# Managing and Mining Sensor Data

## Charu C. Aggarwal

Language: English

Pages: 545

ISBN: 1489992383

Format: PDF / Kindle (mobi) / ePub

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process.

*Managing and Mining Sensor Data* is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.

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Sampling, C(So ) and B(So ) = |S1o | j:sj ∈So Bj (S) are respectively the total cost (or energy required) and average conﬁdence for sampling sensors So . Since the problem in Eq. (2.2) is NP-hard, BBQ proposes a greedy solution to solve this problem. Details of this greedy algorithm can be found in [17]. By executing the proposed greedy algorithm, BBQ selects the sensors for sampling, then it updates the Gaussian distribution, and returns the mean values v¯11 , v¯12 , . . . , v¯1m . These mean.

Raw sensor values. If the sink receives a few sensor values form the source, then, before computing the expected values, the sink updates its dynamic probabilistic model. 2.4.3 A Generic Push-Based Approach. The last pushbased approach that we will survey is a generalized version of other push-based approaches [38]. This approach is proposed by Silberstein et al. [61]. Like other push-based approaches, the base station and the sensor network agree on an expected behavior, and, as usual, the.

Strategies based on time-slot and B+ tree. As an extension of SASE, SASE+ [70] employs an optimization strategy based on pattern match buﬀer sharing to support sharing among intermediate results, thus reduces the maintenance cost of intermediate results. In RCEDA, to process RFID rules, the events from the rules are ﬁrst constructed into an event graph, and then the event graph will be initialized as follows: i) propagate interval constraints in a top-down way; ii) assign event detection modes.

Single variable, if a is the least squares solution, we can express the error in matrix form as EEE({xi }) = y 2 − 2a(yT xi ) + a2 xi 2 . Let d and p denote xi 2 and (xT y), respectively. Since a = d−1 p, EEE({xi }) = y 2 − p2 d−1 . To minimize the error, we must choose xi Dimensionality Reduction and Filtering on Time Series Sensor Streams 119 which maximize p2 and minimize d. Assuming unit-variance (d = 1), such xi is the one with the biggest correlation coeﬃcient to y. This concludes.

Important. When there are a large number of simultaneous data stream, we can use the correlations between diﬀerent data streams in order to make eﬀective predictions [70, 75] on the future behavior of the data stream. In particular, the well known MUSCLES method [75] is useful in applying regression analysis to data streams. The regression analysis is helpful in predicting the future behavior of the data stream. A related technique is the SPIRIT algorithm, which explores the relationship between.