Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/18781
Title: Data storage and retrieval for social network services
Authors: WANG TAO
Keywords: Data Storage, Social Network, Hadoop, Database, Graph Data Model, Data Partition
Issue Date: 7-Jun-2010
Source: WANG TAO (2010-06-07). Data storage and retrieval for social network services. ScholarBank@NUS Repository.
Abstract: In recent years, social network services have become ever more popular and even begin to affect people?s life. A lot of social network sites have attracted tens of millions of users, where people contribute content, share information and activities with each other. Social network services are so popular as they allow users to display their creativity and knowledge, take ownership of the content, and obtain shared information from the community. A social network site serves as a platform for users of a community to interact and collaborate with each other. In social networks, users are connected through various social relationships like friendship, kinship, professional, academic and so forth, while a huge amount of objects such as blogs, photos and videos are connected to the users through ownership, comment-relationship, tagging-relationship and so on. Obviously, a social network contains extremely complicated relationships. This brings many challenges for querying and analyzing social network data. The popularity of social network services and the challenges for querying and analyzing social network data have driven to develop a new type of systems to support social network services. In this thesis, we focus on investigating a new data storage and indexes for a new graph database which is designed to manage nonblob data for social network services. We introduce two approaches, the Ordering method and the Minimum Spanning Tree(MST) method, to partition a huge social network graph into several small parts and distribute them over a cluster of servers. Two types of indexes, content index and node index, are investigated to improve the performance. We also design an object store system, called HadoopObS, to store blob data for social network services. Several experiments on crawled Flickr data are conducted to evaluate our storage and index design.
URI: http://scholarbank.nus.edu.sg/handle/10635/18781
Appears in Collections:Master's Theses (Open)

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