DocumentCode :
249437
Title :
BigObject Store: In-Place Computing for Interactive Analytics
Author :
Yi-Cheng Huang ; Wenwey Hseush ; Yu-Chun Lai ; Fong, Michael
fYear :
2014
fDate :
June 27 2014-July 2 2014
Firstpage :
538
Lastpage :
545
Abstract :
In order to address real-time analytic problems for big data, we have introduced a technology, In-Place Computing, a set of principles for storing and computing big data. It defines an abstract model in which data objects live and work on a flat and infinite address space. The term in-place indicates data is ready for computing in two ways: (1) in-memory and data-centric, in which computations take place where data resides, and (2) well-organized data objects, which have algebraic expressions and operators. Based on these principles, we have developed an in-place computing system, BigObject Store, which implements an extended relational model in the sense allowing various shapes of macro objects besides tables and meanwhile supporting algebraic operators besides join. An abstraction, Macro Data Structure, is defined and a programming paradigm is used to support data-centric computing. Our experiments show that BigObject delivers promising results on typical analytic tasks.
Keywords :
Big Data; data structures; Big Data; BigObject store; data-centric computing; extended relational model; in-memory database; in-place computing system; interactive analytics; macro data structure; well-organized data objects; Algebra; Big data; Computational modeling; Data models; Indexes; Programming; Real-time systems; NoSQL; big data; in-memory database; in-place computing; interactive analytics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
Type :
conf
DOI :
10.1109/BigData.Congress.2014.84
Filename :
6906826
Link To Document :
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