• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.140-150
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• 2024

Database-as-a-Service is one of the prime services provided by Cloud Computing. It provides data storage and management services to individuals, enterprises and organizations on pay and uses basis. In which any enterprise or organization can outsource its databases to the Cloud Service Provider (CSP) and query the data whenever and wherever required through any devices connected to the internet. The advantage of this service is that enterprises or organizations can reduce the cost of establishing and maintaining infrastructure locally. However, there exist some database security, privacychallenges and query performance issues to access data, to overcome these issues, in our recent research, developed a database security model using a deterministic encryption scheme, which improved query execution performance and database security level.As this model is implemented using a deterministic encryption scheme, it may suffer from chosen plain text attack, to overcome this issue. In this paper, we proposed a new model for cloud database security using nondeterministic encryption, order preserving encryption, homomorphic encryptionand database distribution schemes, andour proposed model supports execution of queries with equality check, range condition and aggregate operations on encrypted cloud database without decryption. This model is more secure with optimal query execution performance.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• Journal of KIISE:Databases
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• v.27 no.1
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• pp.13-21
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• 2000

Online analytical processing (OLAP) systems are introduced to support decision support systems. Many researches focussed on efficient processing of aggregate functions that usually occur in OLAP queries. However, most previous researches in the literature are deal with the situation in which aggregate functions arc applied to all the values in a given range. Since those approaches utilize characteristic of aggregate functions applied to a range, they are difficult to be applied to a muitipoint query that is a query considering only some points in a given range. In this paper, we propose the Ranking Index and the flanking Decision Tree (RDT) for efficient evaluation of multipoints MAX/MIN queries. The ranking of possible MAX/MIN values are computed with RDT Then MAX/MIN values can be acquired from the Ranking Index. We show through experiments that our method provides high performance in most situations. In other words, the proposed method is robust as well as efficient. A single common set of precomputed results for both MAX and MIN values is another advantage of the proposed method.

• 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.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.1-11
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• 2005

An aggregation query computes aggregate information over a data cube in the query range specified by a user Existing methods based on the prefix-sum approach use an additional cube called the prefix-sum cube(PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices. In this paper, we propose a new prefix-sum cube called 'SPEC' which drastically reduces the space of the PC in a large data warehouse. The SPEC decreases the update propagation caused by the dependency between values in cells of the PC. We develop an effective algorithm which finds dense sub-cubes from a large data cube. We perform an extensive experiment with respect to various dimensions of the data cube and query sizes, and examine the effectiveness and performance ot our proposed method. Experimental results show that the SPEC significantly reduces the space of the PC while maintaining a reasonable query performance.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.163-165
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• 2001

데이터웨어하우스 환경에서는 범위-집계 질의를 효율적으로 수행하기 위해 데이터큐브로 저장뷰를 구성한다. 큐브트리란 이러한 저장뷰를 R-Tree형태로 구성하는 기법으로, 효율적인 데이터 접근성을 제공하지만 범위-집계 질의 범위 내의 모든노드를 접근해야 하는 단점이 있다. 이 논문에서는 중간노드의 MBR에 자식 노드 레코드들의 집단함수 값을 저장하여, 질의 범위에 포함되는 중간노드의 경우 단말노드를 접근하지 않고 효율적으로 범위-집계 질의를 수행할 수 있는 집계큐브트리를 제안하였다. 집계큐브트리는 기존의 큐브트리에 비해, 항상 적은 수의 노드 접근으로 질의를 수행하며 질의 범위의 크기가 커질수록 좋은 성능을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1207-1210
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• 2013

최근 들어 맵리듀스는 빅데이터 처리의 표준 기술로 자리잡고 있다. 빅데이터 분석에 널리 쓰이는 질의 중 하나는 집계(aggregate) 질의이다. 본 논문에서는 서로 다른 집계 질의가 계속적으로 요청되는 환경에서, 맵리듀스를 사용하여 이들 질의를 효율적으로 처리하는 방법을 제안한다. 제안 방법은 여러 집계 질의를 하나의 효율적인 맵리듀스 잡(job)으로 묶어 일괄 처리함으로써, 단순 방법에 비해 시간당 처리되는 질의 수를 크게 증가시킨다. 성능 평가를 통해, 제안 방법은 단순 방법에 비해 처리 성능을 크게 향상시킴을 확인하였다.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.573-589
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• 2017

Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.32-41
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• 2003

In this paper, we implemented a looms management system which supports remote monitoring and scientific management of the looms. In the implemented system, the layout of the looms is placed in the user interface, and each loom's operating state and rate are automatically represented there. The implemented system has aggregate query processing functions for the looms existing in the selected area by the louse and it also has high level query processing functions to support the 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.