• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.210-224
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• 2019

Given a positive integer k as input, a spatial preference query finds the k best data objects based on the scores (e.g., qualities) of feature objects in their spatial neighborhoods. Several solutions have been proposed for spatial preference queries in Euclidean space. A few algorithms study spatial preference queries in undirected spatial networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, spatial preference queries have not been thoroughly investigated in directed spatial networks where each edge has a particular orientation that makes the distance between two points noncommutative. Therefore, in this study, we present a new method called ALPS+ for processing spatial preference queries in directed spatial networks. We conduct extensive experiments with different setups to demonstrate the superiority of ALPS+ over conventional solutions.

• Food Science of Animal Resources
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• v.34 no.5
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• pp.656-664
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• 2014

The purpose of this study was to analyze myosin heavy chain (MHC) isoforms in bovine longissimus thoracis (LT) muscle by liquid chromatography (LC) and mass spectrometry (MS). LT muscles taken from Hanwoo (Korean native cattle) steer (n=3) used to separate myosin bands by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The peptide queries were obtained from the myosin bands by LC-MS/MS analysis following in-gel digestion with trypsin. A total of 33 and 43 queries were identified as common and unique peptides, respectively, of MHC isoforms (individual ions scores >43 indicate identity or extensive homology, p<0.05). MHC-1 (IIx), -2 (IIa), -4 (IIb), and -7 (slow/I) were identified based on the Mowse score (5118, 3951, 2526, and 2541 for MHC-1, -2, -4, and -7, respectively). However, more analysis is needed to confirm the expression of MHC-4 in bovine LT muscle because any query identified as a unique peptide of MHC-4 was not found. The queries that were identified as unique peptides could be used as peptide markers to confirm MHC-1 (14 queries), -2 (8 queries), and -7 (21 queries) in bovine LT muscle; no query identified as a unique peptide of MHC-4 was found. LC-MS/MS analysis is a useful approach to study MHC isoforms at the protein level.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.182-191
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• 2008

Most of previous works for skyline queries have focused only on static attributes of target objects. With the advance in mobile applications, however, the need of continuous skyline queries for moving objects has been increasing. Even though several techniques to process continuous skyline queries have been proposed recently, they cannot process subspace queries, which use only the subset of attribute dimensions. Therefore it is not feasible to utilize those methods for mobile applications which must consider moving objects and subspaces simultaneously. In this paper, we propose a dominant object-based pruning method to compute subspace skyline of moving objects efficiently at query time and present the experimental results to show the effectiveness of the proposed method.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.149-164
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• 2008

Our limited understanding of real-life music information queries is an impediment to developing music information retrieval (MIR) systems that meet the needs of real users. This study aims to contribute to developing a theorized understanding of how people seek music information by an empirical investigation of real-life queries, in particular, focusing on the accuracy of user-provided information and users' uncertainty expressions. This study found that much of users' information is inaccurate; users made various syntactic and semantic errors in providing this information. Despite these inaccuracies and uncertainties, many queries were successful in eliciting correct answers. A theory from pragmatics is suggested as a partial explanation for the unexpected success of inaccurate queries.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.46-48
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• 2006

This paper addresses the problem of monitoring continuous k-nearest neighbor (k-NN) queries. Given a set of moving (or static) objects and a set of moving (or static) query points, monitoring continuous k-NN query retrieves and updates the closest k objects to a query point continually. In order to support location based services (LBSs) in highly dynamic environments, where objects and/or queries are frequently moving, monitoring continuous queries require real-time updated results when objects and/or queries change their locations. Thus, it is important to minimize time delay for maintaining up to date the results. In this paper, we present monitoring method to shorten time delay for updating continuous k-NN queries based on the notion of result region and the minimum bounding rectangle enclosing all objects in each cell, referred to as c-MBR, in the grid index structure. Simulations are conducted to show the efficiency of the proposed method.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.43-48
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• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.375-383
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• 2010

In developing question-answering (QA) systems, it is hard to analyze natural language questions syntactically and semantically and to find exact answers to given query questions. In order to avoid these difficulties, we propose a new style of question-answering system that automatically generate natural language queries and can allow to search queries fit for given keywords. The key idea behind generating natural queries is that after significant sentences within text documents are applied to the named entity recognition technique, we can generate a natural query (interrogative sentence) for each named entity (such as person, location, and time). The natural query is divided into two types: simple type and sentence structure type. With the large database of question-answer pairs, the system can easily obtain natural queries and their corresponding answers for given keywords. The most important issue is how to generate meaningful queries which can present unambiguous answers. To this end, we propose two principles to decide which declarative sentences can be the sources of natural queries and a pattern-based method for generating meaningful queries from the selected sentences.

• Journal of the Korean Society for information Management
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• v.30 no.2
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• pp.297-315
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• 2013

This study aims to analyze and evaluate term suggestion services of major search portals, Naver and Google Korea. In particular, this study evaluated relevance and currency of related search terms provided, and analyzed characteristics such as number and distribution of terms, and queries that did not produce terms. This study also analyzed types of terms in terms of the relationship between queries and terms, and investigated types and characteristics of harmful terms and terms with grammatical errors. Finally, Korean queries and English queries, and popular queries and academic queries were compared in terms of the amount and relevance of search terms provided. The results of this study show that the relevance and currency of Naver's related search terms are somewhat higher than those of Google. Both Naver and Google tend to add terms to or delete terms from original queries, and provide identical search terms or synonym terms rather than providing entirely new search terms. The results of this study can be implemented to the portal's effective development of term suggestion services.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.3-7
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• 2009

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query but supports various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.133-138
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• 2012

Queries entered in a search box are the results of users' activities to actively seek information. Therefore, search logs are important data which represent users' information needs. The purpose of this study is to examine if there is a relationship between the results of queries automatically classified and the categories of documents accessed. Search sessions were identified in 2009 NDSL(National Discovery for Science Leaders) log dataset of KISTI (Korea Institute of Science and Technology Information). Queries and items used were extracted by session. The queries were processed using an automatic classifier. The identified queries were then compared with the subject categories of items used. As a result, it was found that the average similarity was 58.8% for the automatic classification of the top 100 queries. Interestingly, this result is a numerical value lower than 76.8%, the result of search evaluated by experts. The reason for this difference explains that the terms used as queries are newly emerging as those of concern in other fields of research.