Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.

Similarity search in time-series databases finds such data sequences whose changing patterns are similar to that of a query sequence. For efficient processing, it normally employs a multi-dimensional index. In order to alleviate the well-known dimensionality curse, the previous methods for similarity search apply the Discrete Fourier Transform (DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes. Other than this ad-hoc approach, there have been no research efforts on devising a systematic guideline for choosing the best organizing attributes. This paper first points out the problems occurring in the previous methods, and proposes a novel solution to construct optimal multi-dimensional indexes. The proposed method analyzes the characteristics of a target time-series database, and identifies the organizing attributes having the best discrimination power. It also determines the optimal number of organizing attributes for efficient similarity search by using a cost model. Through a series of experiments, we show that the proposed method outperforms the previous ones significantly.

Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.52.8 times compared with the previous method.

Recently, many studies reported various advanced e-Health service systems in patient care monitoring utilizing sensor networks and questionnaire systems. We propose an informant driven e-Health service system for the identification of heart rate related mental stress factors with a simple operation of informant-client model. Through performance analysis, we show that the proposed system is a cost-effective stress identification system applicable to mobile wireless networks.

In recent years, a horizontal table with a large number of attributes is widely used in OLAP or e-business applications to analyze multidimensional data efficiently. For efficient storing and querying of horizontal tables, recent works have tried to transform a horizontal table to a traditional vertical table. Existing works, however, have the drawback of not considering an optimized PIVOT operation provided (or to be provided) in recent commercial RDBMSs. In this paper we propose a formal approach that exploits the optimized PIVOT operation of commercial RDBMSs for storing and querying of horizontal tables. To achieve this goal, we first provide an overall framework that stores and queries a horizontal table using an equivalent vertical table. Under the proposed framework, we then formally define 1) a method that stores a horizontal table in an equivalent vertical table and 2) a PIVOT operation that converts a stored vertical table to an equivalent horizontal view. Next, we propose a novel method that transforms a user-specified query on horizontal tables to an equivalent PIVOT-included query on vertical tables. In particular, by providing transformation rules for all five elementary operations in relational algebra as theorems, we prove our method is theoretically applicable to commercial RDBMSs. Experimental results show that, compared with the earlier work, our method reduces storage space significantly and also improves average performance by several orders of magnitude. These results indicate that our method provides an excellent framework to maximize performance in handling horizontal tables by exploiting the optimized PIVOT operation in commercial RDBMSs.

The current version of IEEE 802.15.4 MAC protocol does not support energy-efficient mobility for the low-power device. In this paper, we propose an energy-efficient sleep mode as part of the IEEE 802.15.4 that can conserve energy by considering mobility of mobile sensor devices. The proposed energy-efficient sleep mode dynamically extends the sleep interval if there is no data to transmit from the device or receive from corresponding nodes.