東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

A Distributed Graph Mining Framework...

Alkan, Sertan,

FindBook

Google Book

Amazon

博客來

A Distributed Graph Mining Framework Based on Mapreduce = = Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Distributed Graph Mining Framework Based on Mapreduce =/ Sertan Alkan.
其他題名:	Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi /
作者:	Alkan, Sertan,
面頁冊數:	1 electronic resource (63 pages)
附註:	Source: Masters Abstracts International, Volume: 86-05.
Contained By:	Masters Abstracts International86-05.
標題:	Growth models. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31663424
ISBN:	9798342522366

A Distributed Graph Mining Framework Based on Mapreduce = = Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi /
Alkan, Sertan,

A Distributed Graph Mining Framework Based on Mapreduce =Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi /Sertan Alkan. - 1 electronic resource (63 pages)

Source: Masters Abstracts International, Volume: 86-05.

The frequent patterns hidden in a graph can reveal crucial information about the network the graph represents. Existing techniques to mine the frequent subgraphs in a graph database generally rely on the premise that the data can be fit into main memory of the device that the computation takes place. Even though there are some algorithms that are designed using highly optimized methods to some extent, many lack the solution to the problem of scalability. In this thesis work, our aim is to find and enumerate the subgraphs that are at least as frequent as the designated threshold in a given graph. Here, we propose a new distributed algorithm for frequent subgraph mining problem that can scale horizontally as the computing cluster size increases. The method described here, uses a partitioning method and Map/Reduce programming model to distribute the computation of frequent subgraphs. In the core of this algorithm, we make use of an existing graph partitioning method to split the given data in the distributed file system and to merge and join the computed subgraphs without losing information. The frequent subgraph computation in each split is done using another known method which can enumerate the frequent patterns. Although current algorithms can efficiently find frequent patterns, they are not parallel or distributed algorithms in that even when they partition the data, they are designed to work on a single machine. Furthermore, these algorithms are computationally expensive but not fault tolerant and are not designed to work on a distributed file system. Using the Map/Reduce paradigm, we distribute the computation of frequent patterns to every machine in a cluster. Our algorithm, first bi-partitions the data via successive Map/Reduce jobs, then invokes another Map/Reduce job to compute the subgraphs in partitions using CloseGraph, recovers the whole set by invoking a series of Map/Reduce jobs to merge-join the previously found patterns. The implementation uses an open source Map/Reduce environment, Hadoop. In our experiments, our method can scale up to large graphs, as the graph data size gets bigger, this method performs better than the existing algorithms.

English

ISBN: 9798342522366Subjects--Topical Terms:

3681360
Growth models.

A Distributed Graph Mining Framework Based on Mapreduce = = Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi /
LDR:03467nmm a22003733i 4500 001 2437695
005 20251124054958.5
006 m o d
007 cr|nu||||||||
008 261011s2010 miu||||||m |||||||eng d
020 $a 9798342522366
035 $a (MiAaPQD)AAI31663424
035 $a (MiAaPQD)MiddleEastTech19407
035 $a AAI31663424
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Alkan, Sertan, $e author. $3 3828768
245 1 2 $a A Distributed Graph Mining Framework Based on Mapreduce = $b Esle/indirge Yontemi Uzerine Kurulu Daginik bir ag Madenciligi Gerceklemesi / $c Sertan Alkan.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2010
300 $a 1 electronic resource (63 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-05.
500 $a Advisors: Can, Tolga.
502 $b M.S. $c Middle East Technical University (Turkey) $d 2010.
520 $a The frequent patterns hidden in a graph can reveal crucial information about the network the graph represents. Existing techniques to mine the frequent subgraphs in a graph database generally rely on the premise that the data can be fit into main memory of the device that the computation takes place. Even though there are some algorithms that are designed using highly optimized methods to some extent, many lack the solution to the problem of scalability. In this thesis work, our aim is to find and enumerate the subgraphs that are at least as frequent as the designated threshold in a given graph. Here, we propose a new distributed algorithm for frequent subgraph mining problem that can scale horizontally as the computing cluster size increases. The method described here, uses a partitioning method and Map/Reduce programming model to distribute the computation of frequent subgraphs. In the core of this algorithm, we make use of an existing graph partitioning method to split the given data in the distributed file system and to merge and join the computed subgraphs without losing information. The frequent subgraph computation in each split is done using another known method which can enumerate the frequent patterns. Although current algorithms can efficiently find frequent patterns, they are not parallel or distributed algorithms in that even when they partition the data, they are designed to work on a single machine. Furthermore, these algorithms are computationally expensive but not fault tolerant and are not designed to work on a distributed file system. Using the Map/Reduce paradigm, we distribute the computation of frequent patterns to every machine in a cluster. Our algorithm, first bi-partitions the data via successive Map/Reduce jobs, then invokes another Map/Reduce job to compute the subgraphs in partitions using CloseGraph, recovers the whole set by invoking a series of Map/Reduce jobs to merge-join the previously found patterns. The implementation uses an open source Map/Reduce environment, Hadoop. In our experiments, our method can scale up to large graphs, as the graph data size gets bigger, this method performs better than the existing algorithms.
546 $a English
590 $a School code: 2013
650 4 $a Growth models. $3 3681360
650 4 $a Connectivity. $3 3560754
650 4 $a Metabolism. $3 541349
650 4 $a Graph representations. $3 3560730
650 4 $a Computer science. $3 523869
650 4 $a Web studies. $3 2122754
690 $a 0984
690 $a 0646
710 2 $a Middle East Technical University (Turkey). $b Department of Computer Engineering. $e degree granting institution. $3 3821962
720 1 $a Can, Tolga $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-05.
790 $a 2013
791 $a M.S.
792 $a 2010
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31663424