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SIGMOD
2009
ACM

GAMPS: compressing multi sensor data by grouping and amplitude scaling

9 years 9 months ago
GAMPS: compressing multi sensor data by grouping and amplitude scaling
We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L) error . The problem arises naturally in applications that need to collect large amounts of data from multiple concurrent sources, such as sensors, servers and network routers, and archive them over a long period of time for offline data mining. We present GAMPS, a general framework that addresses this problem by combining several novel techniques. First, it dynamically groups multiple signals together so that signals within each group are correlated and can be maximally compressed jointly. Second, it appropriately scales the amplitudes of different signals within a group and compresses them within the maximum allowed reconstruction error bound. Our schemes are polynomial time O(, ) approximation schemes, meaning that the maximum (L) error is at most and it uses at most times the optimal...
Sorabh Gandhi, Suman Nath, Subhash Suri, Jie Liu
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2009
Where SIGMOD
Authors Sorabh Gandhi, Suman Nath, Subhash Suri, Jie Liu
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