• Journal of KIISE:Databases
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• v.32 no.5
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• pp.487-497
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• 2005

A range mosaic query returns distribution of data within the query region as a pattern of mosaic, whereas a range aggregate query returns a single aggregate value of data within the query region. The range mosaic query divides a query region by a multi-dimensional grid, and calculates aggregate values of grid cells. In this paper, we propose a new type of query, range mosaic query and a new operator, mosaic-by, with which the range mosaic queries can be represented. In addition, we suggest efficient algorithms for processing range mosaic queries using an aggregate R-tree. The algorithm that we present computes aggregate results of every mosaic grid cell by one time traversal of the aggregate R-tree, and efficiently executes the queries with only a small number of node accesses by using the aggregate values of the aggregate R-tree. Our experimental study shows that the range mosaic query algorithm is reliable in terms of performance for several synthetic datasets and a real-world dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.597-618
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• 2019

Categorical range aggregation, which is conceptually equivalent to running a range aggregation query separately on multiple datasets, returns the query result on each dataset. The challenge is when the number of dataset is as large as hundreds or thousands, it takes a lot of computation time and I/O. In previous work, only a single dimension of the range restriction has been solved, and in practice, more applications are being used to calculate multiple range restriction statistics. We proposed MCRI-Tree, an index structure designed to solve multi-dimensional categorical range aggregation queries, which can utilize main memory to maximize the efficiency of CRA queries. Specifically, the MCRI-Tree answers any query in $O(nk^{n-1})$ I/Os (where n is the number of dimensions, and k denotes the maximum number of pages covered in one dimension among all the n dimensions during a query). The practical efficiency of our technique is demonstrated with extensive experiments.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.165-174
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• 2000

Recent researches about range queries in OLAP are only concerned with applying an aggregation operator over a certain region. However, data analysts in real world need not only the simple range query pattern but also an extended range query pattern that finds ranges which satisfy a special condition specified by using several aggregation operators. In this work, we define the general form of the extended range query and propose an efficient processing method for the 'MAX -of-SUM' query, which is the representative form of the extended range query pattern. The MAX-of-SUM query finds the range which has the maximum range sum value in data cube where the size of the range is given. The proposed query processing method is based on the prediction of the scope of the range sum values. That is, the search space on the query processing can be reduced by using the result of the prediction, and hence, the query response time is also reduced.

• 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 Korea Spatial Information System Society
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• v.9 no.2
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• pp.67-79
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• 2007

Recently, to support LBS(location-based services) and telematics applications efficiently, there have been many researches which consider the spatial network instead of Euclidean space. However, existing range query and k-nearest neighbor query algorithms show a linear decrease in performance as the value of radius and k is increased. In this paper, to increase the performance of query processing algorithm, we propose materialization-based range and k-nearest neighbor algorithms. In addition, we make the performance comparison to show the proposed algorithm achieves better retrieval performance than the existing algorithm.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3937-3954
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• 2014

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. Many schemes have been proposed to support energy auction in smart grid. However, few of them can achieve range query, ranked search and personalized search. In this paper, we propose an efficient multi-keyword range query (EMRQ) scheme, which can support range query, ranked search and personalized search simultaneously. Based on the homomorphic Paillier cryptosystem, we use two super-increasing sequences to aggregate multidimensional keywords. The first one is used to aggregate one buyer's or seller's multidimensional keywords to an aggregated number. The second one is used to create a summary number by aggregating the aggregated numbers of all sellers. As a result, the comparison between the keywords of all sellers and those of one buyer can be achieved with only one calculation. Security analysis demonstrates that EMRQ can achieve confidentiality of keywords, authentication, data integrity and query privacy. Extensive experiments show that EMRQ is more efficient compared with the scheme in [3] in terms of computation and communication overhead.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.13-38
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• 2006

Range query is one of the most important operations for spatial objects, it retrieves all spatial objects that overlap a given query region in multi-dimensional space. The DOT(DOuble Transformation) is known as an efficient indexing methods, it transforms the MBR of a spatial object into a single numeric value using a space filling curve, and stores the value in a $B^+$-tree. The DOT index is possible to be employed as a primary index for spatial objects. However, the range query processing based on the DOT index requires much overhead for spatial transformations to get the query region in the final space. Also, the detailed range query processing method for 2-dimensional spatial objects has not been studied yet in this paper, we propose an efficient multi-dimensional range query processing technique based on the DOT index. The proposed technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-legions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the range query and thus improves the performance of range query processing. A visual simulator is developed to show the evaluation method and the performance of our technique.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.46-48
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• 2004

Distributed Hash Table(DHT)를 기반으로 하는 structured P2P에서 복잡 질의를 통친 원하는 자료를 찾는 것은 쉽지 않다. 이와 같은 것들 중 하나가 범위 값을 이용하여 원하는 값을 찾는 range query이다. 본 논문에서는 full binary tree의 특징을 이용하여 structured P2P에서 range query를 수행할 수 있는 mapping 함수와, load balance를 지원하기 위한 방법을 제안한다 또한 실험을 통해서 이 방법들이 어느 정도의 성능을 보여주는 지도 살펴본다.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.51-62
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• 2004

Recently, the location-based service for moving customers is becoming one of the most important service in mobile communication area. For moving object applications, there are lots of update operations and such update loads are concentrated on some particular area unevenly. The primary processing of LBS application is spatio-temporal range queries. To improve the throughput of spatio-temporal range queries, the time of disk I/O in query processing should be reduced. In this paper, we adopt non-uniform two-level grid index structures of GALIS architecture,which are designed to minimize update operations. We propose query scheduling technique using spatial relationship and time relationship and a combined spatio-temporal query processing method using time zone concepts to improve the throughput of query processing. Some experimental results are shown for range queries with different query range to show the performance tradeoffs of the proposed methods.