• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.21-30
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• 2002

The active database system introduces the active rules detecting specified state. As the condition evaluation of the active rules is performed every time an event occurs, the performance of the system has a great influence, depending on the conditions processing method. In this paper, we propose the conditions processing system with the preprocessor which determines the delta tree structure, constructs the classification tree, and generates the aggregate function table. Due to the characteristics of the active database through which the active rules can be comprehended beforehand, the preprocessor can be introduced. In this paper, the delta tree which can effectively process the join, selection operations, and the aggregate function is suggested, and it can enhance the condition evaluation performance. And we propose the classification tree which effectively processes the join operation and the aggregate function table processing the aggregate function which demands high cost. In this paper, the conditions processing system can be expected to enhance the performance of conditions processing in the active rules as the number of conditions comparison decreases because of the structure which is made in the preprocessor.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.5
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• pp.1-10
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• 2002

Efficient processing of expensive queries that include joins and/or aggregate functions is crucial in data warehousing environment since there reside enormous volume of data. In this paper, we propose a new method for processing of queries that have both of joins and aggregate functions. The proposed method first performs grouping of the dimension table and then processes join by using the bitmap join index. This makes only the fact table accessed for processing aggregate functions, and thus resolves the serious performance degradation of the existing method. For showing the superiority of the proposed method, we suggest the cost models for the proposed and existing ones, and perform extensive simulations based on the TPC-H benchmark.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.49-54
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• 1998

AS it takes a considerable time for database operations for processing the condition part of active rule, the operations have an important effect on the efficiency of active database system. The processing time of operations should be minimized in order to improve the efficiency of system. The previous works are limited to basic database operations and the partial aggregate functions. In this paper, the processing technique using the structuralization and the state table of relations is suggested. The processing time for basic database operations can be reduced with the structuralization of relations to classification tree and the introduction of deletion information table. With the introduction of binary search tree and relation state table, the aggregate function which has a big of processing cost can be processed effectively and the function of the active database system can be maximized.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.217-228
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• 2009

Data analysis applications typically aggregate data across many dimensions looking for unusual patterns in data. Even though such applications are usually possible with standard structured query language (SQL) queries, the queries may become very complex. A complex query may result in many scans of the base table, leading to poor performance. Because online analytical processing (OLAP) queries are usually complex, it is desired to define a new operator for aggregation, called the data cube or simply cube. Data cube supports OLAP tasks like aggregation and sub-totals. Many aggregate functions can be used to construct a data cube. Those functions can be classified into three categories, the distributive, the algebraic, and the holistic. It has been thought that the distributive functions such as SUM, COUNT, MAX, and MIN can be used to construct a data cube, and also the algebraic function such as AVG can be used if the function is replaced to an intermediate function. It is believed that even though AVG is not distributive, but the intermediate function (SUM, COUNT) is distributive, and AVG can certainly be computed from (SUM, COUNT). In this paper, however, it is found that the intermediate function (SUM COUNT) cannot be applied to OLAP cubes, and consequently the function leads to erroneous conclusions and decisions. The objective of this study is to identify some problems in applying aggregate function AVG to OLAP cubes, and to design a process for solving these problems.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.121-123
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• 2001

대용량의 데이터가 저장되는 데이터 웨어하우징 환경에서는 조인이나 집계 함수와 같은 고비용의 연산의 효율적인 처리는 매우 중요하다. 본 논문에서는 집계 함수(aggregate function)와 조인이 모두 포함된 질의를 처리하는 새로운 기법을 제안한다. 제안하는 기법은 먼저 차원 테이블(dimension table)을 미리 그룹핑한 후, 비트맵 조인 인덱스(bitmap join index)를 이용하여 조인을 처리하는 방식을 사용한다. 이 결과, 사실 테이블만을 접근하여 집계 함수를 처리함으로써 기존 기법이 가지는 성능 저하의 문제점을 해결할 수 있다. 기존 기법과 제안하는 기법에 대한 비용 모델(cost model)을 정립하고, 이를 기반으로 시뮬레이션을 수행함으로써 제안된 기법의 우수성을 규명한다.

• Journal of Korean Society of Transportation
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• v.8 no.2
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• pp.119-131
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• 1990

Physico-chemical properties of asphalt, aggregate, and asphalt-aggregate mixture that might influence stripping were summarized in Table 1, based on the fundamental theories concerning stripping. It was found that although physical properties of aggregate affected stripping, there was no strong correlation between the physical properties of aggregate, such as pore volume and surface area, and the stripping propensity of the aggregate. Chemical and electrochemical properties of aggregate surface in the presence of water were most important factors for stripping. All mineral aggregates tested in this study imparted distinctive pH values to the contacting water and possessed distinctive electrochemical properties as measured by zeta potential. It was found that aggregates which had relatively higher surface potential in water and/or which imparted relatively higher pH to the contacting water were more susceptible to stripping. The functionalities contained in antistripping additives tested were primary and secondary amines and those of organic nitrogen compounds. The functionalities were determined by examining their infrared spectra. Based on the interfacial energy concept, the contact angle of an asphalt drop on an aggregate surface immersed in water related to the stripping propensity. The contact angle and stripping propensity were markedly reduced by the presence of an antistripping additive. In general, all the additives tested improved stripping resistance to some extent, depending on their concentration in the asphalts. The optimum dosage of an additive varied with different asphalts, as well as different aggregates. All antistripping additives tested in this study lost their effectiveness and failed to function to some extent when maintained for hours in a hot asphalt.

• Journal of Technology Innovation
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• v.8 no.1
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• pp.73-94
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• 2000

The main objective of this research is to examine the spillover effects of technological knowledge from IT industry to other industrial sectors and, based on empirical findings, to draw policy implications and suggest policy directions. To this end, we divide IT industry into IT equipment and IT service, assuming that these two sub-sectors are considerably different each other in terms of technology knowledge flow. Other industries are classified into 17 different sectors based on the KSIC of 1990. As the proxy measure of technological knowledge, the notion of R&D stock is employed. The Input/output(I/O) Table is used to define the inter-industrial flow pattern and to draw the knowledge flow matrix. As the research methodology, cost function model is employed to gauge the spillover effects of technological knowledge of IT industry. Based on the results of analysis, it is found that the economic impact of technology diffusion also exhibits a different pattern between IT equipment and IT service. The diffusion of IT equipment tends to show labor-substitution effect whereas IT service displays labor-creation effect. This fact should be considered in devising industry, education, and labor policy. The expectations from this research are as follows. First, the sectoral pattern, difference between IT equipment and service in particular, identified from this research may shed light on the sector-specific policy direction. It is emphasized that a sector-specific approach, rather than an aggregate approach, is relevant for formulating IT policy. Second, it is expected that the importance of technology diffusion programs and policy measures are recognized among policy makers in IT industry.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.183-193
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• 2009

Both On-Line Analytical Processing (OLAF) data cubes and Statistical Databases (SDBs) deal with multidimensional data sets. and both are concerned with statistical summarizations over the dimensions of the data sets. However, there is a distinction between the two that can be made. While SDBs are usually derived from other base data, OLAF data cubes often represent directly the base data. In other word, the base data of SDBs are the macro-data, whereas the core cubiod data in OLAF data cubes are the micro-data. The base table in OLAF is used to populate the data cube with values of the measure attribute, and each record in the base tables is used to populate a cell of the core cuboid. The fact that OLAF data cubes mostly represent the micro-data may make some records be absent in the base table. Some cells of the core cuboid remain empty, if corresponding records are absent in the base table. Wang and others proposed a method for securing OLAF data cubes against privacy breaches. They assert that the proposed method does not depend on specific types of aggregation functions. In this paper, however, it is found that their assertion on aggregate functions is wrong whenever any cell of the core cuboid remains empty. The objective of this study is to design an inference control process in OLAF data cubes which rectifying Wang's error.