use Case
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GPRS/UMTS
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Ad-Hoc Network
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Query Shipping
Query Q
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Caching for Query Shipping
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
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Data Shipping
{7}{1,2,3}
{1,2}
{2,3}
{1,4,7} {1,2}
Query Q: {1,3,4}
{1,3,4,7}
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Caching for Data Shipping
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()
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Application Considerations 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
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Solution Overview Split XML document into disjoint
fragments according to aSplit 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)
XMLS1 S3
S4
S2
S6
S5
XMLS1 S3
S4
S2
S6
S5
S5
S3
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Split Schema Graph XML document split
into disjoint parts
Segment 1 /1/2/2/1
<restaurants> <restaurant id = "25" areaID="15"> <name>Forester`s House</name> <description>Traditional… </…> <style>German</style> </restaurant>
<restaurant id = "35" areaID="17"> <name>Garden of Sun</name> <description>Large beer garden…</…> <style>Austrian</style> </restaurant> ...</restaurants>
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Determine Required Segments
//restaurant[@areaID>13][@areaID<19]/name
Required Segments1 / */*/2/*/
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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
Experimental Evaluation
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Experimental Results
XPath Query Shipping
XPath Query Shipping 500kB Cache
XPath Query Shipping
XPath Query Shipping 500kB Cache
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GZIP Compression
XPath Query Shipping
XPath Query Shipping 500kB Cache
XPath Query Shipping (GZIP)
XPath Query Shipping (GZIP) , 500kB Cache
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Varying Cache Sizes
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
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Summary and Conclusion 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
XMLS1 S3
S4S2
S6
S5
2 /4/*/1 == 2 /4/2/1