• Journal of KIISE:Databases
• /
• v.33 no.1
• /
• pp.32-41
• /
• 2006

In data-centric networks, various data items, such as temperature, humidity, etc. are sensed and stored in sensor nodes. As these attributes are mostly scalar values and inter-related, multi-dimensional range queries are useful. To process multi-dimensional range queries efficiently in data-centric storage, data addressing is essential. The Previous work focused on efficient query processing without considering overall network lifetime. To prolong network lifetime and support multi-dimensional range queries, we propose a dynamic data distribution method for multi-dimensional data, where data space is divided into equal-sized regions and linearized by using Hilbert space filling curve.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.741-744
• /
• 2015

최근 소셜 미디어를 이용한 대량의 데이터로부터 사용자의 의사결정을 지원하기위한 맞춤형 데이터 추천 서비스가 관심을 받고 있으며 사용자의 선호도에 근접한 데이터 추천기법으로 스카이라인 질의가 연구되어왔다. 그러나 기존의 스카이라인 질의는 데이터의 정적속성(위도, 경도, 가격 등)만을 기준으로 모든 사용자에게 동일한 데이터를 반환하기 때문에 맞춤형 데이터를 추천하기 어렵다. 본 논문에서는 사용자의 기호에 대한 정밀도를 높이기 위해 정적속성에서 동적속성(계산속성)을 유도하는 분기한정 동적 스카이라인 질의 기법(Branch and Bound Dynamic Skyline, BBDS)을 구현하였다. 시뮬레이션에서는 대규모 데이터 및 다양한 분포에 따른 실험을 수행한 결과 BBDS가 기존 기법에 비해 데이터 탐색과 추천에 있어서 향상된 성능을 나타내는 것으로 평가되었다.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.79-88
• /
• 2024

The information retrieval domain deals with the retrieval of unstructured data such as text documents. Searching documents is a main component of the modern information retrieval system. Locality Sensitive Hashing (LSH) is one of the most popular methods used in searching for documents in a high-dimensional space. The main benefit of LSH is its theoretical guarantee of query accuracy in a multi-dimensional space. More enhancement can be achieved to LSH by adding a bit to its steps. In this paper, a new Dynamic Locality Sensitive Hashing (DLSH) algorithm is proposed as an improved version of the LSH algorithm, which relies on employing the hierarchal selection of LSH parameters (number of bands, number of shingles, and number of permutation lists) based on the similarity achieved by the algorithm to optimize searching accuracy and increasing its score. Using several tampered file structures, the technique was applied, and the performance is evaluated. In some circumstances, the accuracy of matching with DLSH exceeds 95% with the optimal parameter value selected for the number of bands, the number of shingles, and the number of permutations lists of the DLSH algorithm. The result makes DLSH algorithm suitable to be applied in many critical applications that depend on accurate searching such as forensics technology.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.3
• /
• pp.55-62
• /
• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.287-291
• /
• 2007

기존 센서네트워크 환경의 노드들이 모바일 환경으로 바뀌면서 클러스터를 구축하고 클러스터 헤더를 선정함에 있어 기존 방법은 정적 노드를 대상으로 구축되어 있기 때문에 이를 동적 노드에 적합한 방법으로 구축하기 위해 기존 연속적인 스카이라인 질의방법을 이용하여 클러스터를 구축하고 클러스터헤더를 선정함으로 센서네트워크의 효율적인 환경을 구축하고자 한다. 기존은 클러스터 헤드 선정을 클러스터를 구축하고 구축된 클러스터 내에서 에너지 잔여량을 비교 하여 가장 에너지가 많은 노드를 헤드로 선정하여 라우팅을 고려하는 기법을 사용하였다. 그러나 센서 노드가 모바일 노드일 경우 위치도 함께 고려되어야 할 속성 중 하나일 것이다. 따라서 이 논문에서는 클러스터 헤더 선정기법에서 기존 방식과 달리 클러스터 헤더를 선정하고 클러스터 헤더를 선정하고 클러스터 헤더를 기준으로 R hop 까지를 하나의 클러스터로 설정하는 효율적인 영역 결정 기법을 제안하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.384-387
• /
• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2007.11a
• /
• pp.267-272
• /
• 2007

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching fur expanding results from an exact matching engine to query the OWL MIT Process Handbook. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We perform retrieval experiments to compare the performance of the devised similarity algorithms.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06a
• /
• pp.36-37
• /
• 2010

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.4
• /
• pp.11-20
• /
• 2010

Ontology is important role in semantic web to construct and query semantic data. Because of dynamic characteristic of ontology, P2P environment is considered for ontology processing in web environment. For efficient processing of ontology in P2P environment, clustering of peers should be considered. When new peer is added to the network, cluster allocation problem of the new peer is important for system efficiency. For clustering of peers with similar chateristics, similarlity measure method of ontology in added peer with ontologies in other clusters is needed. In this paper, we propose similarity measure techniques of ontologies for clustering of peers. Similarity measure method in this paper considered ontology's strucural characteristics like schema, class, property. Results of experiments show that ontologies of similar topics, class, property can be allocated to the same cluster.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2004.12a
• /
• pp.69-74
• /
• 2004

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurately selectivity. Although they deal with some problems such as false-count, multi-count, they require a large amount of memory to retain accurate selectivity, so they can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. It also has a flexible structure to react dynamic update. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory.