• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4908-4928
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• 2017

Recent research into wireless sensor network (WSN)-related technology that senses various data has recognized the need for spatio-temporal queries for searching necessary data from wireless sensor nodes. Answers to the queries are transmitted from sensor nodes, and for the efficient transmission of the sensed data to the application server, research on index processing methods that increase accuracy while reducing the energy consumption in the node and minimizing query delays has been conducted extensively. Previous research has emphasized the importance of accuracy and energy efficiency of the sensor node's routing process. In this study, we propose an itinerary-based R-tree (IR-tree) to solve the existing problems of spatial query processing methods such as efficient processing and expansion of the query to the spatio-temporal domain.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.564-566
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• 2003

There are no immutable phenomena in reality. A lot of applications are dealing with data characterized by spatial and temporal and/or uncertain features. Currently, there has no any data model accommodating enough those three elements of spatial objects to directly use in application systems. For such reasons, we introduce a fuzzy spatio -temporal data model (FSTDM) and a method of integrating temporal and fuzzy spatial operators in a unified manner to create fuzzy spatio -temporal (FST) operators. With these operators, complex query expression will become concise. Our research is feasible to apply to the management systems and query processor of natural resource data, weather information, graphic information, and so on.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.294-304
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• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.

• 경영과학
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• 제14권1호
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• pp.87-105
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• 1997

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• 한국경영과학회지
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• 제14권1호
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• pp.87-87
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• 1989

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• 한국인터넷방송통신학회논문지
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• 제17권1호
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• pp.139-144
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• 2017

최근 GeoSensor 활용이 증가함에 따라 공간 센서 데이터와 같은 2차원 데이타를 효율적으로 처리하기 위한 질의 처리 시스템이 활발히 연구되고 있다. 하지만 기존 질의 처리 시스템들은 시공간 센서 데이터와 같은 다차원 데이타를 처리하기 위한 데이타 타입과 연산자를 지원하지 않기 때문에 이와 같은 다차원 데이터를 처리하기에 미흡하다. 따라서, 본 논문은 이러한 센서 네트워크에서 다차원 데이타를 효율적으로 처리하기 위하여 질의 처리 시스템을 개발하였다. 마지막으로 본 논문은 시나리오를 통해 본 시스템의 효용성을 검증하고, 수행시간 및 메모리 사용량 등의 성능평가를 통해 기존 시스템들보다 성능이 우수함을 입증하였다.

• Spatial Information Research
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• 제16권2호
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• pp.219-236
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• 2008

센서로부터 획득되는 데이터 스트림은 스트림 데이터 간의 인과관계와 같은 다양한 유용한 정보를 포함한다. 센서 스트림에 대한 인과관계 조인질의는 스트림으로부터 인과관계의 (원인, 결과) 쌍을 찾아내는 것이다. 하지만 센서로부터 DSMS로 데이터가 전송될 때 발생하는 지연과 제한된 윈도우 크기로 인해 일부의 인과관계 결과 쌍이 손실될 수 있다. 본 논문에서는 먼저 데이터 스트림에서 인과관계 조인질의를 처리할 때 고려해야할 시간적, 공간적 그리고 시공간적 관점에 대해 관찰하고 이러한 관찰들을 고려한 다양한 슬라이딩 윈도우 처리 방법들을 제안한다. 제안된 방법들의 성능은 다양한 실험들을 통해 평가되어지는데 실험 결과들은 본 논문에서 제안된 방법들이 기존의 FIFO 방법에 비해 인과관계 질의 처리 결과가 더 정확함을 보여준다.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• 한국인터넷방송통신학회논문지
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• 제11권1호
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• pp.109-113
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• 2011

최근 유비쿼터스 컴퓨팅 환경이 발전함에 따라 교통(u-Transport), 복지(u-Care), 문화(u-Fun), 환경(u-Green), 산업(u-Business), 행정(u-Government), 도시(u-City) 뿐만 아니라 사용자의 위치와 다양한 공간 정보를 제공하는 u-GIS가 유비쿼터스 컴퓨팅 환경의 핵심 요소 기술로 대두되고 있다. 이에 본 논문에서는 모바일 장치에서 시공간 데이타를 효율적으로 처리 및 관리할 수 있는 모바일 시공간 질의 처리 시스템을 개발하였다. 모바일 시공간 질의 처리 시스템은 OpenGIS "Simple Features Specification for SQL"에서 명시하는 공간 데이타 타입과 공간 연산자를 확장하여 시공간 데이타 타입과 시공간 연산자를 제공하고 시공간 데이타 특성을 고려한 산술 연산 코딩 압축 기법을 제공하고 모바일 시공간 질의 처리 시스템과 u-GIS 서버 사이에서 시공간 데이타 수입/수출의 성능 향상을 위한 데이타 캐슁 기능을 지원한다.

• ETRI Journal
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• 제22권3호
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• pp.41-51
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• 2000

An important use of data warehousing is to provide temporal views over the history of source data. It is significant that nearly all data warehouses are dependent on relational database technology, yet relational databases provide little or no real support for temporal data. Therefore, in is difficult to obtain accurate information for time-varying data. In this paper, we are going to design a temporal data warehouse to support time-varying data efficiently. For this purpose, we present a method to support temporal query by combining a temporal query process layer with the relational database which is used as a source database in an existing data warehouse. We introduce the Temporal Aggregate Tree Strategy (TATS), and suggest its algorithm for the way to aggregate the time-varying data that is changed by the time when the temporal view is created. In addition, The TATS and the materialized view creation method of the existing data warehouse have been evaluated. As a result, the TATS reduces the size of the fact table and it shows a good performance for the comparison factor in case of processing the query for time-varying data.