• Journal of KIISE:Databases
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• v.33 no.2
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• pp.214-223
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• 2006

Recently there is a need to store and process enormous spatial data in spatio-temporal databases. For effective query processing in spatio-temporal databases, selectivity estimation in query optimization techniques, which approximate query results when the precise answer is not necessary or early feedback is helpful, has been studied. There have been selectivity estimation techniques such as sampling-based techniques, histogram-based techniques, and wavelet-based techniques. However, existing techniques in spatio-temporal databases focused on selectivity estimation for future extent of moving objects. In this paper, we construct a new histogram, named T-Minskew, for query optimization of past spatio-temporal data. We also propose an effective selectivity estimation method using T-Minskew histogram and effective histogram maintenance technique to prevent frequent histogram reconstruction using threshold.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.23-30
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• 2004

In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

• Journal of KIISE:Databases
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• v.29 no.3
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• pp.217-229
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• 2002

Regular path expressions are primary elements for formulating queries over the semistructured data that does not assume the conventional schemas. In addition, the query pruning is an important optimization technique to avoid useless traversals in evaluating regular path expressions. However, the existing query pruning often fails to fully optimize multiple regular path expressions, and the previous methods that post-process the result of the existing query pruning must check exponential combinations of sub-results. In this paper, we present a new query pruning technique that consists of the preprocessing phase and the pruning phase. Our two-phase query pruning is affective in optimizing multiple regular path expressions, and is more scalable than the previous methods in that it never check the exponential combinations of sub-results.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.7-8
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• 2007

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.75-85
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• 2008

The DBMS are widely used in embedded systems. The flash memory is used as a storage device of a embedded system. The optimizer of existing database system assumes that the storage device is disk. There is overhead to overwrite on flash memory unlike disk. The block of flash memory should be erased before write. Due to this reason, query optimization model based on disk does not adequate for flash-aware database. Especially embedded system should minimize the consumption of energy, but consumes more energy because of excessive erase operations. This paper proposes new energy based cost model of embedded database and shows the comparison between disk based cost model and energy based cost model.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.585-592
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• 2005

The Semantic Web is being studied as the next step in the evolution of the web. In the environment of the Semantic Web, the information must be understandable computers as well as a just human. So we use ontologies for describing the contents of the web resources. Among such ontologies, OWL is proposed as a recommendation by W3C. OWL data is represented as graph structure and the query is evaluated by traversing each node of the graph. In this paper, we propose the optimization technique based on signature to efficiently process the OWL data. Our approach minimizes traversing each node of the graph in query processing.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.83-94
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• 2004

Recent researches which purport to Integrate distributed information have been concentrated on developing efficient mediator systems that not only provide a high degree of autonomy for local users but also support the flexible integration of required functions for global users. However, there has been little attention on how to evaluate a global query in a mediator. A global query is transformed into a set of its sub-queries and each sub-query is the unit of evaluation in a remote server. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently evaluated sub-queries are materialized in a mediator. Since the Integration schema of a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied, query usage should be carefully monitored to determine the optimized set of materialized sub-queries. Furthermore, as the number of sub-queries increases, the optimization process itself may take too long, so that the optimized set Identified by a long optimization process nay become obsolete due to the recent change of query usage. This paper proposes the adaptive selection of materialized sub-queries such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a query from the past, the accumulated usage frequency of a query decays as time goes by.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.8-21
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• 2009

A wireless sensor network is a computer network which consists of spatially distributed devices, called sensor nodes. In wireless sensor networks, energy efficiency is a key issue since sensor nodes must resides upon limited energy. To retrieve sensor information without dealing with the network issues, a sensor network is treated as conceptual database on which query can be requested. When multiple queries are requested for processing in a wireless sensor network, energy consumption can be significantly reduced if common partial results among similar queries can be effectively shared. In this paper, we propose an energy efficient multi-query processing technique based on the coverage relationship between multiple queries. When a new query is requested, our proposed technique derives an equivalent query from queries running at the moment, if it is derivable. Our technique first computes the set of running queries that may derive a partial result of the new query and then test if this set covers all the result of the new query attribute-wise and tuple-wise. If the result of the new query can be derived from the results of executing queries, the new query derives its result at the base station instead of being executed in the sensor network.

• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.859-870
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• 2009

Despite the surge in the research for keyword search method over relational database, only little attention has been devoted to studying on relational data stream.The research for keyword search over relational data stream is intense interest because streaming data is recently a major research topic of growing interest in the data management. In this regard we first analyze the researches related to keyword search methodover relational data stream, and then this paper focuses on the method of minimizing the join cost occurred while processing keyword search queries. As a result, we propose an advanced keyword search method that can yield more meaningful results for users on relational data streams. We also propose a query optimization method using layered-clustering for efficient query processing.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1418-1430
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• 1998

Temporal databases support an efficient historical management by means of valid time and transaction time. Valid time stands for the time when a data happens in the real world. And transaction time stands for the time when a data is stored in the database, Temporal Query Processing System(TQPS) should be extended so as tc process the temporal operations for the historical informations in the user query as well as the conventional relational operations. In this paper, the extended temporal query processing systems which is based on the previous temporal query processing system for TQuel(Temporal Query Language) consists of the temporal syntax analyzer, temporal semantic analyzer, temporal code generator, and temporal interpreter is to be described, The algorithm for additional functions such as transaction time management, temporal aggregates, temporal views, temporal joins and the heuristic optimization functions and their example how to be processed is shown.