• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.161-168
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• 2009

Recently, use of various position base servicesthat collect position information for moving object and utilize in real life is increasing by the development of wireless network technology. Accordingly, new index structures are required to efficiently retrieve the consecutive positions of moving objects. This paper addresses an improved trajectory split algorithm for the purpose of efficiently supporting spatio-temporal range queries using index structures that use Minimum Bounding Rectangles(MBR) as trajectory approximations. We consider volume of Extended Minimum Bounding Rectangles (EMBR) to be determined by average size of range queries. Also, Use a priority queue to speed up our process. This algorithm gives in general sub-optimal solutions with respect to search space. Our improved trajectory split algorithm is going to derive minimizing volume of EMBRs better than previously proposed split algorithm.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.845-854
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• 2004

Motif databases are used in the function and structure prediction of proteins. The frequency of use about these databases increases continuously because of protein sequence data growth. Recently, many researches about motif resource integration are proceeding. However, existing motif databases were developed independently, thus these databases have a heterogeneous search result problem. Database intnegration for this problem resolution has a periodic update problem, a complex query process problem, a duplicate database entry handling problem and BML support problem. Therefore, in this paper, we suppose a database resource integration method for these problem resolution, describe periodically integrated database update method and XML transformation. finally, we estimate the implementation of our prototype and a case database.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.85-92
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• 2015

As computer game maps get more elaborate, path-finding by using $A^*$ algorithm in grid-based game maps becomes bottlenecks of the overall game performance. It is because the search space becomes large as the number of nodes increases with detailed representation in cells. In this paper we propose an efficient pathfinding method in which the computer game maps in a regular grid is converted into the polygon-based representation of the list of vertices and then the visibility information about vertices of polygons can be utilized. The conversion to the polygon-based map does not give any effect to the real-time query process because it is preprocessed offline. The number of visited nodes during search can be reduced dramatically by designing heuristics using visibility information of vertices that make the accuracy of the estimation enhanced. Through simulations, we show that the proposed methods reduce the search space and the search time effectively while maintaining the advantages of the grid-based method.

• Journal of Korean Medical classics
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• v.32 no.3
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• pp.131-146
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• 2019

Objectives : The aim of this paper is to study the methodology for effectively analyzing the "Jinguiyaolue" prescriptions using database, and to explore possibilities of applying the data construction and query produced in the process to comparative research with other texts in the future. Methods : Using "Xinbianzhongjingquanshu(新編仲景全書)" as original script, the contents of "Jinguiyaolue" were entered into the database, in which one verse would be separated according to content for individual usage. Also, data with medicinal construction and disease pattern information of the previously constructed "Shanghanlun" database designed for comparison with other texts was applied for comparative analysis. Results : For input and analysis, 6 tables and 12 queries were made and used. Formulas were accessible by using herbal combinations, and applications of these formulas could be assembled for comparison. Formulas were also accessible by using disease pattern combinations, and combinations of herbs and disease pattern together were also applicable. In comparison with other texts, examples and frequency of usage of herbs could be relatively accurately compared, while disease patterns could not easily be compared. Conclusions : Herbal combinations, disease pattern combinations could yield related texts and herb/disease pattern combinations of the prescriptions in the "Jinguiyaolue", which shortened time needed for research among formulas in texts. However, standardization research for disease pattern is necessary for a more accurate comparative study that includes disease pattern information.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.765-777
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• 2022

The SABER algorithm, a PKE/KEM algorithm presented in NIST PQC Standardization Round 3, is an algorithm based on the Module-LWR problem among lattice-based problems and has a Meta-PKE structure. At this time, the secret information used in the encryption process is called a ephemeral key, and in this paper, the ephemeral key reuse attack using the Meta-PKE structure is described. For each parameter satisfying the security strengths required by NIST, we present a detailed analysis of the previous studies attacked using 4, 6, and 6 queries, and improve them, using only 3, 4, and 4 queries. In addition, we introduce how to reduce the computational complexity of recovering ephemeral keys with a single query from the brute-force complexity on the n-dimension lattice, 2^7.91×n, 2^10.51×n, 2^12.22×n to 2^4.91×n, 2^6.5×n, 2^6.22×n, for each parameter, and present the results and limitations.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.238-244
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• 2022

Civil projects are associated with many uncertainties because they involve a long duration, many resources, a large area, and many supply chains. Therefore, the price of a civil project is not simply proportional to the quantity and unit price of the item but has a variable value, including uncertainty risk. This study investigates the influence of the uncertainty factors in the pre-bid clarification document on bid price formation during the project bidding phase. To this end, civil projects from the California Department of Transportation (Caltrans) were used as research data. This study randomly selected fifty sample data from each of twelve counties from 2008-to 2020: six hundred. The authors observed that each project sample had 0 to n query cases due to uncertainty. Then, this study examined the project uncertainty cases and categorized them into the following four uncertainty factors: 'conflict' (UF1), 'impossibility' (UF2), 'lack' (UF3), and 'missing' (UF4). Under the extracting process, the cases are classified into four uncertainty factors. With the project not containing any uncertainty factors as a control group, the project containing these uncertainty factors was designated as an experimental group. After comparing the bidder's price, the experimental group's bid price was higher than the control group's. This result suggests that uncertainty factors in bid documents induce bidders to set a high bid price as a defense against uncertainty.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.61-83
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• 2012

A knowledge map describes the network of related knowledge into the form of a diagram, and therefore underpins the structure of knowledge categorizing and archiving by defining the relationship of the referential navigation between knowledge. The referential navigation between knowledge means the relationship of cross-referencing exhibited when a piece of knowledge is utilized by a user. To understand the contents of the knowledge, a user usually requires additionally information or knowledge related with each other in the relation of cause and effect. This relation can be expanded as the effective connection between knowledge increases, and finally forms the network of knowledge. A network display of knowledge using nodes and links to arrange and to represent the relationship between concepts can provide a more complex knowledge structure than a hierarchical display. Moreover, it can facilitate a user to infer through the links shown on the network. For this reason, building a knowledge map based on the ontology technology has been emphasized to formally as well as objectively describe the knowledge and its relationships. As the necessity to build a knowledge map based on the structure of the ontology has been emphasized, not a few researches have been proposed to fulfill the needs. However, most of those researches to apply the ontology to build the knowledge map just focused on formally expressing knowledge and its relationships with other knowledge to promote the possibility of knowledge reuse. Although many types of knowledge maps based on the structure of the ontology were proposed, no researches have tried to design and implement the referential navigation-enabled knowledge map. This paper addresses a methodology to build the ontology-based knowledge map enabling the referential navigation between knowledge. The ontology-based knowledge map resulted from the proposed methodology can not only express the referential navigation between knowledge but also infer additional relationships among knowledge based on the referential relationships. The most highlighted benefits that can be delivered by applying the ontology technology to the knowledge map include; formal expression about knowledge and its relationships with others, automatic identification of the knowledge network based on the function of self-inference on the referential relationships, and automatic expansion of the knowledge-base designed to categorize and store knowledge according to the network between knowledge. To enable the referential navigation between knowledge included in the knowledge map, and therefore to form the knowledge map in the format of a network, the ontology must describe knowledge according to the relation with the process and task. A process is composed of component tasks, while a task is activated after any required knowledge is inputted. Since the relation of cause and effect between knowledge can be inherently determined by the sequence of tasks, the referential relationship between knowledge can be circuitously implemented if the knowledge is modeled to be one of input or output of each task. To describe the knowledge with respect to related process and task, the Protege-OWL, an editor that enables users to build ontologies for the Semantic Web, is used. An OWL ontology-based knowledge map includes descriptions of classes (process, task, and knowledge), properties (relationships between process and task, task and knowledge), and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. Therefore a knowledge network can be automatically formulated based on the defined relationships, and the referential navigation between knowledge is enabled. To verify the validity of the proposed concepts, two real business process-oriented knowledge maps are exemplified: the knowledge map of the process of 'Business Trip Application' and 'Purchase Management'. By applying the 'DL-Query' provided by the Protege-OWL as a plug-in module, the performance of the implemented ontology-based knowledge map has been examined. Two kinds of queries to check whether the knowledge is networked with respect to the referential relations as well as the ontology-based knowledge network can infer further facts that are not literally described were tested. The test results show that not only the referential navigation between knowledge has been correctly realized, but also the additional inference has been accurately performed.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.