• Journal of KIISE:Databases
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• v.35 no.3
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• pp.231-242
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• 2008

W3C has selected OWL as a standard for Web ontology description and a necessity of research on storage models that can store OWL ontologies effectively has been issued. Until now, relational model-based storage systems such as Jena, Sesame, and DLDB, have been developed, but there still remain several issues. Especially, they lead inefficient query processing performance. The structural problems of their low query processing performance are as follow: Jena has a simple structure which is not normalized and also stores most information in a single table. It exponentially decreases the performance because of comparison with unnecessary information for processing queries requiring join operations as well as simple search. The structures of storages(e.g., Sesame) have been completely normalized. Therefore it executes many join operations for query processing. The storages require many join operations to find simply a specific class. This paper proposes a storage model to resolve the problems that the query processing performance is decreased because of non-normalization or complete normalization of the existing storages. To achieve this goal, we analyze the problems of existing storage models as well as relations of OWL elements and query patterns. The proposed model, defined with the analysis results, provides an optimal normalized structure to minimize join operations or unnecessary information comparison. For the experiment of query processing performance, a LUBM data sets are used and query patterns are defined considering search targets and their hierarchical relations. In addition, this paper conducts experiments on correctness and completeness of query results to verify data loss of the proposed model, and the results are described. With the comparative evaluation results, our proposal showed a better performance than the existing storage models.

• Asia pacific journal of information systems
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• v.8 no.1
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• pp.43-64
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• 1998

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention to the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy(KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance, On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.536-546
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• 2005

In this paper, we focus on the filtering step of candidate objects for spatial join operations on the input tables that none of the inputs is indexed. Over the last decade, several spatial Join algorithms for the input tables with index have been extensively studied. Those algorithms show excellent performance over most spatial data, while little research on solving the performance degradation in the presence of skewed data has been attempted. Therefore, we propose a spatial hash strip join(SHSJ) algorithm that can refine the problem of skewed data in the conventional spatial hash Join(SHJ) algorithm. The basic idea is similar to the conventional SHJ algorithm, but the differences are that bucket capacities are not limited while allocating data into buckets and SSSJ algorithm is applied to bucket join operations. Finally, as a result of experiment using Tiger/line data set, the performance of the spatial hash strip join operation was improved over existing SHJ algorithm and SSSJ algorithm.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.605-614
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• 2007

As a standard query language to search XML data, XQuery and XPath were proposed by W3C. By widely using XQuery and XPath languages, recent researches focus on the development of query processing algorithm and data structure for efficiently processing XML query with the enormous XML database system. Recently, when processing XML path expressions, the concept of the structural join which may determine the structural relationship between XML elements, e.g., ancestor-descendant or parent-child, has been one of the dominant XPath processing mechanisms. However, structural joins which frequently occur in XPath query processing require high cost. In this paper, we propose a new structural join algorithm, called SISJ, based on our structured index, called SI, in order to process XPath queries efficiently. Experimental results show that our algorithm performs marginally better than previous ones. However, in the case of high recursive documents, it performed more than 30% by the pruning feature of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.55-62
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• 2017

Recently, there has been an increasing need for the sharing of microdata containing information regarding an individual entity. As microdata usually contains sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, to avoid the privacy problems that occur through the release of microdata for public use, extensive studies have been conducted in the area of privacy-preserving data publishing (PPDP). The k-anonymity algorithm, which is the most popular method, guarantees that, for each record, there are at least k-1 other records included in the released data that have the same values for a set of quasi-identifier attributes. Given an original table, the corresponding k-anonymous table is obtained by generalizing each record in the table into an indistinguishable group, called the equivalent class, by replacing the specific values of the quasi-identifier attributes with more general values. However, query processing over the anonymized data is a very challenging task, due to generalized attribute values. In particular, the problem becomes more challenging with an equi-join query (which is the most common type of query in data analysis tasks) over k-anonymous tables, since with the generalized attribute values, it is hard to determine whether two records can be joinable. Thus, to address this challenge, in this paper, we develop a novel scheme that is able to effectively perform an equi-join between k-anonymous tables. The experiment results show that, through the proposed method, significant gains in accuracy over using a naive scheme can be achieved.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.1
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• pp.99-117
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• 1999

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine Interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adepts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms using query generalization and specialization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically. four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual Join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.1-16
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• 2008

Join queries having heavy cost are necessary to Data Stream Management System in Sensor Network where plural short information is generated. It is reasonable that each join operator has a sliding-window constraint for preventing DISK I/O because the data stream represents the infinite size of data. In addition, the join operator should be able to take multiple inputs for overall results. It is possible for the MJoin operator with sliding-windows to do so. In this paper, we consider the data stream environment where multiple MJoin operators are registered and propose MMJoin which deals with issues of building and processing a globally shared query considering characteristics of the MJoin operator with sliding-windows. First, we propose a solution of building the global shared query execution plan. Second, we solved the problems of updating a window size and routing for a join result. Our study can be utilized as a fundamental research for an optimization technique for multiple continuous joins in the data stream environment.

• Journal of Computing Science and Engineering
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• v.4 no.4
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• pp.276-290
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• 2010

The join operator is fundamental in relational database systems. Evaluating join queries on large tables is challenging because records need to be efficiently matched based on a given key. In this work, we analyze join queries in SQL with large tables in which a foreign key may be null, invalid or valid, given a referential integrity constraint. We conduct an extensive join performance evaluation on three DBMSs. Specifically, we study join queries varying table sizes, row size and key probabilistic distribution, inserting null, invalid or valid foreign key values. We also benchmark three well-known query optimizations: view materialization, secondary index and join reordering. Our experiments show certain optimizations perform well across DBMSs, whereas other optimizations depend on the DBMS architecture.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.933-942
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• 2017

Purpose: The purpose of this paper is to evaluate the performance quality of the open source DBMSs. Performance quality is defined as processing time for Join queries. Query processing time is measured and compared in the most widely used open source DBMSs and commercial DBMS. Methods: By varying the number of tuples of two relations to be joined, the average processing time(seconds) of a Join query in each DBMS was obtained experimentally. ANOVA and Tukey HSD test were used in order to compare the performance quality of DBMSs. Results: There was a significant difference between the performance qualities of the three DBMSs at all experimental levels where the number of tuples was 100, 1,000, 2,000, 10,000, and 50,000. As a result of the Tukey HSD test, two open source DBMSs (MariaDB, MySQL) were classified in the same group only at the tuple level of 100. The commercial DBMS (MS-SQL Server) belonged to another group. At level of more than 1,000 tuples, all three DBMSs belonged to different groups. Conclusion: Within the open source DBMS group, MariaDB showed the better performance quality except for a small number of tuples. Thus the results show that MariaDB can be the alternative to MySQL which is currently most widely used. Between open source DBMS and commercial DBMS groups, MS-SQL Server always shows the best performance quality, but the less number of tuples, the less the difference.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.9-16
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• 2002

Join operation exert a great effect on the performance of system in temporal database as in the relational database. Especially, as for the temporal join, the optimization of interval partition decides the performance of query processing. In this paper, to improve the efficiency of parallel join query in temporal database. I proposed Minimum Interval Partition(MIP) scheme that time interval partitioning. The validity of this MIP algorithm that decides minimum breakpoint of the partition is proved by example scenario and I confirmed improved efficiency as compared with existing partition algorithm.