• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.736-744
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• 2006

For the handhold devices, minimizing repetitive CPU operations such as multiplications is a major factor for their performances. In this paper, we propose efficient algorithms for finding similar sequences from streaming time-series data such as stock prices, network traffic data, and sensor network data. First, we formally define the problem of similar subsequence matching from streaming time-series data, which is called the stream sequence matching in this paper. Second, based on the window construction mechanism adopted by the previous subsequence matching algorithms, we present an efficient window-based approach that minimizes CPU operations required for stream sequence matching. Third, we propose a notion of window MBR and present two stream sequence matching algorithms based on the notion. Fourth, we formally prove correctness of the proposed algorithms. Finally, through a series of analyses and experiments, we show that our algorithms significantly outperform the naive algorithm. We believe that our window-based algorithms are excellent choices for embedded stream sequence matching in handhold devices.

• ETRI Journal
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• v.31 no.4
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• pp.463-465
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• 2009

In this letter, we present an event stream processing system that can evaluate a pattern query for a data sequence with predicates. We propose a pattern query language and develop a pattern query processing system. In our system, we propose novel techniques for run-time aggregation and negation processing and apply our system to stream data generated from vehicles to monitor unusual driving patterns.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.96-102
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• 2003

Video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching. This paper proposes two new sequence matching techniques for copy detection and compares the performance with color techniques that is the existing techniques. Motion, intensity and color-based signatures are compared in the context of copy detection. Comparison of experimental results are reported on detecting copies of movie clips.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.459-470
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.347-356
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• 2006

A video stream can be represented by a sequence of data points in a multidimensional space. In this paper, we introduce a trend vector that approximates values of data points in a sequence and represents the moving trend of points in the sequence, and present a pattern similarity matching method for data sequences using the trend vector. A sequence is partitioned into multiple segments, each of which is represented by a trend vector. The query processing is based on the comparison of these vectors instead of scanning data elements of entire sequences. Using the trend vector, our method is designed to filter out irrelevant sequences from a database and to find similar sequences with respect to a query. We have performed an extensive experiment on synthetic sequences as well as video streams. Experimental results show that the precision of our method is up to 2.1 times higher and the processing time is up to 45% reduced, compared with an existing method.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.492-501
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• 2009

Information on movement of objects in videos can be used as an important part in categorizing and separating the contents of a scene. This paper is proposing a shape-based movement-matching algorithm to effectively find the movement of an object in video streams. Information on object movement is extracted from the object boundaries from the input video frames becoming expressed in continuous 2D shape information while individual 2D shape information is converted into a lD shape feature using the shape descriptor. Object movement in video can be found as simply as searching for a word in a text without a separate movement segmentation process using the sequence of the shape descriptor listed according to order. The performance comparison results with the MPEG-7 shape variation descriptor showed that the proposed method can effectively express the movement information of the object and can be applied to movement search and analysis applications.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.497-502
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• 2003

Content-based video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching which is based on key frame detection. This paper proposes a motion directional histogram, which is quantized and accumulated the direction of motion, for video copy detection. The video clip is represented by a motion directional histogram as a 1-dimensional graph. This method is suitable for real time indexing and counting the TV CF verification that is high motion video clips.