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Ooi Beng Chin 黄铭钧

Databases, Machine Learning and Systems

 
 
 

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In-Memory Big Data Management and Processing: A Survey (TKDE Open Access)  

2014-11-28 10:02:52|  分类: 默认分类 |  标签: |举报 |字号 订阅

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  In-Memory Big Data Management and Processing: A Survey - 黄铭钧  - Ooi Beng Chin   黄铭钧

 Figure 1. Landscape of Modern Database Systems

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer.

Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism and concurrency control. In this survey [1], we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing. Figure 1 describes the landscape of the modern database systems, and in [1]. we shall focus on in-memory systems and large scale Big Data processing.  For a survey on MapReduce like systems, please refer to [2].

  

References:

[1] H. Zhang, G. Chen, B. C. Ooi, K. L. Tan, M. Zhang: In-Memory Big Data Management and Processing: A Survey. IEEE Transactions on Knowledge and Data Engineering. Vol. 27, No. 7, p1920-1948,  2015  (IEEE Open Access)

[2]  F. Li, B. C. Ooi, T. Ozsu, S. Wu: Distributed Data Management Using MapReduce.   ACM Computing Survey, 46(3), January 2014..

[3] K.-L. Tan, Q. Cai, B. C. Ooi, W.F. Wong, C. Yao, H. Zhang: In-memory Databases – Challenges and Opportunities -- From Software and Hardware Perspective. ACM SIGMOD Record, Special Issue on Visionary Ideas in Data Management, June 2015.

[4]  C. Yao, et al.  DGCC: A New Dependency Graph based Concurrency Control Protocol for Multicore Database Systems.  CoRR http://arxiv.org/abs/1503.03642,  12 March 2015

 

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