A scalable and flexible correlated data storage framework based on heterogeneous graph

Pao Yue-kong Library Electronic Theses Database

A scalable and flexible correlated data storage framework based on heterogeneous graph

 

Author: Zhou, You
Title: A scalable and flexible correlated data storage framework based on heterogeneous graph
Degree: M.Sc.
Year: 2014
Subject: Information storage and retrieval systems.
Computer vision.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: vii, 57 leaves : illustrations ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2757725
URI: http://theses.lib.polyu.edu.hk/handle/200/7542
Abstract: Heterogeneous Graph, with the ability to store data and relations with diverse schemas, is both intuitive to manage relational information and powerful to mine underlying knowledge from them. In typical cases, the graph is required to be progressive, which means the attributes of nodes (or edges) need to support drill down/up to represent information with different granularity. To meet this requirement, previous works simply copy and transform the data from predefined granularity and store it as a graph, which is not flexible and results in redundant data across graphs. However, due to the fact that graphs with millions of nodes and edges are now common places, it is costly to copy data with extreme high volume and might lead to sync problem. Moreover, with the design of centralized data storage, those works are not scalable as well. To address the problems, GNODE, a scalable and flexible heterogeneous graph storage framework, is proposed. The contributions of this paper include: 1) multi-schema data model and dynamic binding design permit graphs with different granularity built sharing the same dataset to reduce data redundancy and increase flexibility; 2) distributed architecture and index design increase the scalability and performance without losing flexibility. With the experiments conducted on varisized datasets, GNODE is proved to have similar performance to current graph database like neo4j but outperform in flexibility and scalability.

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