자료유형 | 학위논문 |
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서명/저자사항 | Big Graph Analytics on Just a Single PC. |
개인저자 | Wang, Kai. |
단체저자명 | University of California, Los Angeles. Computer Science 0201. |
발행사항 | [S.l.]: University of California, Los Angeles., 2019. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
형태사항 | 147 p. |
기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
ISBN | 9781085654494 |
학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2019. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Xu, Harry Guoqing. |
이용제한사항 | This item must not be sold to any third party vendors. |
요약 | As graph data becomes ubiquitous in modern computing, developing systems to efficiently process large graphs has gained increasing popularity. There are two major types of analytical problems over large graphs: graph computation and graph mining. Graph computation includes a set of problems that can be represented through liner algebra over an adjacency matrix based representation of the graph. Graph mining aims to discover complex structural patterns of a graph, for example, finding relationship patterns in social media network, detecting link spam in web data.Due to their importance in machine learning, web application and social media, graph analytical problems have been extensively studied in the past decade. Practical solutions have been implemented in a wide variety of graph analytical systems. However, most of the existing systems for graph analytics are distributed frameworks, which suffer from one or more of the following drawbacks: (1) many of the (current and future) users performing graph analytics will be domain experts with limited computer science background. They are faced with the challenge of managing a cluster, which involves tasks such as data partitioning and fault tolerance they are not familiar with |
일반주제명 | Computer science. |
언어 | 영어 |
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