• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.71-81
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• 2011

The EMR management services can monitoring the patients' record with any doctors in any hospital by using the internet and smartphones online. To handle the real time, multidimensional, continuous data, database management systems (DBMS) must cope with high insert rates for updates, however the traditional DBMS suffers from processing these kinds of data due to its serious design bottlenecks. So the researchers put forward to Data Stream Management System (DSMS). In this paper we describe the real-time Data Stream Server for Continuity of Care Record (CCR) that including continuos query processor. This system is compiled with DSMS and DBMS in EMR system for processing and monitoring the coming CCR data stream, and also storing the processed result with high-efficiency. The system enables users not only to query stored CCR information from DBMS, but also to execute continue query on real-time CCR Data Stream, and health information can be transferred between different healthcare providers that would reduce medical error. At last, we develop a IPhone mobile application to test the proposed real-time data stream server.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.1-4
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• 2019

Edge Computing 환경에서는 데이터 처리와 시스템 제어를 위한 별도의 서버가 존재하지 않는다. 서버를 통한 중앙통제 방식이 아닌 Edge computing에 사용된 IoT기기들이 연동되어 데이터 분산 처리와 연산을 통해 전체 시스템이 동작된다. 이러한 Edge computing 시스템 구조 특성상 전체 시스템이 과부하를 피하기 위해 각 IoT 기기에서 동시다발적으로 감지되는 실시간 상황 정보를 효율적으로 처리 하여야한다. 이에 따라 실시간 상황 정보를 효율적으로 처리하거나, 다양한 데이터 분석처리 알고리즘들이 연구 개발되어 데이터 처리에 적용되어 왔다. 하지만 데이터의 정보 흐름과 타입에 초점을 맞춘 것이 아니라 예상분석 및 획일화된 알고리즘을 통해서 분석되기 때문에 해당 플랫폼이 주로 지향하는 데이터 형식에 맞지 않으면 성능저하를 수반하며 사용에 제약이 많은 문제점이 있다. 따라서 본 논문에서는 IoT 환경에서 실시간 반응성 향상을 목표로 오픈소스 기반 스트림 데이터 처리 방법에 대한 비교 분석과 Fast-reaction을 위한 데이터 처리 도구 비교 분석을 연구를 진행한다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.473-482
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• 2019

There have already been studies analyzing the trajectories of objects from data streams of moving objects. Among those studies, there are also studies to discover groups of objects that move together, called companion groups. Most studies to discover companion groups use existing clustering techniques to find groups of objects close to each other. However, these clustering-based methods are often difficult to find the right companion groups because the number of clusters is unpredictable in advance or the shape or size of clusters is hard to control. In this study, we propose a new method that discovers companion groups based on the distance specified by the user. The proposed method does not apply the existing clustering techniques but periodically determines the groups of objects close to each other, by using a technique that efficiently finds the groups of objects that exist within the user-specified distance. Furthermore, unlike the existing methods that return only companion groups and their trajectories, the proposed method also returns their appearance and disappearance time. Through various experiments, we show that the proposed method can detect companion groups correctly and very efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.871-874
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• 2007

Stream data need a efficient data management with high reliability and real time processing. The characteristics of these data are a large volume, a short report interval and asynchronous report time. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query to search the signal value of a past time, the historical query to search the signal value of a past time to current. This paper proposes the efficient method to manage the above signals by using a file structured database in QNX operating systems. The query model to accommodate various query for stream data is proposed. The proposed methods are applied to reactive protection system to verify their usefulness. The COM(Cabinet Operator Module) based on the QNX employs file database that adopts a delta version and a buffering method for the resource limit of a small storage and a low computing power.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.459-470
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4183-4191
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• 2011

Considering the characteristics of a data stream whose data elements are continuously generated and may change over time, there have been many techniques to differentiate the importance of data elements in a data stream by their generation time. The conventional techniques are efficient to get an analysis result focusing on the recent information in a data stream, but they have a limitation to differentiate the importance of information in various ways more flexible. An information differentiation technique based on the term of a fuzzy set can be an alternative way to compensate the limitation. A term of a fuzzy set has been widely used in various data mining fields, which can overcome the sharp boundary problem and give an analysis result reflecting the requirements in real world applications more. In this paper, a fuzzy window mechanism is proposed, which is adapting a term of a fuzzy set and is efficiently used to differentiate the importance of information in mining data streams. Basic concepts including fuzzy calendars are described first, and subsequently details on data stream mining of weighted patterns using a fuzzy window technique are described.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.19-29
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• 2011

Due to the development of web technologies and the increasing use of smart devices such as smart phone, in recent various web services are widely used in many application fields. In this environment, the topic of supporting personalized and intelligent web services have been actively researched, and an analysis technique on a web-click stream generated from web usage logs is one of the essential techniques related to the topic. In this paper, for efficient analyzing a web-click stream of sequences, a sequential pattern mining technique is proposed, which satisfies the basic requirements for data stream processing and finds a refined mining result. For this purpose, a concept of interesting sequential patterns with a time-interval constraint is defined, which uses not on1y the order of items in a sequential pattern but also their generation times. In addition, A mining method to find the interesting sequential patterns efficiently over a data stream such as a web-click stream is proposed. The proposed method can be effectively used to various computing application fields such as E-commerce, bio-informatics, and USN environments, which generate data as a form of data streams.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• Proceedings of the Korea Database Society Conference
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• 2002.10a
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• pp.456-462
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• 2002

본 논문은 은행에서 사용하던 기존 Stream 기반 전문데이터 교환 방식에서 인터넷 뱅킹 시스템 환경의 XML 기반 전문데이터 교환 방식으로의 전환을 통해서 시스템의 성능을 개선하고 구조화된 데이터의 활용을 통해 재사용성과 관리 효율을 획기적으로 향상시킨 개선방안을 제시하고 있다. 기존 스트림 구조의 XML 프레임으로의 설계와 이의 실시간 활용으로 한층 더 지능화된 시스템으로 설계 및 구현하는 방식을 제시하고 있다.