efficient local algorithms for distributed data mining in large scale peer to peer environments

236 Pages·2010·2.44 MB·English

APPROVAL SHEET
TitleofThesis: EfficientLocal AlgorithmsforDistributedDataMiningin LargeScale
Peer toPeer Environments: A DeterministicApproach
NameofCandidate: KanishkaBhaduri
DoctorofPhilosophy,2008
Thesis and AbstractApproved:
Dr. HillolKargupta
AssociateProfessor
DepartmentofComputerScienceand
Electrical Engineering
DateApproved:
Curriculum Vitae
Name: KanishkaBhaduri.
Permanent Address: 211-FAtholgateLane, BaltimoreMD-21229.
Degree anddate to beconferred: DoctorofPhilosophy,2008.
DateofBirth: April24, 1980.
PlaceofBirth: Kolkata,India.
Secondary Education: SouthPointHighSchool, Kolkata,India,1997.
Collegiateinstitutions attended:
UniversityofMaryland BaltimoreCounty,Maryland,USA,
•
DoctorofPhilosophy,2008.
UniversityofMiami,USA,
•
2003–2004.
JadavpurUniversity,Kolkata,India,
•
BachelorofEngineering,ComputerScienceand Engineering,2003.
Major: ComputerScience.
Professionalpublications:
Refereed Journals
1. K.Bhaduri, H. Kargupta. DistributedMultivariateRegressioninPeer-to-Peer
Networks. StatisticalAnalysisandDataMining(SAM)Special IssueonBest of
SDM’08. (submitted)2008.
2. K.Bhaduri, R. Wolff,C. Giannella,H. Kargupta. DecisionTreeInductionin
Peer-to-Peer Systems. StatisticalAnalysisand DataMining(SAM)accepted for
publication. 2008.
3. K. Das,K. Bhaduri, K. Liu,H. Kargupta. DistributedIdentification ofTop-l Inner
Product Elementsand itsApplicationinaPeer-to-Peer Network. IEEE Transactions
onKnowledgeand DataEngineering. Volume20,Issue4,pp. 475-488. April2008.
4. K. Liu,K.Bhaduri, K. Das, P. Nguyen,H. Kargupta. Client-sideWeb Miningfor
CommunityFormationin Peer-to-Peer Environments. SIGKDD Explorations.
Volume8, Issue2, pp. 11-20. December 2006.
5. R. Wolff,K. Bhaduri, H. Kargupta. A GenericLocal AlgorithmwithApplications
forDataMiningin LargeDistributedSystems. IEEE Transactionson Knowledge
and DataEngineering(TKDE)(submitted). 2007.
BookChapter
1. K.Bhaduri, K. Das, K. SivaKumar,H.Kargupta, R. Wolff, R. Chen. Algorithms
forDistributedDataStream Mining. Achapterin DataStreams: Modelsand
Algorithms,Charu Aggarwal(Editor),Springer. pp. 309-332. 2006.
Refereed Conference Proceedings
1. K.Bhaduri, H. Kargupta. Anefficient local AlgorithmforDistributedMultivariate
RegressioninPeer-to-Peer Networks. Accepted forpublicationat the2008SIAM
InternationalDataMiningConference. (Best ofSDM’08)
2. R. Wolff,K. Bhaduri, H. Kargupta. Local L2 ThresholdingBased DataMiningin
Peer-to-Peer Systems. SIAM InternationalConference inDataMining,Bethesda,
Maryland,USA. pp. 430-441. 2006.
Refereed WorkshopProceedings
1. K. Liu,KBhaduri, K. Das, P. Nguyen,H. Kargupta. Client-sideWeb Miningfor
CommunityFormationin Peer-to-Peer Environments. SIGKDD workshoponweb
usageandanalysis(WebKDD). Philadelphia,Pennsylvania,USA. 2006. (Selected
asthe mostinteresting paper from the WebKDDworkshop)
2. K.Bhaduri, K. Das, H. Kargupta. Peer-to-Peer DataMining. Autonomous
IntelligentSystems: Agentsand DataMining. V. Gorodetsky,C. Zhang,V.
Skormin,L. Cao (Editors),LNAI4476,Springer. pp. 1-10. 2007.
Invited
1. S. Datta, K.Bhaduri, C. Giannella, R. Wolff,H. Kargupta. DistributedData
Miningin Peer-to-Peer Networks. IEEEInternet Computingspecial issueon
DistributedDataMining. Volume10, Number4, pp. 18-26. 2006.
Professionalpositions held:
Research Assistant. (05/2004–03/2008).
•
DistributedAdaptiveDiscoveryandComputationLab,Department ofComputer
Science andElectrical Engineering,UniversityofMaryland BaltimoreCounty
(UMBC).
SoftwareInternship. (05/2007– 08/2007).
•
SymantecCorporation,Columbia,Maryland.
Teaching Assistant. (08/2003– 05/2004).
•
DepartmentofComputerScience, UniversityofMiami,Florida.
ABSTRACT
TitleofDissertation: Efficient Local Algorithms for Distributed
Data Mining in Large Scale Peer to Peer
Environments: ADeterministicApproach
KanishkaBhaduri, DoctorofPhilosophy,2008
Thesis directed by: Dr. HillolKargupta
AssociateProfessor
DepartmentofComputerScienceand
Electrical Engineering
Peer-to-peer (P2P) systems such as Gnutella, Napster, e-Mule, Kazaa, and Freenet
areincreasinglybecomingpopularformanyapplicationsthatgobeyonddownloadingmu-
sic files without paying for it. Examples include P2P systems for network storage, web
caching,searchingandindexingofrelevantdocumentsanddistributednetwork-threatanal-
ysis. These environments are rich in data and this data, if mined, can provide valuable
source of information. Mining the web cache of users, for example, may often give in-
formationabout theirbrowsingpatternsleading toefficient searching,resource utilization,
query routing and more. However, most of the off-the-shelf data analysis techniques are
designed for centralized applications where the entire data is stored in a single location.
These techniques do not work in a highly decentralized, distributed environment such as
a P2P network. We need distributed data mining algorithms that are fundamentally local,
scalable, decentralized, asynchronousand anytimetosolvethisproblem.
ThisresearchproposesDeFraLC:aDeterminsiticFrameworkforLocalComputation
of functions defined on data distributed in large scale (peer to peer) systems. Computing
globaldatamodelsinsuchenvironmentscanbeveryexpensive. Movingallorsomeofthe
datatoacentral locationdoesnotworkbecauseofthehighcostinvolvedincentralization.
The cost increases even more under a dynamic scenario where the peers’ data and the
network topology change arbitrarily. In this dissertation we have focused on developing
algorithms for deterministic function-computation in large scale P2P environments. Our
algorithmic framework is local which means that a peer can compute a function based on
theinformationofonlyahandfulofnearby neighborsand thecommunicationoverheadof
the algorithm is upper bounded by some constant, independent of the size of the system.
As a consequence, several messages can be pruned, leading to excellent scalability of our
algorithms.
Thefirstalgorithmthatwehavedeveloped—PeGMA,Peer-to-PeerGenericMonitoring
Algorithm—iscapableofcomputingcomplexfunctionsdefinedontheaverageofthehor-
izontallydistributeddata. Thisgenericalgorithmisextremelyaccurate,highlyscalableand
can seamlesslyadapt to changes in thedata orthenetwork. FollowingPeGMA, severalin-
teresting algorithms can be developed such as the L2 norm monitoring of distributed data
which is a very powerful primitive. Using a two step feedback loop, a number of data
mining algorithms have been proposed. The first step uses the local algorithm to raise a
flag whenever the current data does not fit the function. The second step uses a feedback
loop to sample data from the network and build a new function. The correctness of the
local algorithm guarantees that once the computation terminates each peer has the same
resultcomparedtoacentralizedscenario. WeproposesolutionsforP2Pk-meansmonitor-
ing, eigen monitoring and multivariate regression in P2P environments. Furthermore, we
have shown how a complex data mining algorithm such as decision tree induction can be
developed for P2P environments. Finally we have implemented all of the algorithms in a
Distributed Data Mining Toolkit (DDMT) [44] developed at the DIADIC research lab at
UMBC.Ourextensiveexperimentalresultsshowthattheproposedalgorithmsareaccurate,
efficient and highlyscalable.
EFFICIENT LOCAL ALGORITHMS FOR
DISTRIBUTED DATA MINING IN LARGE SCALE
PEER TO PEER ENVIRONMENTS: A
DETERMINISTIC APPROACH
by
Kanishka Bhaduri
DissertationsubmittedtotheFacultyoftheGraduateSchool
oftheUniversityofMarylandinpartial fulfillment
oftherequirementsforthedegreeof
DoctorofPhilosophy
2008
c CopyrightKanishkaBhaduri 2008
(cid:13)