• Journal of KIISE:Databases
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• v.36 no.6
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• pp.455-465
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• 2009

Suffix trees are widely used in similar sequence matching for DNA. They have several problems such as time consuming, large space usages of disks and memories and data skew, since DNA sequences are very large and do not fit in the main memory. Thus, in the paper, we present a space efficient indexing method called SENoM, allowing us to build trees without merging phases for the partitioned sub trees. The proposed method is constructed in two phases. In the first phase, we partition the suffixes of the input string based on a common variable-length prefix till the number of suffixes is smaller than a threshold. In the second phase, we construct a sub tree based on the disk using the suffix sets, and then write it to the disk. The proposed method, SENoM eliminates complex merging phases. We show experimentally that proposed method is effective as bellows. SENoM reduces the disk usage less than 35% and reduces the memory usage less than 20% compared with TRELLIS algorithm. SENoM is available to query efficiently using the prefix tree even when the length of query sequence is large.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.135-148
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• 2008

The expansion of the internet has made web applications become a part of everyday lift. As a result the number of incidents which exploit web application vulnerabilities are increasing. A large percentage of these incidents are SQL Injection attacks which are a serious security threat to databases with potentially sensitive information. Therefore, much research has been done to detect and prevent these attacks and it resulted in a decline of SQL Injection attacks. However, there are still methods to bypass them and these methods are too complex to implement in real web applications. This paper proposes a simple and effective SQL Query attribute value removal method which uses Static and Dynamic Analysis and evaluates the efficiency through various experiments.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.66-72
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• 2003

Toxicogenomics is now emerging as one of the most important genomics application because the toxicity test based on gene expression profiles is expected more precise and efficient than current histopathological approach in pre-clinical phase. One of the challenging points in Toxicogenomics is the construction of intelligent database management system which can deal with very heterogeneous and complex data from many different experimental and information sources. Here we present a new Toxicogenomics database developed as a part of 'Toxicogenomics for Efficient Safety Test (TEST) project'. The TEST database is especially focused on the connectivity of heterogeneous data and intelligent query system which enables users to get inspiration from the complex data sets. The database deals with four kinds of information; compound information, histopathological information, gene expression information, and annotation information. Currently, TEST database has Toxicogenomics information fer 12 molecules with 4 efficacy classes; anti cancer, antibiotic, hypotension, and gastric ulcer. Users can easily access all kinds of detailed information about there compounds and simultaneously, users can also check the confidence of retrieved information by browsing the quality of experimental data and toxicity grade of gene generated from our toxicology annotation system. Intelligent query system is designed for multiple comparisons of experimental data because the comparison of experimental data according to histopathological toxicity, compounds, efficacy, and individual variation is crucial to find common genetic characteristics .Our presented system can be a good information source for the study of toxicology mechanism in the genome-wide level and also can be utilized fur the design of toxicity test chip.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.2
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• pp.51-73
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• 2015

The Web now provides instant access to an unprecedented amount of information that was unthinkable even 20-30 years ago. However, the full potential of the contents available through the Internet can only be realized when one can speak and understand foreign languages, especially English which accounts for more than half of web contents. In this study, we try to investigate the effect of search task languages and task complexity on searching performance. A total of thirty students enrolled at a top private university in Korea were recruited as study subjects. We set up a quasi-experimental design in which thirty subjects are randomly assigned to a set of eight different search tasks containing an equal number of simple and complex tasks and an equal number of tasks in Korean and in English. The results show that there is a significant difference between simple and complex tasks in terms of SERP time, number of queries used, correctness of results and total search time. However, task language does not seem to have affected search performance for this study group. In addition, students with high English proficiency test scores show comparable search performance in English tasks compared with lower test scores. But we note differences in behavioral patterns (different search engines used and search tactics) among the study participants.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.325-338
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• 2004

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

• Spatial Information Research
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• v.19 no.1
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• pp.93-105
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• 2011

In recent years, large and complex three-dimensional building has been constructed by the development of building technology and advanced IT skills, and people have lived there and spent a considerable time so far. Accordingly. in this sophisticatcd three-dimensional space, emergencies services or convenient information services have been in demand. In order to provide these services efficiently, understanding of topological relationships among the complex space should be supported naturally. Not on1y each method of understanding the topological relationships but also its efficiency can be different depending on different topological data models. B-rep based data model is the most widely used for storaging and representing of topological relationships. And from early 2000s, many researches on a network based topological data model have been conducted. The purpose of this study is to verify the efficiency of performance on spatial queries. As a result, Network-based topological data model is more efficient than B-rep based data model for determining the spatial relationship.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.477-482
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• 2016

As being the advanced analytics indispensable on big data for agile decision-making and tactical planning in enterprises, distributed processing platforms, such as Hadoop and Spark which distribute and handle the large volume of data on multiple nodes, receive great attention in the field. In Spark platform stack, Spark SQL unveiled recently to make Spark able to support distributed processing framework based on SQL. However, Spark SQL cannot effectively handle advanced analytics that involves machine learning and graph processing in terms of iterative tasks and task allocations. Motivated by these issues, this paper proposes the design of SQL-based big data optimal processing engine and processing framework to support advanced analytics in Spark environments. Big data optimal processing engines copes with complex SQL queries that involves multiple parameters and join, aggregation and sorting operations in distributed/parallel manner and the proposing framework optimizes machine learning process in terms of relational operations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.511-520
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• 2013

Recently, human activities have expanded from outdoor spaces to indoor spaces since a lot of complex buildings were constructed over the world. Especially, visitors in a shopping mall would like to receive specific information of interest regarding various shopping-related activities as well as shopping itself. However, when it comes to providing the information, existing guide services have some drawbacks. Firstly, the existing services cannot provide visitors with the information of other stores simply and promptly on the current location. Secondly, the services have difficulties in representation and share of the shopping-related knowledge, and in providing inferred information. Thus, the purpose of this study is to develop a method that allows topological analysis utilizing ontology technique around the current position in such shopping mall in order to provide shopping-related information. For this, the shopping activity ontology model is designed, and based on the ontology model, inferencing rules are defined in order to extract the information of interest efficiently through semantic queries. Also, a geocoding method in indoor spaces is used regarding the current location, and optimal routing analysis, which is one of topological analysis, is applied with the result from the semantic queries. As a result, an Android application is developed for 3D visualization and user interface.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.716-720
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• 2006

In this paper, we designed and proposed new routing algorithm that can support a variety of vital-sign traffic characteristic and could be applicable to USN for telemedicine by using adaptive transmission power level and increase frequency of routing request message. In proposed routing algorithm, when an emergency vital-sign traffic is applied, we use large transmission power to reduce route query response time and make the priority order in route process. On the other hand, for non emergency vital-sign traffic, we use low transmission power and adaptive decrease frequency of routing request message. which is insensitive to delay. The proposed scheme should be better QoS performance in complex USN than conventional method, which is performed based on uniform transmission power level.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.1-13
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• 1999

The modeling components in the object-oriented paradigm are based on the object, not the structured function or procedure. That is, in the past, when one wanted to solve problems, he would describe the solution procedure. However, the object-oriented paradigm includes the concepts that solve problems through interaction between objects. The object-oriented model is constructed by describing the relationship between object to represent the real world. As in object-oriented model the relationships between objects increase, the control of objects caused by their insertions, deletions, and modifications comes to be very complex and difficult. Because the loss of the referential integrity happens and the object reusability is reduced. For these reasons, the necessity of the control of objects and the visualization of the relationships between them is required. In order that we design a database necessary to implement Object Browser that has functionalities to visualize Java objects and to perform the query processing in Java object modeling, in this paper we show the processes for EER modeling on Java object and its transformation into relational database schema. In addition we implement Java Object Parser that parses Java object and inserts the parsed results into the implemented database.