Key-Key-Value Stores for Efficiently Processing Graph Data in the Cloud
DocUID: 2011-009 Full Text:
PDF Author: Alexander G. Connor, Panos K. Chrysanthis, Alexandros Labrinidis
Abstract: Modern cloud data storage services have powerful capabilities for data-sets that can be indexed by a single key-key-value stores-and for data-sets that are characterized by multiple attributes (such as Google's BigTable). These data stores have non-ideal overheads, however, when graph data needs to be maintained; overheads are incurred because related (by graph edges) keys are managed in physically different host machines. We propose a new distributed data-storage paradigm, the keykey- value store, which will extend the key-value model and significantly reduce these overheads by storing related keys in the same place. We provide a high-level description of our proposed system for storing large-scale, highly interconnected graph data - such as social networks - as well as an analysis of our key-key-value system in relation to existing work. In this paper, we show how our novel data organization paradigm will facilitate improved levels of QoS in large graph data stores.
Keywords: graph data, cloud data management, distrbibuted data management
Published In: The 2nd International Workshop on Graph Data Management
Place Published: Hannover, Germany
Year Published: 2011
Project: Others Subject Area: Caching, Web Databases, Database Servers
Publication Type: Workshop Paper
Sponsor: Others