• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.44-52
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• 2012

This paper proposes to increase an efficiency of somebody searching information by a visualization of an unseen query words with well-selected user's intent structures. If a search engine identifies user's intent to pursue information, it would be an effective search engine. To do so, it is needed that relationships between query-words are to be visible after recovering words lost during formulated, and that an intention structure/elements is to be established. This paper will review previous studies, after then, define a simple structure of the search intent, and show a process to expand and to generate the query words appropriate to the intent structure with a method for the visualization of the query words. In this process, some examples and tests are necessary that one of the multiple intent structured layers is to assign to a range of query-words. Increasing/Decreasing an efficiency are analyzed to find. Future research is needed how to automate a process to extend structural nodules of user's intent.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.135-145
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• 2005

Without detailed exact knowledge of a retrieval collection, most users find it difficult to formulate effective queries. In fact, most users may spend large amount of time formulating queries in order to obtain their desired result. A method to overcome this difficulty is to use query expansion that reformulates better query from initial query. In this paper we propose concept based query evaluation method using concept of class that retrieved from initial query. This concept is expanded through thesaurus. For efficiency evaluation of query expansion, we defined most critical value through a simulation and compared precision and recall each other.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.139-147
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• 2009

BLAST (Basic Local Alignment Search Tool) is a best well-known tool in a bioinformatics area. BLAST quickly compares input sequences with annotated huge sequence databases and predicts their functions. It helps biologists to make it easy to annotate newly found sequences with reduced experimental time, scope, and cost. However, as the amount of sequences is increasing remarkably with the advance of sequencing machines, performance of BLAST has been a critical issue and tried to solve it with several alternatives. In this paper, we propose a new PC-Based Cluster system (E-Cluster), a new physical data design methodology (logical partitioning technique) and a query routing technique (intra-query routing). To verify our system, we measure response time, speedup, and efficiency for various sizes of sequences in NR (Non-Redundancy) database. Experimental result shows that proposed system has better speedup and efficiency (maximum 600%) than those o( conventional approaches such as SMF machines, clusters, and grids.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4908-4928
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• 2017

Recent research into wireless sensor network (WSN)-related technology that senses various data has recognized the need for spatio-temporal queries for searching necessary data from wireless sensor nodes. Answers to the queries are transmitted from sensor nodes, and for the efficient transmission of the sensed data to the application server, research on index processing methods that increase accuracy while reducing the energy consumption in the node and minimizing query delays has been conducted extensively. Previous research has emphasized the importance of accuracy and energy efficiency of the sensor node's routing process. In this study, we propose an itinerary-based R-tree (IR-tree) to solve the existing problems of spatial query processing methods such as efficient processing and expansion of the query to the spatio-temporal domain.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.266-272
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• 2002

Recently various of information suppliers provide information via WWW so the necessary of search engine grows larger. However the efficiency of most search engines is low comparatively because of using simple pattern match technique between user's query and web document. And a manifest contents of query for special expert field so much worse A specialized search engine returns the specialized information depend on each user's search goal. It is trend to develop specialized search engines in many countries. For example, in America, there are a site that searches only the recently updated headline news and the federal law and the government and and so on. However, most such engines don't satisfy the user's needs. This paper proposes the specialized search engine for ocean information that uses user's query related with ocean and search engine uses the association rules in web data mining. So specialized search engine for ocean provides more information related to ocean because of raising recall about user's query

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.794-804
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• 2008

In this paper, we analyze the performance of probabilistic slotted anti-collision algorithm used in EPCglobal Class-1 Generation-2 (Gen2). To increase throughput and system efficiency, and to decrease tag identification time and collision ratio, we propose new tag anti-collision algorithms, which are FAFQ (fired adjustable flamed Q) algorithm and AAFQ (adaptive adjustable framed Q) algorithm, by using QueryAdjust command. We also propose grouping method based on Gen2 to improve the efficiency of tag identification. The simulation results show that all the proposed algorithms outperform Q algorithm, and AAFQ algorithm performs the best. That is, AAFQ has an increment of 5% of system efficiency and a decrement of 4.5% of collision ratio. For FAFQ and AAFQ algorithm, the performance of grouping method is similar to that of ungrouping method. However, for Q algorithm in Gen2, grouping method can increase throughput and system efficiency, and decrease tag identification time and collision ratio compared with ungrouping method.

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

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.69-77
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• 2009

Stream service environment demands real-time query processing for voluminous data which are ceaselessly delivered from tremendous sources. Typical R-tree based query processing technologies cannot efficiently handle such situations, which require repetitive and inefficient exploration from the tree root on every data event. However, many stream data including sensor readings show high locality, which we exploit to reduce the search space of queries to explore. In this paper, we propose a query processing scheme exploiting the locality of stream data. From the simulation, we conclude that the proposed scheme performs much better than the traditional ones in terms of scalability and exploration efficiency.

• 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 KIISE:Databases
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• v.33 no.3
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• pp.327-341
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• 2006

As we are getting to deal with more applications that generate streaming data such as sensor network, monitoring, and SDI (selective dissemination of information), active research is being conducted to support efficient processing of queries over streaming data. The applications on the Web environment like SDI, among others, require query processing over streaming XML data, and its investigation is very important because XML has been established as the standard for data exchange on the Web. One of the major problems with the previous systems that support query processing over streaming XML data is that they cannot deal adaptively with dynamically changing stream because they rely on static query plans. On the other hand, the stream query processing systems based on relational data model have achieved adaptiveness in query processing due to query operator routing. In this paper, we propose a system of adaptive query processing over streaming XML data in which the model of adaptive query processing over streaming relational data is applied. We compare our system with YFiiter, one of the representative systems that provide XML stream query processing capability, to show efficiency of our system.