• Journal of Korea Spatial Information System Society
• /
• v.9 no.3
• /
• pp.67-80
• /
• 2007

Recently, many studies on query processing algorithms has been done for spatial networks, such as roads and railways, instead of Euclidean spaces, in order to efficiently support LBS(location-based service) and Telematics applications. However, both a closest pairs query and an e-distance join query require a very high cost in query processing because they can be answered by processing a set of POIs, instead of a single POI. Nevertheless, the query processing cost for closest pairs and e-distance join queries is rapidly increased as the number of k (or the length of radius) is increased. Therefore, we propose both a closest pairs query processing algorithm and an e-distance join query processing algorithm using a POI-based materialization technique so that we can process closest pairs and e-distance join queries in an efficient way. In addition, we show the retrieval efficiency of the proposed algorithms by making a performance comparison of the conventional algorithms.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.4
• /
• pp.963-972
• /
• 1997

In imformation retrival, query reformulation algorithms construct querise from a set of intial input and feedback documents, and retrieval performance cna be varied by different sets of input documents. In this study, we developed a criterion for measuring the performance sensitivity of query reformulation algorithms to unput sets. In addition, we also propose a way of mesuring the changes in retrived area, (CIRA) during qucry reformulation. We cimpared CIRAs of query refromulation algorithms (i.e., query tree, DNF method, and Dillon's method) using three test sets:the CACM, CISI, and Medlars. In the experiments, the query tree showed the highest decreasing CIRA during refirmulations, which means the fastest convergence rate to an output set. For sensitivity analysis, the query tree sored the highest sensitivity to different input sets even though its differences to the tther algorithms are very small.

• Journal of Korea Spatial Information System Society
• /
• v.9 no.2
• /
• pp.67-79
• /
• 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:Databases
• /
• v.32 no.3
• /
• pp.334-343
• /
• 2005

Complex queries such as path alld twig patterns have been the focus of much research on processing XML data. Structural join algorithms use a form of encoded structural information for elements in an XML document to facilitate join processing. Recently, structural join algorithms such as Twigstack and TSGeneric- have been developed to process such complex queries, and they have been shown that the processing costs of the algorithms are linearly proportional to the sum of input data. However, the algorithms have a shortcoming that their processing costs increase with the length of a queery. To overcome the shortcoming, we propose the segment join technique to augment the structural join with structural indexes such as the 1-Index. The SegmentTwig algorithm based on the segment join technique performs joins between a pair of segments, which is a series of query nodes, rather than joins between a pair of query nodes. Consequently, the query can be processed by reading only a query node per segment. Our experimental study shorts that segment join algorithms outperform the structural join methods consistently and considerably for various data sets.

• KIISE Transactions on Computing Practices
• /
• v.21 no.1
• /
• pp.76-81
• /
• 2015

Research on top-k query processing algorithms for analyzing big data have been spotlighted recently. However, because existing top-k query processing algorithms do not provide an efficient index structure, they incur high query processing costs and cannot support various types of queries. To solve these problems, we propose a top-k query processing algorithm using a view selection method based on a grid index. The proposed algorithm reduces the query processing time by retrieving the minimum number of grid cells for the query range, by using a grid index-based view selection method. Finally, we show from our performance analysis that the proposed scheme outperforms an existing scheme, in terms of both query processing time and query result accuracy.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.3
• /
• pp.709-719
• /
• 1997

Performance of an algorithm can be mesaurde from serval aspects.Suppose thre is a query formulation al-gorithm.Even though this algorithm shows high retrival performance, ie, high recall and percision, retriveing items can rake a long time.In this study, we time complexity of automatic query reformulation algorithms, named the query Tree, DNF method, and Dillon's method, and comparethem in theoretical and practical aspects using a tral-time performance)the absolute times for each algorithm to fromulate a query)in a Sun SparcStation 2. In experiments using three test sets, CSCM, CISI, and Medlars, the query Tree algorithm was the fastest among the three algorithms tested.

• Journal of Korea Multimedia Society
• /
• v.17 no.10
• /
• pp.1182-1188
• /
• 2014

Recently, the research on database outsourcing has been actively done with the popularity of cloud computing. However, because users' data may contain sensitive personal information, such as health, financial and location information, the data encryption methods have attracted much interest. Existing data encryption schemes process a query without decrypting the encrypted databases in order to support user privacy protection. On the other hand, to efficiently handle the large amount of data in cloud computing, it is necessary to study the distributed index structure. However, existing index structure and query processing algorithms have a limitation that they only consider single-column query processing. In this paper, we propose a grid-based multi column indexing scheme and an encrypted query processing algorithm. In order to support multi-column query processing, the multi-dimensional index keys are generated by using a space decomposition method, i.e. grid index. To support encrypted query processing over encrypted data, we adopt the Hilbert curve when generating a index key. Finally, we prove that the proposed scheme is more efficient than existing scheme for processing the exact and range query.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1437-1457
• /
• 2016

In outsourced databases, the cloud provides an authorized user with querying services on the outsourced database. However, sensitive data, such as financial or medical records, should be encrypted before being outsourced to the cloud. Meanwhile, k-Nearest Neighbor (kNN) query is the typical query type which is widely used in many fields and the result of the kNN query is closely related to the interest and preference of the user. Therefore, studies on secure kNN query processing algorithms that preserve both the data privacy and the query privacy have been proposed. However, existing algorithms either suffer from high computation cost or leak data access patterns because retrieved index nodes and query results are disclosed. To solve these problems, in this paper we propose a new kNN query processing algorithm on the encrypted database. Our algorithm preserves both data privacy and query privacy. It also hides data access patterns while supporting efficient query processing. To achieve this, we devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. Through the performance analysis, we verify that our proposed algorithm shows better performance than the existing algorithms in terms of query processing times.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.4
• /
• pp.1-8
• /
• 1997

In this paper, we propose a new intelligent query processing system for relational database !systems. By analyzing previous research results related with fuzzy queries, a new intelligent query processing sysytem is developed and the role of each module including intelligent query processor is defined and :some algorithms for parser, query translation module, inference engine, semantic DB and result com-poser are suggested. By applying a typical example to the proposed intelligent query processing liysytem, reasonable results for the ambiguous query are drawn, and therefore it shows a promising model returning ordered result for both the ambiguous queries and general queries.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.5
• /
• pp.2211-2232
• /
• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.