This research is motivated by large-scale pervasive sensing applications. We examine the benefits and costs of caching data for such applications. We propose and evaluate several approaches to querying for, and then caching data in a sensor field data server. We show that for some application requirements (i.e., when delay drives data quality), policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost savings. This win-win is because when system delay is sufficiently important, the benefit to both query cost and data quality achieved by using approximate values outweighs the negative impact on quality due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between query cost and data quality emerges. We also identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a single sensor field query. Finally, we demonstrate that our results are robust to the manner in which the environment being monitored changes using models for two different sensing systems.

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Data Quality and Query Cost in Pervasive Sensing Systems

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques. DOI: 10.4018/978-1-60566-328-9.ch013

Sensor Field Resource Management for Sensor Network Data Mining

We propose a new method of client-side data caching for relational databases with a central server and distant clients. Data is loaded into client cache based on queries executed on the central database at the server. These queries have a special form - “universal relational query”. The majority of search queries to database can be expressed in such form. Besides, this form allows us to analyze query correctness by checking lossless join property. A subsequent query may be executed in client's local cache if we can determine that the query result is entirely contained in the cache. For this we compare truth spaces of the logical restrictions in new user's query and results of the queries execution in cache. This method may be used to define lacking data in cache and execute the query on server for this data only. Problem of data actualizations in cache is not discussed in this paper. However, it can be solved by cataloging of queries on server and their serving by triggers in background mode.

Using logical formulas for caching uniform RDB queries

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Data Quality and Query Cost in Pervasive Sensing Systems