• The Transactions of the Korea Information Processing Society
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• v.5 no.12
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• pp.3025-3035
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• 1998

In this paper, we describe the design and implementationof COIRS COncept,based Image Retricval System). It differs from extant content-based image retrieval systems in that it enables users to query based on concepts- it allows users to get images concepmally relevant. A concept is basically an aggregation of promitive objects in an image. For such a cencept based image retrieval functionality. COIRS aglopts an image descriptor called triple and includes a triple thesaurus used for capturing concepts. There are four facilities in COIRS: a visual image indeses a triple thesaurus, an inverted fiel, and a user query interface. The visnal image indeser facilitates object laeling and the percification of positionof objects. It is an assistant tool designed to minimize manual work when indexing images. The thesarrus captires the concepts by analyzing triples, thereby extracting image semantics. The triples are then for formalating queries as well as indexing images. The user query interiare enables users to formulate...

• International Journal of Contents
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• v.8 no.2
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• pp.13-18
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• 2012

In wireless sensor networks, an energy efficient data gathering scheme is one of core technologies to process a query. The cluster-based data gathering methods minimize the energy consumption of sensor nodes by maximizing the efficiency of data aggregation. However, since the existing clustering methods consider only uniform network environments, they are not suitable for the real world applications that sensor nodes can be distributed unevenly. To solve such a problem, we propose a balanced multi-hop clustering scheme in non-uniform wireless sensor networks. The proposed scheme constructs a cluster based on the logical distance to the cluster head using a min-distance hop count. To show the superiority of our proposed scheme, we compare it with the existing clustering schemes in sensor networks. Our experimental results show that our proposed scheme prolongs about 48% lifetime over the existing methods on average.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.215-220
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• 2007

최근 센서 네트워크에서 에너지 효율성을 위한 다양한 연구가 진행 중이다. 특히 센서 노드의 저전력을 위해서는 센서 네트워크에서 전송되는 데이터의 횟수나 전송량을 최소한으로 줄이면서 효율적이면서 신뢰성을 가지는 질의에 대한 결과를 얻을 수 있어야 한다. 본 연구에서는 해양 센서 네트워크 상에서 데이터의 전송량을 줄일 수 있는 SDMTree(Sensing Data Management Tree)를 제안한다. 제안된 SDMTree는 질의 최적화를 위해 질의 처리기 구성 요소로 도입 가능하다. 해양 센서 네트워크에서 in-network 각 4레벨에서 하위 노드로부터 받은 데이터를 병합, 관리하기 위한 방법으로 데이터를 속성별로 구분하여 중복된 데이터를 제거하여 트리형태로 구성되기 때문에 질의에 대한 응답에 해당하는 데이터 검색시 정확하고 신속하게 처리할 수 있으며, 트리 구성 또한 중복 데이터 및 중복 영역을 배제하여 구성되므로, 상위노드가 하위 노드로부터 센싱 데이터를 수집하여 저장하기 위한 에너지와 상위 노드에서 하위 노드로 질의를 전송시 질의에 해당하는 특정 영역에만 질의를 전송할 수 있기 때문에 데이터 저장 및 통신에 소모되는 불필요한 에너지를 최대한 줄일 수 있다.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.

• Journal of KIISE
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• v.44 no.9
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• pp.966-975
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• 2017

In Internet of Things (IoT), the aim of the nodes (called 'Things') is to exchange information with each other, whereby they gather and share information with each other through self decision-making. Therefore, we cannot apply existing aggregation algorithms of Wireless sensor networks that aim to transmit information to only a sink node or a central server, directly to the IoT environment. In addition, since existing algorithms aggregate information from all sensor nodes, problems can arise including an increasing number of transmissions and increasing transmission delay and energy consumption. In this paper, we propose the clustering and property based data exchange method for energy efficient information sharing. First, the proposed method assigns the properties of each node, including the sensing data and unique resource. The property determines whether the node can respond to the query requested from the other node. Second, a cluster network is constructed considering the location and energy consumption. Finally, the nodes communicate with each other efficiently using the properties. For the performance evaluation, TOSSIM was used to measure the network lifetime and average energy consumption.

• Spatial Information Research
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• v.18 no.4
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• pp.75-88
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• 2010

Recent development in wireless communication and mobile positioning technologies make geosensor networks widely used in the various fields of real world. As a result, much research has been done on the middleware that uses limited energy resources efficiently. However, because traditional middleware does not consider the characteristics of sensor node, such as computing power and specification, the existing middleware call not support the sensor nodes with only the restricted system resource. Therefore, in this paper, we design and implement a new Bytecode based Data Service Middleware supporting energy efficiency in geosensor networks. At first, the proposed middleware provides the optimized functions for sensor nodes by using minimum by tee ode instruction set and data manager supporting hardware abstraction. Secondly, the proposed middleware increases the energy efficiency of sensor node through both data aggregation query processing and data filtering that minimize data transmission by eliminating unnecessary data. Finally, we show from our performance analysis that the proposed middleware is more energy efficient than the existing SwissQM.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.80-84
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• 2008

DSMS는 USN과 같은 환경으로부터 스트림데이터를 실시간으로 입력 받아 등록된 연속질의를 처리하는 시스템이다. DSMS는 등록된 연속질의 처리를 위해 필요한 데이터를 버퍼에 관리하며, 스트림데이터의 저장기법에 따라 연속질의 처리 성능 및 버퍼 저장비용이 개선될 수 있으며, DSMS에서 연속질의는 특정 스트림데이터에 대해 일정한 기간 동안의 평균 값, 최대 소 값, 누적 값 등의 집계 연산을 요구하는 경우가 많다. 기존의 DSMS에서는 이러한 집계 연산이 필요한 연속질의의 효율적인 처리를 위해 LINT, BINT등의 자원 공유 집계 처리기법이 제안 되었다. 하지만 기존의 자원공유 집계 기법들은 위치 값을 포함하는 GeoSensing 데이터에 대한 고려를 하지 않았다. 본 논문에서는 공간 DSMS에서 공간영역질의 기반의 연속질의를 효율적으로 처리하기 위한 R-tree기반의 집계기법을 제안한다. 이는 각각의 연속질의에 포함된 공간 영역을 R-tree 인덱스로 구성하고, 연속질의에 필요한 공간 스트림데이터에 대한 집계값을 저장하여 연속질의를 처리하는 것이다. 제안기법은 공간 DSMS에서 공간영역 기반의 연속질의 처리 성능을 개선할 수 있으며, R-tree 기반으로 해당 영역에 대한 데이터 만을 버퍼에 관리하여 저장비용을 줄일 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.477-482
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• 2016

As being the advanced analytics indispensable on big data for agile decision-making and tactical planning in enterprises, distributed processing platforms, such as Hadoop and Spark which distribute and handle the large volume of data on multiple nodes, receive great attention in the field. In Spark platform stack, Spark SQL unveiled recently to make Spark able to support distributed processing framework based on SQL. However, Spark SQL cannot effectively handle advanced analytics that involves machine learning and graph processing in terms of iterative tasks and task allocations. Motivated by these issues, this paper proposes the design of SQL-based big data optimal processing engine and processing framework to support advanced analytics in Spark environments. Big data optimal processing engines copes with complex SQL queries that involves multiple parameters and join, aggregation and sorting operations in distributed/parallel manner and the proposing framework optimizes machine learning process in terms of relational operations.