• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.67-76
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• 2018

This paper addresses the problem of efficient processing of region monitoring queries. The centralized methods used for existing region monitoring query processing assumes that the mobile object periodically sends location-updates to the server and the server continues to update the query results. However, a large amount of location updates seriously degrade the system performance. Recently, some distributed methods have been proposed for region monitoring query processing. In the distributed methods, the server allocates to all objects i) a resident domain that is a subspace of the workspace, and ii) a number of nearby query regions. All moving objects send location updates to the server only when they leave the resident domain or cross the boundary of the query region. In order to allocate the resident domain to the moving object along with the nearby query region, we use a query index structure that is constructed by splitting the workspace recursively into equal halves. However, However, the above index structure causes unnecessary division, resulting in deterioration of system performance. In this paper, we propose an adaptive split method to reduce unnecessary splitting. The workspace splitting is dynamically allocated i) considering the spatial relationship between the query region and the resultant subspace, and ii) the distribution of the query region. We proposed an enhanced QR-tree with a new splitting method. Through a set of simulations, we verify the efficiency of the proposed split methods.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.297-304
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• 2008

Due to the proliferation of the Internet and intranet, new application domains such as stream data processing have emerged. Stream data is real-timely and continuously generated. In stream data environments, a lot of queries are registered, and then, the arrived data item is evaluated by registered queries. Thus, to accelerate the query performance, diverse continuous query index schemes have been proposed for stream data processing systems. In this paper, we focus on the query index technique for stream data. In general, a stream query contains the range condition. Thus, by using range conditions, the queries can be indexed. In this paper, we propose an efficient query index scheme, called QUISIS, using a modified Interval Skip Lists to accelerate search time. QUISIS utilizes a locality where a value which will arrive in near future is similar to the current value. Through the experimental study, we show the efficiency of our proposed method.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.471-482
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• 2007

In this paper, we propose an efficient continuous range query processing scheme using a modified grid based query indexing to reduce storage spaces and to accelerate processing time. The proposed method has two major features. First, each query has a bit identifier and each cell in a grid has a bit pattern that consists of the bit identifiers of the queries. The bit patterns present the relationship between cells and queries. Using the bit patterns, we can compute quickly what queries overlap a cell in a grid and reduce the number of unnecessary operations by comparing the bit patterns without comparing the query identifiers when we compute the relation between cells and queries. Second, the management of cells in the grid by groups prevents from wasting the storage space through the increase of the length of the bit pattern and increasing the comparison costs of bit patterns. We show through the performance evaluation that the proposed method outperforms the existing methods.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.189-197
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• 2009

Top-k queries are important to many wireless sensor applications. Conventional Top-k query processing algorithms install a filter at each sensor node and suppress unnecessary sensor updates. However, they have some drawbacks that the sensor nodes consume energy extremely to probe sensor reading or update filters. Especially, it becomes worse, when the variation ratio of top-k result is higher. In this paper, we propose a novel Top-k query processing algorithm for energy-efficiency. First, each sensor determines its priority as the order of data gathering. Next, sensor nodes that have higher priority transmit their sensor readings to the base station until gathering k sensor readings. In order to show the superiority of our query processing algorithm, we simulate the performance with the existing query processing algorithms. As a result, our experimental results show that the network lifetime of our method is prolonged largely over the existing method.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.1-9
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• 1998

Temporal Databases store all of informations by time varying, so the temporal query processor has to process very large information. Therefore, we propose an efficient method of query processing by using the relevance checking algorithm of input query and view definition. The relevance checking algorithm of query investigates relevance between the input query of user about base relation and the execution tree of view definition stored in system catalog. And related input query with view definition have a process of the query translation to the execution tree of view. So temporal query processor is able to increase performance of query processor by reducing the number of tuple.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.1-9
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• 1994

Astorage structure using signatures is proposed to evaluate efficently queries including conditions of nested attributes in the nested relational databases This method stores a subrelation signature into the storage structure for a nested tuple with its subrelation pointer. During query processing steps, the subrelation signatures are matched first with the nested predicates in the query. When the match operation completes with success then physical retrieval of the subrelation occurrs resulting in reduction of disk 1/Os.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.19-27
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• 2011

Access control that has been previously performed mainly on safety, and thus not much effort has been done to consider access control in terms of efficiency. This paper proposes a method for an efficient and secure query processing of XML data streams, such as a personal digital assistant and a portable terminal, at the client side with limited resources. Specifically, this paper proposes an access control processing that possesses a small overhead for attaining a secure result with limited memory and a method to enhance the performance, finding the parts capable of optimizing each processing step for offsetting the overhead caused by the addition of access control processing. The superiority of the new method is analyzed through an experiments.

• 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 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.