• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.507-516
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• 2009

In stream data processing system, generally the interval queries are in advance registered in the system. When a data is input to the system continuously, for realtime processing, a query indexing method is used to quickly search queries. Thus, a main memory-based query index with a small storage cost and a fast search time is needed for searching queries. In this paper, we propose a LVC-based(Limited Virtual Construct-based) query index method using a hashing to meet the both needs. In LVC-based query index, we divide the range of a stream into limited virtual construct, or LVC. We map each interval query to its corresponding LVC and the query ID is stored on each LVC. We have compared with the CEI-based query indexing method through the simulation experiment. When the range of values of input stream is broad and there are many short interval queries, the LVC-based indexing method have shown the performance enhancement for the storage cost and search time.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.93-105
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• 2012

A data stream is a series of tuples which are generated in real-time, incessant, immense, and volatile manner. As new information technologies are actively emerging, stream processing methods are being needed to efficiently handle data streams. Especially, finding out an efficient evaluation for a multi-way join would make outstanding contributions toward improving the performance of a data stream management system because a join operation is one of the most resource-consuming operators for evaluating queries. In this paper, in order to evaluate efficiently a multi-way join continuous query, we propose a novel method to decrease the cost of a query by eliminating unsuccessful intermediate results. For this, we propose a matrix-based structure for monitoring data streams and estimate the number of final result tuples of the query and find out unsuccessful tuples by matrix multiplication operations. And then using these information, we process efficiently a multi-way join continuous query by filtering out the unsuccessful tuples in advance before actual evaluation of the query.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5042-5048
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• 2010

In ubiquitous environments, many continuous query processing techniques make use of operator network and sharing methods on continuous data stream generated from various sensors. Since similar continuous queries with the location information intensively occur in specific regions, we suggest a new operator sharing method based on grid partition for the spatial continuous query processing for location-based applications. Due to the proposed method shares moving objects by the given grid cell without sharing spatial operators individually, our approach can not only share spatial operators including similar conditions, but also increase the query processing performance and the utilization of memory by reducing the frequency of use of spatial operators.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.285-290
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• 2004

Processing the continuous queries which perform the identical query in given time interval is required because of the continuous change of location of the moving object. Thus the continuous query may cause loads to the server or network and take the cost of processing time because the continuous query is performed in server as the moving object's location changes. In this paper, we propose the query processing technique to perform the continuous range query that is a sort of continuous query effectively The proposed query processing technique predicts the query result and the validity of query answer to perform the continuous query for reciprocal moving object. And it enables to process effectively the query that moves dynamically.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.249-252
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• 2009

Recently, it is possible for users to acquire necessary data easily as the various schemes of the searching information are developed. Since these data rise continuously like stream data, it is required to extract the appropriate data for the user's needs from the mass data on the internet. In the traditional scheme, they are acquired by processing the user queries after the occurred data are stored at a database. However, it is inefficient to process the user queries over the large volume of continuous data by using the traditional scheme. In this paper, we propose the query processing scheme to extract the data efficiently for the user requirements from the large volume of continuous data. On the proposed scheme, we present the Event-Profile Model to define the data occurrence on the internet as the events and the user's requirements as the profiles. We also show the filtering scheme to process the events and the profiles efficiently.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.297-304
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• 2008

Due to the proliferation of the Internet and intranet, new application domains such as stream data processing have emerged. Stream data is real-timely and continuously generated. In stream data environments, a lot of queries are registered, and then, the arrived data item is evaluated by registered queries. Thus, to accelerate the query performance, diverse continuous query index schemes have been proposed for stream data processing systems. In this paper, we focus on the query index technique for stream data. In general, a stream query contains the range condition. Thus, by using range conditions, the queries can be indexed. In this paper, we propose an efficient query index scheme, called QUISIS, using a modified Interval Skip Lists to accelerate search time. QUISIS utilizes a locality where a value which will arrive in near future is similar to the current value. Through the experimental study, we show the efficiency of our proposed method.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.97-102
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• 2008

A wireless sensor network consists of many sensors that collect and transmit physical or environmental conditions at different locations to a server continuously. Many researches mainly focus on processing continuous queries on real-time data stream. However, they do not concern the problem of storing the historical data, which is mandatory to the historical queries. In this paper, we propose two time-based storage methods to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.69-82
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• 2004

A moving object has a special feature that it's spatial location, shape and size are changed as time goes. These changes of the object accompany the continuous movement that is called the trajectory. In this paper, we propose an index structure that users can retrieve the trajectory of a moving object with the access of a page. We also propose the multi-complex query that is a new query type for trajectory retrieval. In order to prove the excellence of our method, we compare and analyze the performance for query time and storage space through experiments in various environments. It is shown that our method outperforms the existing index structures when processing spatio-temporal trajectory queries on moving objects.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.563-577
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• 2006

A stream processor uses resource sharing method for efficient of limited resource in multiple continuous queries. The previous methods process aggregate queries to consist the level structure. So insert operation needs to reconstruct cost of the level structure. Also a search operation needs to search cost of aggregation information in each size of sliding windows. Therefore this paper uses linear structure for optimization of sliding window aggregations. The method comprises of making decision, generation and deletion of panes in sequence. The decision phase determines optimum pane size for holding accurate aggregate information. The generation phase stores aggregate information of data per pane from stream buffer. At the deletion phase, panes are deleted that are no longer used. The proposed method uses resources less than the method where level structures were used as data structures as it uses linear data format. The input cost of aggregate information is saved by calculating only pane size of data though numerous stream data is arrived, and the search cost of aggregate information is also saved by linear searching though those sliding window size is different each other. In experiment, the proposed method has low usage of memory and the speed of query processing is increased.