• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.69-76
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• 1999

Recently, it has been studied the methods to materialize and precompute the query results for complexed spatial aggregation queries with high response time and the popular use in spatial data warehouse. In this paper, we propose extended selective materialization algorithm and present the way to materialize selectively which is considered access frequency and computation time of spatial operation according to spatial measures of spatial views for improvement of existing selective materialization algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.5
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• pp.419-433
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• 2004

As XML becomes a standard for data representation, integration, and exchange on the Web, several XML query languages have been proposed. World Wide Web Consortium(W3C) has proposed XQuery as a standard for the XML query language. Like SQL, XQuery allows nested queries. Thus, normalization rules have been proposed to transform nested XQuery queries to semantically equivalent ones that could be executed more efficiently. However, previous normalization rules are applicable only to restricted forms of nested XQuery queries. Specifically, they can not handle FLWR expressions having nested expressions in the where clause. In this paper, we propose normalization rules for XQuery queries by extending those for SQL queries. Our proposed rules can handle FLWR expressions haying nested expressions in every clause. The major contributions of this paper are as follows. First, we classily nesting types of XQuery queries according to the existence of correlation and aggregation. We then propose normalization rules for each nesting type. Second, we propose detailed algorithms that apply the normalization rules to nested XQuery queries.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.501-512
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• 2006

XML is the most popular platform-independent data expression which is used to communicate between loosely coupled heterogeneous systems such as B2B Applications or Workflow systems. The powerful query language XQuery has been developed to support diverse needs for querying XML documents. XQuery is designed to configure results from diverse data sources into a uniquely structured query result. Therefore, it became the standard for the XML query language. Although the latest XQuery supports heavy search functions including iterations, the grouping mechanism for data is too primitive and makes the query expression difficult and complex. Therefore, this work is focused on supporting the groupby clause in the query expression to process XQuery grouping. We suggest it to be a more efficient way to process grouping for restructuring and aggregation functions on XML data. We propose an XQuery EBNF that includes the groupby clause and implemented an XQuery processing system with grouping functions based on the eXist Native XML Database.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.23-28
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• 2006

In this paper, a power-aware query processing using optimized distributed R-tree in a sensor network is proposed. The proposed technique is a new approach for processing range queries that uses spatial indexing. Range queries are most often encountered under sensor networks for computing aggregation values. The previous work just addressed the importance but didn't provide any efficient technique for processing range queries. A query processing scheme is thus designed for efficiently processing them. Each node in the sensor network has the MBR of the region where its children nodes and the node itself are located. The range query is evaluated over the region which intersects the geographic location of sensors. It ensures the maximum power savings by avoiding the communication of nodes not participating over the evaluation of the query.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.389-391
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• 1998

데이터 웨어하우스에서는 시간에 따라 변화되는 데이터를 관리함으로써 좀더 정확하게 요약화된 정보를 제공할 수 있다. 거의 모든 데이터 웨어하우스는 원시 데이터로 관계형 데이터베이스를 사용하지만, 관계형 데이터베이스는 시간 데이터에 대해 실제적인 지원을 하지 않는다. 그러므로 시간 변이 데이터에 대한 정확한 정보를 얻기가 어렵다. 본 논문에서는 이러한 시간 변이 데이터의 지원이 가능한 시간지원 데이터 웨어하우스를 설계하고자 한다. 이를 위해, 기존의 데이터 웨어하우스에서 원시 데이터로 사용하는 관계형 데이터베이스에 시간지원질의 처리 계층을 결합하는 방법을 보이고, 시간지원 데이터의 간격 시간에 대한 요약화 방법으로 시간지원 집계 트리 전략을 소개한다.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.451-463
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• 2003

Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on huge amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi~tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called Hilbert Cube(H-Cube), which employs fractals in order to impose a total-order on cells. In addition, the H-Cube takes advantage of the traditional Prefix-sum approach to improve Query efficiency significantly. The H-Cube partitions an embedding space into a set of cells which are clustered on disk by Hilbert ordering, and then composes a cube by arranging the grid cells in a chronological order. The H-Cube refines cells adaptively to handle regional data skew, which may change its locations over time. The H-Cube is an adaptive, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our approach focuses on indexing dynamic point objects in static spatial dimensions. Through the extensive performance studies, we observed that The H-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree structures.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.99-109
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• 2002

ROLAP(Relational Online Analytical Processing) is a process and methodology for a multidimensional data analysis that is essential to extract desired data and to derive value-added information from an enterprise data warehouse. In order to speed up query processing, most ROLAP systems pre-compute summary tables. This process is called 'cube generation' and it mostly involves intensive table sorting stages. (1) showed that it is much faster to generate ROLAP summary tables indirectly using a MOLAP(multidimensional OLAP) cube generation algorithm. In this paper, we present such an indirect ROLAP cube generation algorithm that is fast and scalable. High memory utilization is achieved by slicing the input fact table along one or more dimensions before generating summary tables. High speed is achieved by producing summary tables from their smallest parents. We showed the efficiency of our algorithm through experiments.

• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.387-392
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• 2015

MapReduce is a framework used to process large data sets in parallel on a large cluster. A group-by query is a query that partitions the input data into groups based on the values of the specified attributes, and then evaluates the value of the specified aggregate function for each group. In this paper, we propose an efficient method for processing multiple group-by queries using MapReduce. Instead of computing each group-by query independently, the proposed method computes multiple group-by queries in stages with one or more MapReduce jobs in order to reduce the total execution cost. We compared the performance of this method with the performance of a less sophisticated method that computes each group-by query independently. This comparison showed that the proposed method offers better performance in terms of execution time.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.512-519
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• 2002

In this paper, we implemented a loom component to be placed in a window and a looms management system which is able to connect database and to process various queries. The implemented system has aggregate query p개cessing functions for the loom components existing in the selected area by the mouse and it also has high level query processing functions represented with chart and pivot table; it can be used as a decision support system. The proposed system can detect temporal or persistent problems of the looms. Therefore it can be used to raise the productivity and to reduce the cost in textile companies by coping with the situation properly.