• Journal of the Korean Institute of Rural Architecture
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• v.17 no.2
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• pp.27-34
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• 2015

As part of the waterway restoration to renew traditional urban area, this paper is to assume and research Gyoseocheon(Gyoseo waterway) in Late 1960s Cheongju Korea. The main stream of Gyoseocheon flew from Sangjwagol(the head of the valley) of Uam Mt. to the north gate under Cheongju castle at first, and meandering from Sangdang Park, flew through Sudongseongdang and Bangadari Road to Musimcheon since open railroad of 1920s. This waterway, the eco-friendly figure of Gyoseocheon, that flew to inside the downtown with a planted tree and that is open space was so. The sub stream of Gyoseocheon originated in Seoundong and Tapdong were divided into two parts. One was to join the main stream of Gyoseocheon on Sangdang Park via Munhwadong, and the other was to join the sub stream of Musimcheon at northwest corner via south and west gate of Cheongju castle. This waterway as branch sewer were built into the road culvert.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.149-157
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• 2012

In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

• Journal of Computing Science and Engineering
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• v.3 no.1
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• pp.27-58
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• 2009

Aggregation join queries are an important class of queries over data streams. These queries involve both join and aggregation operations, with window-based joins followed by an aggregation on the join output. All existing research address join query optimization and aggregation query optimization as separate problems. We observe that, by putting them within the same scope of query optimization, more efficient query execution plans are possible through more versatile query transformations. The enabling idea is to perform aggregation before join so that the join execution time may be reduced. There has been some research done on such query transformations in relational databases, but none has been done in data streams. Doing it in data streams brings new challenges due to the incremental and continuous arrival of tuples. These challenges are addressed in this paper. Specifically, we first present a query processing model geared to facilitate query transformations and propose a query transformation rule specialized to work with streams. The rule is simple and yet covers all possible cases of transformation. Then we present a generic query processing algorithm that works with all alternative query execution plans possible with the transformation, and develop the cost formulas of the query execution plans. Based on the processing algorithm, we validate the rule theoretically by proving the equivalence of query execution plans. Finally, through extensive experiments, we validate the cost formulas and study the performances of alternative query execution plans.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.159-175
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• 2013

Recently, as real-time availability of e-commerce data becomes possible, the requirement of real-time analysis of e-commerce increases significantly. In the real-time analysis of e-commerce data, it is very important to efficiently process continuous join queries between an e-commerce data stream and disk-based large relations. In this paper, we propose an efficient method for processing a continuous join query between an e-commerce data stream and multiple disk-based relations. The proposed method improves the service rate significantly, while reducing the amount of required memory substantially. Through analysis and various experiments, we show the efficiency of the proposed method compared with the previous one in terms of service rate and memory usage.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.25-34
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• 2007

Existing approaches that select an order for the join of three or more data streams have always used the simple heuristics. For their disadvantage - only one factor is considered and that is join selectivity or arrival rate, these methods lead to poor performance and inefficiency In some applications. The graph-based sliding window multi -join algorithm with optimal join sequence is proposed in this paper. In this method, sliding window join graph is set up primarily, in which a vertex represents a join operator and an edge indicates the join relationship among sliding windows, also the vertex weight and the edge weight represent the cost of join and the reciprocity of join operators respectively. Then the optimal join order can be found in the graph by using improved MVP algorithm. The final result can be produced by executing the join plan with the nested loop join procedure, The advantages of our algorithm are proved by the performance comparison with existing join algorithms.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.310-326
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• 2006

In this paper, we deal with a method of efficiently processing continuous queries in a data stream environment. We classify previous query processing methods into two dual categories - data-initiative and query-initiative - depending on whether query processing is initiated by selecting a data element or a query. This classification stems from the fact that data and queries have been treated asymmetrically. For processing continuous queries, only data-initiative methods have traditionally been employed, and thus, the performance gain that could be obtained by query-initiative methods has been overlooked. To solve this problem, we focus on an observation that data and queries can be treated symmetrically. In this paper, we propose the duality model of data and queries and, based on this model, present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We also present a continuous query processing algorithm based on spatial join, named Spatial Join CQ. Spatial Join CQ processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries defined as regions in the multi-dimensional space. The algorithm achieves the effects of both of the two dual methods by using the spatial join, which is a symmetric operation. Experimental results show that the proposed algorithm outperforms earlier methods by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join continuous queries.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.189-192
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• 2008

최근에 제한된 데이터 셋보다 센서 데이터 처리, 웹 서버 로그나 전화 기록과 같은 다양한 트랜잭션 로그 분석등과 관련된 대용량 데이터 스트림을 실시간으로 처리하는 것에 많은 관심이 집중되고 있으며, 특히 데이터 스트림의 조인 처리에 대한 관심이 증가하고 있다. 본 논문에서는 조인 연산을 빠르게 처리하기 위한 효율적인 해시 구조와 조인 방법에 대해서 연구하고 다양한 환경에서 제안 방법을 검증한다.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.1-11
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• 2010

Recently, there has been a growing interest in the processing of continuous queries over multiple data streams. When the arrival rates of tuples exceed the memory capacity of the system, a load shedding technique is used to avoid the system becoming overloaded by dropping some subset of input tuples. In this paper, we propose an effective load shedding algorithm for multi-way windowed joins over multiple data streams. Most previous load shedding algorithms estimate the productivity of each tuple, i.e., the number of join output tuples produced by the tuple, based on its "join attribute value" and drop tuples with the lowest productivity. However, the productivity of a tuple cannot be accurately estimated from its join attribute value when the join attribute values are unique and do not repeat, or the distribution of the join attribute values changes over time. For these cases, we estimate the productivity of a tuple based on its "arrival order" on data streams, rather than its join attribute value. The proposed method can effectively estimate the productivity of a tuple even when the productivity of a tuple cannot be accurately estimated from its join attribute value. Through extensive experiments and analysis, we show that our proposed method outperforms the previous methods in terms of effectiveness and efficiency.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.1-8
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• 2024

In the API gateway, API composition is an essential function that can reduce the number of client calls and prevent over-fetching and under-fetching. API composition that operate with IMJ (In-Memory Join) consume a lot of resources, putting a burden on the performance of the API gateway. In this paper, to improve the problem of IMJ-style API composition, we propose SAPIC (Stream-based API Composition), which delivers the data to be composed to the client by streaming. SAPIC calls each MSA API that makes up the client response data and immediately streams the received response data to the client, reducing the resource consumption of the API gateway and providing faster response time compared to IMJ. As a result of a comparison experiment with GraphQL, a representative API combination technology, SAPIC recorded a maximum CPU occupancy rate of approximately 21 to 70 % lower, a maximum heap usage rate of approximately 16 to 74 % lower, and a throughput rate that was 1 to 2.3 times higher than GraphQL.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.220-230
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• 2013

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.