NDLI: Scalable management of storage for massive quality-adjustable sensor data

Content Provider	Springer Nature Link
Author	Lee, Dongeun Ryu, Junhee Shin, Heonshik
Copyright Year	2015
Abstract	The quality-adjustable nature of sensor data and gradually decreasing access pattern foster a new data archiving scheme that can cope with rapidly increasing data generation rates by sensors. We propose a scalable quality management of massive sensor data, which handles less frequent data access through discarding supplementary layers as time elapses for efficient usage of storage space. The efficacy of our scheme is shown by its capability to offer multiple fidelity levels compactly utilizing spatio-temporal correlation, without compromising key features of sensor data. In order to store a huge amount of data from various sensor types efficiently, we also study the optimal storage configuration strategy using analytical models that can capture characteristics of our scheme. This strategy helps storing sensor data blocks while minimizing total distortion under a given total rate budget.
Ending Page	793
Page Count	25
Starting Page	769
File Format	PDF
ISSN	0010485X
e-ISSN	14365057
Journal	Computing
Issue Number	8
Volume Number	97
Language	English
Publisher	Springer Vienna
Publisher Date	2015-06-25
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rate-distortion theory Quality-adjustable sensor data Scalable quality management Optimal rate allocation Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) Information storage and retrieval Data aging Software Engineering Computer Application in Administrative Data Processing Computer Science Artificial Intelligence (incl. Robotics) Big data Information Systems Applications (incl. Internet) Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computational Mathematics Numerical Analysis Computer Science Applications Software

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