• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.34-37
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• 2007

간 및 낮은 메모리 사용량을 요구한다. 이러한 데이터 스트림에서의 데이터 마이닝은 전체 데이터에 대한 분석 보다는 사용자가 관심을 갖는 영역에 대한 마이닝에 초점이 맞추어져 있어, 사용자 관심영역에 대한 분석 데이터 탐색을 필요로 한다. 이에 본 논문에서는 기존의 분석 데이터 탐색 기법인 빙산 질의 및 상위-k 질의에 대하여 알아보고, 이를 보완하기 위한 확률에 기반한 데이터 탐색법인 확률기반 빙산 질의를 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.509-516
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• 2008

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. These signals are the stream data of which characteristics are several numbers. a short report interval and asynchronous report time. It is necessary that the system brings about high accuracy and real time process for stream data. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query at a past time, the sliding window query from a past time to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in small-size operating systems. We also propose a query model to accommodate various queries including the sliding window query. The file database in the QNX adopts a delta version and a shared memory buffering method for the resource limit of a small storage and a low computing power.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.49-51
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• 2005

데이터 스트림 환경에서는 빠르게 무한히 생성되는 데이터를 가능한 빠른 시간에 질의 조건의 충족 여부를 판단하는 것이 시스템의 성능에서 중요한 역할을 한다. 따라서 신속한 판단을 위해 다수의 데이터 스트림 시스템에서 개별 선택 조건을 처리하는 것보다는 속성으로 통합하여 그룹 처리하는 방식을 사용하고 있다. 그룹 필터 처리시에는 그룹 필터의 순서에 따라 큰 처리비용의 차이가 발생하는데 본 논문은 데이터스트림 환경에서 시스템이 처리해야 하는 다수의 연속질의의 선택조건을 그룹 처리할 때 그룹 필터 연산의 처리 비용을 최소화 하기 위한 순서를 결정하는 방법을 제안한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2158-2164
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• 2015

In u-GIS environments, various load shedding techniques have been researched in order to balance loads caused by input spatial data streams. However, typical load shedding methods on aspatial data lack regard for characteristics of spatial data, also previous load shedding approaches on spatial, which still lack regard for spatial data density or dynamic input data stream, give rise to troubles on spatial query processing performance and accuracy. Therefore, dynamic load shedding scheme over spatial data stream is proposed through stored spatial data deviation and load ratio of input data stream in order to improve spatial continuous query accuracy and performance in u-GIS environment. In proposed scheme, input data which are a big probability related to spatial continuous query may be a strong chance to be dropped relatively.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.05a
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• pp.204-209
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• 1998

OMG에서 제안된 CORBA는 표준 개방 분산환경의 플래트폼을 제공하며 객체지향적인 방법으로 최근 주목을 받고 있다. 일반적으로 CORBA는 원격 데이터베이스 질의 등과 같은 요구/응답형의 응용에 잘 적용이 된다. 그러나 과도한 처리부담, 실시간 처리 기능과 QOS 지원 등의 결여로 멀티미디어 스트림과 같은 연속미디어의 처리에는 부적합하다. 본 논문에서는 실시간 제약을 갖는 멀티미디어 스트림의 효율적인 전송을 위하여 기존의 CORBA를 확장하여 멀티미디어 서비스를 설계한다.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.67-83
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• 2008

In order to realize ubiquitous computing, it is essential to efficiently use the resources and the computing power of mobile devices. Among others, memory efficiency, energy efficiency, and processing efficiency are required in executing the softwares embedded in mobile devices. In this paper, query processing over XML data in a mobile device where resources are limited is addressed. In a device with limited amount of memory, the techniques of XML. stream query processing need to be employed to process queries over a large volume of XML data Recently, a technique Galled XFrag was proposed whereby XML data is fragmented with the hole-filler model and streamed in fragments for processing. With XFrag, query processing is possible in the mobile device with limited memory without reconstructing the XML data out of its fragment stream. With the hole-filler model, however, memory efficiency is not high because the additional information on holes and fillers needs to be stored. In this paper, we propose a new technique called XFLab whereby XML data is fragmented with the XML labeling scheme which is for representing the structural relationship in XML data, and streamed in fragments for processing. Through implementation and experiments, XML showed that our XFLab outperformed XFrag both in memory usage and processing time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.535-544
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• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.336-339
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• 2007

A new query model is required to match requirements of stream-based applications such as patient monitoring system, since traditional DBMSs are not designed to provide continuous queries over stream data. In the patient monitoring system, there are many types of biomedical signals such as blood pressure and temperature, and these signals gathered by biomedical sensors should be treated as a stream, that is an ordered set of signals. In this paper, we categorized all possible queries to be used in patient monitoring system by four types of queries. Then, we have proposed a new sliding window query model which is capable of expressing these four types of queries.