• Journal of KIISE:Databases
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• v.35 no.4
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• pp.356-367
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• 2008

As XML is gaining unqualified success in being adopted as a universal data representation and exchange format, particularly in the World Wide Web, the problem of querying XML documents poses interesting challenges to database researcher. Several structural XML query processing methods, including XISS and XR-tree, for past years, have been proposed for fast query processing. However, structural XML query processing has the problem of requiring expensive Join cost for twig path query Recently, sequence matching based XML query processing methods, including ViST and PRIX, have been proposed to solve the problem of structural XML query processing methods. Through sequence matching based XML query processing methods match structured queries against structured data as a whole without breaking down the queries into sub queries of paths or nodes and relying on join operations to combine their results. However, determining the structural relationship of ViST is incorrect because its numbering scheme is not optimized. And PRIX requires many processing time for matching LPS and NPS about XML data trees and queries. Therefore, in this paper, we propose efficient sequence matching method u sing the bottom-up query processing for efficient XML query processing. Also, to verify the superiority of our index structure, we compare our sequence matching method with ViST and PRIX in terms of query processing with linear path or twig path including wild-card('*' and '//').

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.1-11
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• 2000

Up to the present, many GIS logical data models have been proposed to incorporate time dimension into traditional 2-dimensional (2D space) GIS databases. However, these models are difficult to implementation of multi-dimensional GIS and have problems of significant data redundancy and search performance. This paper proposes a new storage strategy and a separate storage structure, that is composed of current database and past database, in order to reduce data redundancy and improve search performance of temporal query. Also we design moving algorithm for migration from current database to past database, and design efficient search algorithm for temporal query.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.53-63
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• 2011

The repetitive occurrence of similar accident is one of the biggest feature in construction disasters. Similar accident cases provide direct information for finding risk of scheduled activities and planning safety countermeasure. Many systems are developed to retrieve and use past accident cases by researchers. However, these researches have some limitations for performing too much retrieval to obtain results considering construction site conditions or not reflecting characteristics of safety planning steps or both. To overcome these limitations, this study proposes accident case retrieval system that can search similar accident cases. It also helps safety planning using information retrieval and building information modeling. The retrieval system extracts BIM objects and composes a query set combining BIM objects with site information DB. With past accident cases DB compares a query set, it seeks the most similar case. And results are provided to safety managers. Based on results of this study, safety managers can reduce excessive query generation. Furthermore, they can be easy to recognize risk of a construction site by obtaining coordinations of objects where similar accidents occurred.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.461-465
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• 2006

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. They need a efficient data management with high reliability and real time processing. The characteristics of these data are a large volume, a short report interval and asynchronous report time. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query to search the signal value of a past time, the historical query to search the signal value of a past tine to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in QNX operating systems. The data communications among the devices are done by Profibus-FMS protocol and the file databases are used for adjusting monitoring frequency and storing signals. The file database adopts a delta version and a periodical back up in due consideration of the resource limit of a small storage and a low computing power in QNX COM(Cabinet Operator Module).

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.280-291
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• 2009

Skyline Query processing is important to wireless sensor applications in order to process multi-dimensional data efficiently. Most skyline researches about sensor network focus on minimizing the energy consumption due to the battery powered constraints. In order to reduce energy consumption, Filtering Method is proposed. Most existing researches have assumed a snapshot skyline query processing and do not consider continuous queries and use data generated in ancestor node. In this paper, we propose an energy efficient method called Bottom up filtering tuple selection for continuous skyline query processing. Past skyline data generated in child nodes are stored in each sensor node and is used when choosing filtering tuple. We also extend the algorithms, called Support filtering tuple(SFT) that is used when we choose the additional filtering tuple. There is a temporal correlation between previous sensing data and recent sensing data. Thus, Based on past data, we estimate current data. By considering this point, we reduce the unnecessary communication cost. The experimental results show that our method outperforms the existing methods in terms of both data reduction rate(DRR) and total communication cost.

• 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.10D no.1
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• pp.45-56
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• 2003

In the wireless environment, according to the development of technology, which is able to obtain location information of spatiotemporal moving object, the various application systems are developed such as vehicle tracking system, forest fire management system and digital battle field system. These application systems need the data model, which is able to represent and process the continuous change of moving object. However, if moving objects are expressed by a relational model, there is a problem which is not able to store all location information that changed per every time. Also, existing data models of moving object have a week point, which constrain the query time to the time that is managed in the database such as past or current and near future. Therefore, in this paper, we propose a data model, which is able to not only express the continuous movement of moving point and moving region but also process the operation at all query time by using shape-change process and location determination functions for past and future. In addition, we apply the proposed model to forest fire management system and evaluate the validity through the implementation result.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.1-20
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• 2013

Over the past generation, data warehousing and online analytical processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP's table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features from both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Specifically, it utilizes an OLAP algebra coupled with a domain specific query optimizer, to map user queries directly to the storage and indexing framework. Experimental results demonstrate that not only does the design improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.252-257
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• 2001

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM, Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly. In this paper we propose a new soft evaluation method for Information retrieval using query splitting relevance feedback model. We also show through performance comparison that query splitting relevance feedback(QSRF) is more efficient and effective than MMM, Paice and P-norm.