• Journal of KIISE:Databases
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• v.31 no.2
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• pp.150-162
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• 2004

Moving objects have characteristics that they change continuously their positions over time. The movement of moving objects should be stored on trajectories for processing past queries. Moving objects databases need to provide spatio-temporal index for handling moving objects queries like combined queries. Combined queries consist of a range query selecting trajectories within a specific range and a trajectory query extracting to parts of the whole trajectory. Access methods showing good performance in range queries have a shortcoming that the cost of processing trajectory Queries is high. On the other hand, trajectory-based index schemes like the TB-tree are not suitable for range queries because of high overlaps between index nodes. This paper proposes new TR(Trajectory Riving)-tree which is revised for efficiently processing the combined queries. This index scheme has several features like the trajectory preservation, the increase of the capacity of leaf nodes, and the logical trajectory riving in order to reduce dead space and high overlap between bounding boxes of nodes. In our Performance study, the number of node access for combined queries in TR-tree is about 25% less than the STR-tree and the TB-tree.

• ETRI Journal
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• v.29 no.5
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• pp.691-693
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• 2007

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)-based query index. We project a two-dimensional continuous range query on each axis to get two one-dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment-encoded squares-based indexing method, which is one of the most recently proposed methods.

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

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.129-141
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• 2005

Although spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since to answer range sum queries, the direct access to a huge amount of data incurs prohibitive computation cost, materialization techniques based on existing index structures are recently suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. However, the MVR-tree has a difficulty in maintaining pre-aggregated results inside its internal nodes due to cyclic paths between nodes. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new indexing technique called the Adaptive Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Experimental results show that the performance of the APART is typically above 2 times better than existing aggregate structures in a wide range of scenarios.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.195-210
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• 2007

Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.247-261
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• 2002

Aggregation is an important operation that affects the performance of OLAP systems. In this paper we define a new class of aggregation queries, called range-groupby queries, and present a method for processing them. A range-groupby query is defined as a query that, for an arbitrarily specified region of an n-dimensional cube, computes aggregations for each combination of values of the grouping attributes. Range-groupby queries are used very frequently in analyzing information in MOLAP since they allow us to summarize various trends in an arbitrarily specified subregion of the domain space. In MOLAP applications, in order to improve the performance of query processing, a method of maintaining precomputed aggregation results, called the prefix-sum array, is widely used. For the case of range-groupby queries, however, maintaining precomputed aggregation results for each combination of the grouping attributes incurs enormous storage overhead. Here, we propose a fast algorithm that can compute range-groupby queries with minimal storage overhead. Our algorithm maintains only one prefix-sum away and still effectively processes range-groupby queries for all possible combinations of the grouping attributes. Compared with the method that maintains a prefix-sum array for each combination of the grouping attributes in an n-dimensional cube, our algorithm reduces the space overhead by (equation omitted), while accessing a similar number of cells.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.397-401
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• 2007

Recent applications on continuous queries on moving objects are extended quickly to various parts. These applications need not only 2-dimensional space data but also high-dimensional space data. If we use previous index for overlapped continuous range queries on high-dimensional space data, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We focus on stationary queries, non-exponential increase of storage cost and efficient processing time for large data sets. In this paper, to solve these problems, we present a novel query indexing method, denoted as PAB(Projected Attribute Bit)-based query index. We transfer information of high-dimensional continuous range query on each axis into one-dimensional bit lists by projecting technique. Also proposed query index supports incremental update for efficient query processing. Through various experiments, we show that our method outperforms the CES(containment-encoded squares)-based indexing method which is one of the most recent research.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.70-75
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• 2015

In this paper, we explore the efficient processing of continuous range queries over a huge number of moving objects, each of which retrieves the moving objects that are currently located within a geographic query region of interest. The moving objects should continually communicate with the server to report their current locations, so as to keep the results of the continuous range queries up-to-date. However, this increases the server workload and involves a enormous amount of communication as the number of continuous range queries and the moving objects becomes enormous. In this paper, we adopt an approach where we leverage available memory and computational resources of the moving objects in order to resolve these problems. To this end, we propose a query indexing structure, referred to as the Space Partitioning Query Index(SPQI), which enables the server to efficiently cooperate with the moving objects for processing continuous range queries. SPQI improves system performance in terms of server workload and communication cost. Through simulations, we show the superiority of SPQI.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.3-7
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• 2009

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query but supports various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.13-24
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• 2005

Objects, which change their positions over time such as cars, are called moving objects. Trajectories of a moving object have large volumes because trajectories are accumulated. Efficient indexing techniques for searching these large volumes of trajectories are needed in the moving object databases. Especially the TB-tree which supports bundling trajectories is suitable for processing combined queries which have 2 steps: first step is selecting trajectories (range search), next is selecting the parts of each trajectory (trajectory search). But the TB-tree has unnecessary disk accesses cause of lack of spatial discrimination in range queries. In this paper, we propose and implement the splitting polity which can reduce dead spaces of non-leaf node in order to process range queries efficiently. The policy has better performance about range queries than the TB-tree as well as the advantages of the TB-tree, such as highly space utilization and efficient trajectory extraction. This paper shows that the newly proposed split policy has better performance in processing the range queries than that of the TB-tree by experimental evaluation.