• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.729-734
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• 2005

In order to access XML data stored in object databases, an efficient index scheme is inevitable. There have been several index schemes that can be used to efficiently retrieve XML data stored In object databases, but they are all the single path indexes that support indexing along a single schema path. Henee, if a query contains an extended path which is denoted by wild character ('*'), a query processor has to examine multiple index objects, resulting in poor performance and inconsistent index management. In this paper, we propose MPI (Multi-Path Index) scheme as a new index scheme that provides the functionality of multiple path indexes more efficiently, while it uses only one index structure. The proposed scheme is easy to manage since it considers the extended path as a logically single schema path. It is also practical since it can be implemented by little modification of the B -tree index structure.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• Journal of KIISE
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• v.42 no.11
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• pp.1303-1313
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• 2015

MapReduce provides high levels of system scalability and fault tolerance for large-size data processing. A MapReduce-based k-nearest-neighbor(k-NN) join algorithm seeks to produce the k nearest-neighbors of each point of a dataset from another dataset. The algorithm has been considered important in bigdata analysis. However, the existing k-NN join query-processing algorithm suffers from a high index-construction cost that makes it unsuitable for the processing of bigdata. To solve the corresponding problems, we propose a new grid-based, k-NN join query-processing algorithm. Our algorithm retrieves only the neighboring data from a query cell and sends them to each MapReduce task, making it possible to improve the overhead data transmission and computation. Our performance analysis shows that our algorithm outperforms the existing scheme by up to seven-fold in terms of the query-processing time, while also achieving high extent of query-result accuracy.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.754-765
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• 2006

In XML keyword search, a search result is defined as a set of the smallest elements (i.e., least common ancestors) containing all query keywords and a granularity of indexing is an XML element instead of a document. Under the conventional index structure, all least common ancestors produced by the combination of the elements, each of which contains a query keyword, are considered as a search result. In this paper, to avoid unnecessary operations of producing the least common ancestors and reduce query process time, we describe a way to construct a partitioned index composed of several partitions and produce a search result by merging those partitions if necessary. When a search result is restricted to be composed of the least common ancestors whose depths are higher than a given minimum depth, under the proposed partitioned index structure, search systems can reduce the query process time by considering only combinations of the elements belonging to the same partition. Even though the minimum depth is not given or unknown, search systems can obtain a search result with the partitioned index, which requires the same query process time to obtain the search result with non-partitioned index. Our experiment was conducted with the XML documents provided by the DBLP site and INEX2003, and the partitioned index could reduce a substantial amount of query processing time when the minimum depth is given.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.513-525
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• 2006

Search results of XML keyword search are defined generally as the most specific elements containing all query keywords in the literature. The labels of XML elements and semantic information such as ontology, conceptual model, thesaurus, and so on, are used to improve the preciseness of the search results. This paper presents a hierarchical index for an efficient XML keyword query processing on the condition that returnable search concepts are defined and users' query concepts can be interpreted with the help of the semantic information. The hierarchical index separately stores the XML elements containing a keyword on the basis of the hierarchical relations of the concepts that the XML elements belong to, and makes it possible to obtain least common ancestors, which are candidates for the search results, with selectively reading the elements belonging to the concepts relevant to query concepts and without considering all the combinations of the elements having been read. This paper deals with how to organize the hierarchical index and how to process XML keyword queries with the index. In our experiment with the DBLP XML document and the XML documents in the INEX2003 test set, the hierarchical index worked well.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.14-17
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• 2013

RDF data is widely used for exchanging data nowadays to enable semantic web era. This leads to the need for storing and retrieving these data efficiently and effectively. Recently, the structure index in graph-based perspective is considered as a promising approach to deal with issues of complex query graphs. However, even though there are many researches based on structure indexing, there can be a better architectural approach instead of addressing the issue as a part. In this research, we propose architecture for storing, query processing and retrieving RDF data in efficient manner using structure indexing. Our research utilizes research results from iStore and 2 relation-based approaches and we focus on improving query processing to reduce the time of loading data and I/O cost.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.429-437
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• 2003

Over the last decades, improvements in CPU speed have greatly exceeded those in memory and disk speeds by orders of magnitude and this enabled the use of compression techniques to reduce the database size as well as the query cost. Although compression techniques are employed in various database researches, there is little work on compressing multi-dimensional index structures. In this paper, we propose an efficient compression method called the hybrid encoding method (HEM) that is tailored to multi-dimensional indexing structures. The HEM compression significantly reduces the query cost and the size of multi-dimensional index structures. Through mathematical analyses and extensive experiments, we show that the HEM compression outperforms an existing method in terms of the index size and the query cost.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.183-186
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• 2012

To process continuous queries on a sensor network, it is required to transfer query predicates and build a query index on each sensor node. However, if we transfer query predicates to all sensor nodes, it makes the number of messages for query predicates increase. In this paper, we propose the scheme to construct the tree based relationship structure using data region of the sensor node and select the target nodes to transfer query predicates. we also implement the tree based relationship structure and measure the number of messages for sending predicates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.923-926
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• 2010

A sensor network system processes user queries using the recent field data collected by each sensor node. To process user queries, the system propagates the queries to the specific sensor nodes which have the relevant data and aggregates the results of the queries. However, if continuous queries are processed by the existing scheme, the system has the problem where the queries are propagated repeatedly. In this paper, we propose the query processing scheme to process the continuous queries over the sensor streaming data. To do this, each sensor node builds its own query index on its node. And, we present the scheme to deal with the unexpected data rising on the sensor node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3087-3103
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• 2022

Nearest neighbor query in location-based services has become a popular application. Aiming at the shortcomings of the privacy protection algorithms of traditional ciphertext nearest neighbor query having the high system overhead because of the usage of the double Hilbert curves and having the inaccurate query results in some special circumstances, a privacy protection algorithm of ciphertext nearest neighbor query which is based on the single Hilbert curve has been proposed. This algorithm uses a single Hilbert curve to transform the two-dimensional coordinates of the points of interest into Hilbert values, and then encrypts them by the order preserving encryption scheme to obtain the one-dimensional ciphertext data which can be compared in numerical size. Then stores the points of interest as elements composed of index value and the ciphertext of the other information about the points of interest on the server-side database. When the user needs to use the nearest neighbor query, firstly calls the approximate nearest neighbor query algorithm proposed in this paper to query on the server-side database, and then obtains the approximate nearest neighbor query results. After that, the accurate nearest neighbor query result can be obtained by calling the precision processing algorithm proposed in this paper. The experimental results show that this privacy protection algorithm of ciphertext nearest neighbor query which is based on the single Hilbert curve is not only feasible, but also optimizes the system overhead and the accuracy of ciphertext nearest neighbor query result.