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...

• Type: Conference
• Year: 2009
• Where: SIGMOD
• Authors: Sorabh Gandhi, Suman Nath, Subhash Suri, Jie Liu
• Keywords: Database, Maximum Allowed Reconstruction, National Science Foundation, Science Foundation Grants, SIGMOD 2009