scenario query shipping vs. data shipping solution experiments sebastian obermeier, stefan böttcher...

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:

ICEC 2008 2/19


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

ICEC 2008 9/19


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



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GZIP Compression



XPath Query Shipping (GZIP)

XPath Query Shipping (GZIP) , 500kB Cache

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Varying Cache Sizes



500kB Cache


1000kB Cache


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

scenario query shipping vs. data shipping solution experiments sebastian obermeier, stefan böttcher...

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