• Journal of KIISE:Databases
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• v.27 no.2
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• pp.256-268
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• 2000

Multi-way spatial join is a nested expression of two or more spatial joins. It costs much to process multi-way spatial join, but there have not still reported the scheme of parallel processing of multi-way spatial join. In this paper, parallel processing of multi-way spatial join consists of parallel multi-way spatial filter and parallel spatial refinement. Parallel spatial refinement is executed by the following two steps. The first is the generation of a graph used for reducing duplication of both spatial objects and spatial operations from pairs candidate object table that are the results of multi-way spatial filter. The second is the parallel spatial refinement using that graph. Refinement using the graph is proved to be more efficient than the others. In task creation for parallel refinement, minimum duplication partitioning of the Spatial_Obicct_On_Node graph shows best performance.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.593-605
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• 2003

Since spatial join processing consumes much time, several algorithms have been proposed to improve spatial join performance. The M-way R-tree join (MRJ) is a join algorithm which synchronously traverses M R-trees in the M-way spatial join. In this paper, we introduce indirect predicates which do not directly come from the multi-way join conditions but are indirectly derived from them. By applying the concept of indirect predicates to MRJ, we improve the performance of MRJ. We call such a multi-way R-tree join algorithm using indirect predicates indirect predicate filtering (IPF). Through experiments using synthetic data and real data, we show that IPF significantly

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.93-105
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• 2012

A data stream is a series of tuples which are generated in real-time, incessant, immense, and volatile manner. As new information technologies are actively emerging, stream processing methods are being needed to efficiently handle data streams. Especially, finding out an efficient evaluation for a multi-way join would make outstanding contributions toward improving the performance of a data stream management system because a join operation is one of the most resource-consuming operators for evaluating queries. In this paper, in order to evaluate efficiently a multi-way join continuous query, we propose a novel method to decrease the cost of a query by eliminating unsuccessful intermediate results. For this, we propose a matrix-based structure for monitoring data streams and estimate the number of final result tuples of the query and find out unsuccessful tuples by matrix multiplication operations. And then using these information, we process efficiently a multi-way join continuous query by filtering out the unsuccessful tuples in advance before actual evaluation of the query.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.459-468
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• 2008

A data stream which is a massive unbounded sequence of data elements continuously generated at a rapid rate. Many recent research activities for emerging applications often need to deal with the data stream. Such applications can be web click monitoring, sensor data processing, network traffic analysis. telephone records and multi-media data. For this. data processing over a data stream are not performed on the stored data but performed the newly updated data with pre-registered queries, and then return a result immediately or periodically. Recently, many studies are focused on dealing with a data stream more than a stored data set. Especially. there are many researches to optimize continuous queries in order to perform them efficiently. This paper proposes a query optimization algorithm to manage continuous query which has multiple join operators(Multi-way join) over data streams. It is called by an Extended Greedy query optimization based on a greedy algorithm. It defines a join cost by a required operation to compute a join and an operation to process a result and then stores all information for computing join cost and join cost in the statistics catalog. To overcome a weak point of greedy algorithm which has poor performance, the algorithm selects the set of operators with a small lay, instead of operator with the smallest cost. The set is influenced the accuracy and execution time of the algorithm and can be controlled adaptively by two user-defined values. Experiment results illustrate the performance of the EGA algorithm in various stream environments.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.297-306
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• 2008

MJoin is proposed as an algorithm to join multiple data streams efficiently, whose characteristics are unpredictably changed. It extends a symmetric hash join to handle multiple data streams. Whenever a tuple arrives from a remote stream source, MJoin checks whether all of hash tables have matching tuples. However, when a join involves many data streams with low join selectivity, the performance of this checking process is significantly influenced by the checking order of hash tables. In this paper, we propose a BiHT-Join algorithm which extends MJoin to conduct this checking in a constant time regardless of a join order. BiHT-Join maintains a bit-vector which represents the existence of tuples in streams and decides a successful/unsuccessful join through comparing a bit-vector. Based on the bit-vector comparison, BiHT-Join can conduct a hash join only for successful joining tuples based on this decision. Our experimental results show that the proposed BiHT-Join provides better performance than MJoin in the processing of multiple streams.

• 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 KIISE:Databases
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• v.29 no.3
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• pp.180-192
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• 2002

We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new harsh join methods in the shared-nothing multiprocessor environment. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets dynamically via a frequency distribution. Using harsh-based joins, multiple joins can be pipelined to that the early results from a join, before the whole join is completed, are sent to the next join processing without staying in disks. Shared nothing multiprocessor architecture is known to be more scalable to support very large databases. However, this hardware structure is very sensitive to the data skew. Unless the pipelining execution of multiple hash joins includes some dynamic load balancing mechanism, the skew effect can severely deteriorate the system performance. In this parer, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.779-782
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• 2014

맵리듀스(MapReduce)는 대용량 데이터의 병렬 처리에 사용되는 프로그래밍 모델이다. 조인(join)은 둘 이상의 테이블에서 동일한 애트리뷰트 값을 가지는 레코드들을 결합하는 연산으로, 데이터베이스 분야에서 가장 중요한 연산 중 하나이다. 본 논문은 맵리듀스를 이용하여 다중 조인(multi-way)을 효율적으로 처리하는 방법을 제안한다. n개 테이블의 다중 조인을 처리하기 위해 기존 방법은 2-way 조인을 수행하는 맵리듀스 잡을 (n-1)번 수행하거나, 레코드들을 중복시켜 n개 테이블의 조인을 1 개의 맵리듀스 잡으로 한 번에 처리한다. 하지만 전자는 맵리듀스 잡을 (n-1)번 수행해야 하며, 후자는 레코드들을 상당히 많이 중복시켜야 한다는 단점이 있다. 본 논문은 레코드를 전혀 중복시키지 않고도 ${\lceil}{\log}_2n{\rceil}$개의 맵리듀스 잡만으로 다중 조인을 효율적으로 처리하는 방법을 제안한다. 실험을 통해 제안 방법은 기존 방법에 대해 다중 조인을 더 빠르게 처리함을 보인다.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.755-768
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• 2002

We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new hash join methods with load balancing capabilities. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets adaptively via a frequency distribution. Using hash-based joins, multiple joins can be pipelined so that the early results from a join, before the whole join is completed, are sent to the next join processing without staying on disks. Unless the pipelining execution of multiple hash joins includes some load balancing mechanisms, the skew effect can severely deteriorate system performance. In this paper, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.226-229
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• 2007

스트리밍 환경에서 조인 연산은 기존의 기법과는 다른 처리 방법을 요구한다. 이런 문제를 해결 하기 위해 기존에 여러 가지의 다양한 기법들이 제안되었다. 하지만 지금까지 제안된 방법들은 두 개의 입력 스트림에 대한 조인만 고려하거나 단일 속성 멀티 스트림 조인에 대해서만 고려해왔다. 하지만 조인 속성이 여러개인 경우에는 한단계로 조인을 수행하는 것은 불가능하다. 이 눈문에서는 이러한 문제를 해결하기 위해서 지금까지 고려되어 왔던 환경에서 더 일반화 된 다중속성을 가지는 조인을 고려한다. 이러한 경우에는 조인이 다단계로 일어나게 되는데 이러한 환경에서는 이전 단계의 조인이 다음 단계의 조인에 영향을 미치게 된다. 그러므로 최종 조인 결과를 빠르게 만들어 내기 위해서는 여러 입력 스트림 중에서 어떤 조인을 먼저 수행하느냐에 따라 전체적인 조인결과를 만들어내는 속도가 달라지게 된다. 그러므로 전체 조인결과를 빠르게 만들어 내기 위해서 조인이 수행되는 과정에서 여러 입력 스트림중에 어떤 스트림을 먼저 수행할지를 결정함으로써 최종 조인 결과를 빠르게 만들어낼 수 있게 하는 방법을 제안한다.