• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.93-107
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• 2007

In this paper, an adaptive range aggregation spatial index method is proposed for spatial range query in ubiquitous sensor networks. As the ubiquitous sensor networks are the new information-oriented paradigm, many energy efficient spatial range query methods in ubiquitous sensor networks environment are studied vigorously. In sensor networks, users can monitor environment scalar data such as temperature and humidity during user defined time and spatial ranges. In order to execute spatial range query efficiently, rectangle based index methods are proposed, such as SPIX. But they define the return path as the opposite of its query transmit path. However, the sensor nodes in queried ranges are closed to each other, they can't aggregate the sensed value in a queried range because their query transmission paths are different. As a result, the previous methods waste energy unnecessarily to aggregate sensing data out of the queried range. In this paper, an adaptive aggregation index method is proposed that can aggregate values in a user defined range adaptively by using its neighbor information. It is shown that sensor power is saved efficiently by using the proposed method over the performance evaluation.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.89-98
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• 2009

As data stream is entered into system continuously and the memory space is limited, the data exceeding the memory size cannot be processed. In order to solve the problem, load shedding methods which drop a part of data to prevent exceeding the storage space have been researched. Generally, a traditional load shedding method uses random sampling with optimized rate according to data deviation. The method samples data not to distinguish those used in spatial query because the method uses only a random sampling with optimized rate according to data deviation. Therefore, the accuracy of query was reduced in u-GIS environment including spatial query. In this paper, we researched a new load shedding method improving accuracy of the query in u-GIS environment which runs spatial query and aspatial query simultaneously. The method uses a new sampling method that samples data having low probability used in query. Therefore proposed method improves spatial query accuracy and query processing speed as applying spatial filtering operation to sampling operator.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.297-312
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• 2005

Aggregation is an operation that plays a key role in multidimensional OLAP (MOLAP) systems of data warehouse. Existing aggregation operations in MOLAP have been proposed for file structures such as multidimensional arrays. These tile structures do not work well with skewed distributions. This paper presents a physical design methodology for storage structures ni MOLAP that use the multidimensional tile organizations adapting to a skewed distribution. In uniform data distribution, we first show that the performance of multidimensional analytical processing is highly affected by the similarity of the shapes between query regions and page regions in the domain space of the multidimensional file organizations. And than, in skewed distributions, we reflect the effect of data distributions on the design by using the shapes of the normalized query regions that are weighted with data density of those query regions. Finally, we demonstrate that the physical design methodology theoretically derived is indeed correct in real environments. In the two-dimensional file organizations, the results of experiments indicate that the performance of the proposed method is enhanced by more than seven times over the conventional method. We expect that the performance will be more enhanced when the dimensionality is more than two. The result confirms that the proposed physical design methodology is useful in a practical way.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.69-76
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• 1999

Recently, it has been studied the methods to materialize and precompute the query results for complexed spatial aggregation queries with high response time and the popular use in spatial data warehouse. In this paper, we propose extended selective materialization algorithm and present the way to materialize selectively which is considered access frequency and computation time of spatial operation according to spatial measures of spatial views for improvement of existing selective materialization algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.46-48
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• 2003

최근 시공간 데이타에 대한 OLAP연산 효율을 증가시키기 위한 여러 가지 연구들이 행하여지고 있다. 이들 연구의 대부분은 다중트리구조에 기반하고 있다. 다중트리구조는 공간차원을 색인하기 위한 하나의 R-tree와 시간차원을 색인하기 위한 다수의 B-tree로 이루어져 있다. 하지만, 이러한 다중트리구조는 높은 유지비용과 불충분한 질의 처리 효율로 인해 현실적으로 시공간 OLAP연산에 적용하기에는 어려운 점이 있다. 본 논문에서는 이러한 문제를 근본적으로 개선하기 위한 접근 방법으로서 힐버트큐브(Hilbert Cube, H-Cube)를 제안하고 있다. H-Cube는 집계질의(aggregation query) 처리 효율을 높이기 위해 힐버트 곡선을 이용하여 셀들에게 완전순서(total-order)를 부여하고 있으며, 아울러 전통적인 누적합(prefix-sum) 기법을 함께 적용하고 있다. H-Cube는 적응적이며, 완전순서화되어 있으며, 또한 누적합을 이용한 셀 기반의 색인구조이다. 본 논문에서는 H-Cube의 성능 평가를 위해서 다양한 실험을 하였으며, 그 결과로서 유지비용과 질의 처리 효율성면 모두에서 다중트리구조보다 높은 성능 향상이 있음을 보인다.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.451-463
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• 2003

Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on huge amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi~tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called Hilbert Cube(H-Cube), which employs fractals in order to impose a total-order on cells. In addition, the H-Cube takes advantage of the traditional Prefix-sum approach to improve Query efficiency significantly. The H-Cube partitions an embedding space into a set of cells which are clustered on disk by Hilbert ordering, and then composes a cube by arranging the grid cells in a chronological order. The H-Cube refines cells adaptively to handle regional data skew, which may change its locations over time. The H-Cube is an adaptive, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our approach focuses on indexing dynamic point objects in static spatial dimensions. Through the extensive performance studies, we observed that The H-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree structures.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.298-300
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• 2002

다차원 데이터를 배열에 저장하는 Multidimensional OLAP (MOLAP) 시스템은 배열내의 위치 정보를 통해 데이터를 신속하게 엑세스할 수 있는 장점을 갖는다. 그러나 실생활의 다차원 데이터는 대체로 희박하여 저장될 때 압축되고, 데이터가 검색될 때는 원래의 위치 정보를 찾기 위해 인덱스를 필요로 하게 된다. 다양한 종류의 다차원 인덱스가 테이블 형태의 데이터를 대상으로 개발되어 있으나, 이들은 데이터의 삽입과 삭제에 유연하게 대처할 수 있도록 하기 위해서 인덱스 공간과 데이터 검색 시간에 약간의 낭비를 초래한다. 본 연구에서는 OLAP 데이터가 주기적으로 갱신되며, 분석에 필요한 집계 데이터도 점진적으로 갱신되기보다 실제로는 새로 생성되고 있다는 점을 고려하여, 읽기 전용 MOLAP 데이터를 위한 인덱스 구조를 제안한다. 데이터는 청크들로 나뉜 후 압축 저장되며, 각 청크는 위치 정보를 유지하면서 비트로 표현되어 인덱스에 저장되도록 하였다. 제안한 비트맵 인덱스는 높은 압축률을 보이며, 범위 질의(range query)를 포함한 OLAP 주요 연산들 처리에 특히 효율적이다.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1386-1396
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• 2009

Most decision support functions of spatial data warehouse rely on the OLAP operations upon a spatial cube. Meanwhile, higher performance is always guaranteed by indexing the cube, which stores huge amount of pre-aggregated information. Hierarchical Dwarf was proposed as a solution, which can be taken as an extension of the Dwarf, a compressed index for cube structures. However, it does not consider the spatial dimension and even aggregates incorrectly if there are redundant values at the lower levels. OLAP-favored Searching was proposed as a spatial hierarchy based OLAP operation, which employs the advantages of R-tree. Although it supports aggregating functions well against specified areas, it ignores the operations on the spatial dimensions. In this paper, an indexing approach, which aims at utilizing the concept hierarchy of the spatial cube for decision support, is proposed. The index consists of concept hierarchy trees of all dimensions, which are linked according to the tuples stored in the fact table. It saves storage cost by preventing identical trees from being created redundantly. Also, it reduces the OLAP operation cost by integrating the spatial and aspatial dimensions in the virtual concept hierarchy.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.585-595
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• 2004

A query processing is a critical issue in data warehouse environment since queries on data warehouses often involve hundreds of complex operations over large volumes of data. Data warehouses therefore build a large number of materialized views to increase the system performance. Which views to materialized is an important factor on the view maintenance cost as well as the query performance. The goal of materialized view selection problem is to select an optimal set of views that minimizes total query response time in addition to the view maintenance cost. In this paper, we present an efficient solution for the materialized view selection problem. Although the optimal selection of materialized views is NP-hard problem, we developed a feasible solution by utilizing the characteristics of relational operators such as join, selection, and grouping.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1381-1394
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• 2006

Generation methods of data cube have been studied for many years in data warehouse which supports decision making using stored data. There are two previous studies, one is multi-way array algorithm and the other is H-cubing algorithm which is based on the hyper-tree. The multi-way array algorithm stores all aggregation data in arrays, so if the base data is increased, the size of memory is also grow. The H-cubing algorithm which is based on the hyper-tree stores all tuples in one tree so the construction cost is increased. In this paper, we present an efficient data cube generation method based on hash table using weight mapping table and record hash table. Because the proposed method uses a hash table, the generation cost of data cube is decreased and the memory usage is also decreased. In the performance study, we shows that the proposed method provides faster search operation time and make data cube generation operate more efficiently.