• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.1-12
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• 2009

An RFID application needs to collect product's information scattered over the RFID network efficiently according to the globalization of RFID applied enterprises. To be informed of the stock status of products promptly in the supply chain network, especially, it is necessary to support queries that retrieve statistical information of tagged products. Since existing RFID network does not provide these kinds of queries, however, an application should request a query to several RFID middleware systems and analyze collected data directly. This process makes an application do a heavy computation for retrieving statistical information. To solve this problem, we define a new Distributed Continuous Query that finds information of tagged products from the global RFID network and provides statistical information to RFID applications. We also propose a Distributed Continuous Query System to process the distributed continuous query efficiently. To find out the movement of products via multiple RFID systems in real time, our proposed system uses Pedigree which represents trade information of items. Our system can also reduce the cost of query processing for removing duplicated data from multiple middleware systems by using Pedigree.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.563-577
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• 2006

A stream processor uses resource sharing method for efficient of limited resource in multiple continuous queries. The previous methods process aggregate queries to consist the level structure. So insert operation needs to reconstruct cost of the level structure. Also a search operation needs to search cost of aggregation information in each size of sliding windows. Therefore this paper uses linear structure for optimization of sliding window aggregations. The method comprises of making decision, generation and deletion of panes in sequence. The decision phase determines optimum pane size for holding accurate aggregate information. The generation phase stores aggregate information of data per pane from stream buffer. At the deletion phase, panes are deleted that are no longer used. The proposed method uses resources less than the method where level structures were used as data structures as it uses linear data format. The input cost of aggregate information is saved by calculating only pane size of data though numerous stream data is arrived, and the search cost of aggregate information is also saved by linear searching though those sliding window size is different each other. In experiment, the proposed method has low usage of memory and the speed of query processing is increased.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.1-16
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• 2008

Join queries having heavy cost are necessary to Data Stream Management System in Sensor Network where plural short information is generated. It is reasonable that each join operator has a sliding-window constraint for preventing DISK I/O because the data stream represents the infinite size of data. In addition, the join operator should be able to take multiple inputs for overall results. It is possible for the MJoin operator with sliding-windows to do so. In this paper, we consider the data stream environment where multiple MJoin operators are registered and propose MMJoin which deals with issues of building and processing a globally shared query considering characteristics of the MJoin operator with sliding-windows. First, we propose a solution of building the global shared query execution plan. Second, we solved the problems of updating a window size and routing for a join result. Our study can be utilized as a fundamental research for an optimization technique for multiple continuous joins in the data stream environment.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.7-9
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• 2004

센서 네트워크의 급속한 성장에 따라 센서 네트워크의 효율적 관리를 위한 다양한 연구가 진행 중이다. 특히, 센서의 저 전력을 위한 다양한 기술들이 개발되고 있다. 본 논문은 센서에 전해지는 다중 연속 질의의 중복 성을 제거함으로써, 센서 네트워크의 효율적 관리를 제공할 수 있는 방법을 제안한다. 다중 연속 질의는 두 가지 단계로 최적화가 이루어진다. 먼저, 다중 연속 질의의 시간 속성 중복을 제거하기 위해 B+tree를 이용해 그룹 핑된다. 그룹 핑된 다중 연속 질의들은 연관 속성의 중복 여부 판단을 통해, 중복 성을 제거하여 재구성 된다. 그러므로 재구성된 다중 연속 질의가 센서 노드에 전해지게 되면, 센서는 중복된 결과를 전송하지 않기 때문에 센서 노드의 불필요한 전력을 낭비하지 않게 된다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.25-28
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• 2007

무한히 연속적으로 발생하는 데이터 스트림에서의 연속 질의 처리는 빠른 처리 시간과 적은 메모리 사용량을 요구한다. 이런 제약 사항을 만족하기 위해 연속 질의의 선택 조건절에 사용된 같은 속성들로 그룹화하여 해당 속성들을 처리함으로써 빠르게 질의를 처리할 수 있다. 그리고 더 효율적으로 질의를 처리하기 위해 초기에 일정 기간 동안 데이터 스트림에 대한 통계 정보를 수집한다. 실행 시 통계 정보를 수집하는 이유는 데이터 스트림의 특성을 예측할 수 없기 때문에 데이터 특성에 대한 정보를 수집하고 수집된 정보를 가지고 가장 좋은 질의 처리 순서를 결정함으로 써 전체적인 질의 처리 성능을 향상 시킬 수 있고 실험을 통해 이를 검증한다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.43-48
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• 2007

데이터 스트림 관리 시스템에서 조인 연산자는 질의가 내포하는 여러 연산자들 가운데 상대적인 계산비용이 높은 연산자로, 센서 네트워크와 같이 한정적 정보들이 개별적으로 입력되는 환경에서는 필연적으로 요구된다. 데이터 스트림은 잠재적으로 무한한 크기를 가지므로 조인 연산자는 슬라이딩 윈도우 제약사항을 가져야 하며, 종합적인 결과를 얻기 위해 조인 연산자가 여러 입력을 취할 수 있어야 한다. 이를 가능하게 하는 것이 바로 슬라이딩 윈도우를 가지는 MJoin 연산자이다. 본 논문에서는 이러한 여러 MJoin 연산자가 시스템에 등록되어 있는 환경을 가정하고, 슬라이딩 윈도우 제약사항과 MJoin의 특성을 반영하여 전역적으로 공유된 질의 실행 계획 수립 및 처리에 관한 문제를 다룬다. 이러한 다중 MJoin에 대한 전역 공유 질의 실행 계획 수립 문제가 NP-Hard임을 증명하고, 근사화 접근 방법을 제안한다. 또한 전역적으로 공유된 질의 실행 계획을 올바르게 수행할 수 있는 처리 기법을 제안한다. 이러한 연구의 노력은 데이터 스트림 환경에서 효율적인 다중 질의 최적화 및 처리기법의 기초 연구로 활용될 수 있다.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.187-190
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• 2008

데이터 스트림이란 제한 없이 끊임없이 흘러 들어오는 일련의 많은 양의 데이터 객체들을 의미하며, 센서 데이터 처리, 인터넷 트래픽 분석, 웹 서버 로그와 같은 다양한 트랜잭션 로그 분석등과 관련된 수많은 응용 분야에 적용 가능하기 때문에 이들을 처리 하기 위해 많은 연구가 진행되었다. 데이트 스트림을 처리하기 위해서는 미리 등록된 질의들(연속 질의)을 새롭게 들어오는 스트림 데이터들로 계산하여 그 결과를 계속적으로 생성하여야 하므로 연속 질의들은 스트림 데이터가 들어올 때마다 반복적으로 수행되며, 데이터 스트림은 매우 빠르게 입력되는 특성을 가지고 있기 때문에 보다 빠르게 질의를 처리하여야만 한다. 본 논문에서는 다수의 조인 연속 질의들이 시스템에 등록되어 있을 때, 이들을 보다 빠르게 처리할 수 있도록 여러 개의 질의에 반복적으로 적용되는 조인 연산들을 공유함으로써 최적의 질의 계획을 생성하는 기법을 제안한다.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.325-330
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• 2005

We are generally interested in the analysis, detection and prediction of structural motifs in proteins, in order to infer compatibility of amino acid sequence to structure in proteins of known three-dimensional structure available in the Protein Data Bank. In this context, we are analyzing some of the well-characterized structural motifs in proteins. We have analyzed simple structural motifs, such as, ${\beta}$-turns and ${\gamma}$-turns by evaluating the statistically significant type-dependent amino acid positional preferences in enlarged representative protein datasets and revised the amino acid preferences. In doing so, we identified a number of ‘unexpected’ isolated ${\beta}$-turns with a proline amino acid residue at the (i+2) position. We extended our study to the identification of multiple turns, continuous turns and to peptides that correspond to the combinations of individual ${\beta}$ and ${\gamma}$-turns in proteins and examined the hydrogen-bond interactions likely to stabilize these peptides. This led us to develop a database of structural motifs in proteins (DSMP) that would primarily allow us to make queries based on the various fields in the database for some well-characterized structural motifs, such as, helices, ${\beta}$-strands, turns, ${\beta}$-hairpins, ${\beta}$-${\alpha}$-${\beta}$, ${\psi}$-loops, ${\beta}$-sheets, disulphide bridges. We have recently implemented this information for all entries in the current PDB in a relational database called ODSMP using Oracle9i that is easy to update and maintain and added few additional structural motifs. We have also developed another relational database corresponding to amino acid sequences and their associated secondary structure for representative proteins in the PDB called PSSARD. This database allows flexible queries to be made on the compatibility of amino acid sequences in the PDB to ‘user-defined’ super-secondary structure conformation and vice-versa. Currently, we have extended this database to include nearly 23,000 protein crystal structures available in the PDB. Further, we have analyzed the ‘structural plasticity’ associated with the ${\beta}$-propeller structural motif We have developed a method to automatically detect ${\beta}$-propellers from the PDB codes. We evaluated the accuracy and consistency of predicting ${\beta}$ and ${\gamma}$-turns in proteins using the residue-coupled model. I will discuss results of our work and describe databases and software applications that have been developed.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.35-44
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• 2009

The emerging notion of data stream has brought many new challenges to the research communities as a consequence of its conceptual difference with conventional concepts of just data. One typical example is data stream processing in sensor networks. The range of data processing considerations in a sensor network is very wide, from physical resource restrictions such as bandwidth, energy, and memory to the peculiarities of query processing including continuous and specific types of queries. In this paper, as one of the physical constraints in data stream processing, we consider the problem of limited memory and propose a new scheme for data stream reduction based on the Principal Component Analysis (PCA) technique. PCA can transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. We adapt PCA for the data stream of a sensor network assuming the cooperation of a query engine (or application) with a network base station. Our method exploits the spatio-temporal correlation among multiple measurements from different sensors. Finally, we present a new framework for data processing and describe a number of experiments under this framework. We compare our scheme with the wavelet transform and observe the effect of time stamps on the compression ratio. We report on some of the results.