• The KIPS Transactions:PartD
• /
• v.16D no.6
• /
• pp.845-850
• /
• 2009

Use of XML data has been increased with growth of Web 2.0 environment. XML is recognized its advantages by using based technology of RSS or ATOM for transferring information from blogs and news feed. Bitmap clustering is a method to keep index in main memory based on Relational DBMS, and which performed better than the other XML indexing methods during the evaluation. Existing method generates too many clusters, and it causes deterioration of result of searching quality. This paper proposes k-Bitmap clustering method that can generate user defined k clusters to solve above-mentioned problem. The proposed method also keeps additional inverted index for searching excluded terms from representative bits of k-Bitmap. We performed evaluation and the result shows that the users can control the number of clusters. Also our method has high recall value in single term search, and it guarantees the searching result includes all related documents for its query with keeping two indices.

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

• Journal of Microbiology
• /
• v.43 no.5
• /
• pp.406-410
• /
• 2005

In contrast to Saccharomyces cerevisiae, little is known about the kinesin-like protein (KLP) in Candida albicans. The motor domain of kinesin, or KLP, contains a subregion, which is well conserved from yeast to humans. A similarity search, with the murine ubiquitous kinesin heavy chain region as a query, revealed 6 contigs that contain putative KLPs in the genome of C. albicans. Of these, the length of an open reading (ORF) of 375 amino acids, temporarily designated CaKAR3, was noticeably short compared with the closely related S. cerevisiae KAR3 (ScKAR3) of 729 amino acids. This finding prompted us to isolate a ${\lambda}$ genomic clone containing the complete CaKAR3 ORF, and here the complete sequence of CaKAR3 is reported. CaKAR3 is a C-terminus motor protein, of 687 amino acids, encoded by a non-disrupting gene. When compared with ScKAR3, the amino terminal region of 112 amino acids was unique, with the middle part of the 306 amino acids exhibiting $25\%$ identity and $44\%$ similarity, while the remaining C-terminal motor domain exhibited $64\%$ identity and $78\%$ similarity, and have been submitted to GeneBank under the accession number AY182242.

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

• Healthcare Informatics Research
• /
• v.24 no.4
• /
• pp.253-262
• /
• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• The Korean Journal of Pain
• /
• v.34 no.3
• /
• pp.271-287
• /
• 2021

Background: Postoperative pain management is crucial for patients undergoing total knee arthroplasty (TKA). There have been many recent clinical trials on post-TKA peripheral nerve block; however, they have reported inconsistent findings. In this meta-analysis, we aimed to comprehensively analyze studies on post-TKA analgesia to provide evidence-based clinical suggestions. Methods: We performed a computer-based query of PubMed, Embase, the Cochrane Library, and the Web of Science to retrieve related articles using neurothe following search terms: nerve block, nerve blockade, chemodenervation, chemical neurolysis, peridural block, epidural anesthesia, extradural anesthesia, total knee arthroplasty, total knee replacement, partial knee replacement, and others. After quality evaluation and data extraction, we analyzed the complications, visual analogue scale (VAS) score, patient satisfaction, perioperative opioid dosage, and rehabilitation indices. Evidence was rated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results: We included 16 randomized controlled trials involving 981 patients (511 receiving peripheral nerve block and 470 receiving epidural block) in the final analysis. Compared with an epidural block, a peripheral nerve block significantly reduced complications. There were no significant between-group differences in the postoperative VAS score, patient satisfaction, perioperative opioid dosage, and rehabilitation indices. Conclusions: Our findings demonstrate that the peripheral nerve block is superior to the epidural block in reducing complications without compromising the analgesic effect and patient satisfaction. Therefore, a peripheral nerve block is a safe and effective postoperative analgesic method with encouraging clinical prospects.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.151-159
• /
• 2021

This article addresses the issue of finding optimal solutions using the information technologies of accounting and analysis in modern companies. The aim of the study is to reveal available information technologies for the needs of small, medium and large businesses operating in modern conditions. This goal is achieved by using systematization, comparison, and analysis of information, obtained under the survey and open management statistics. For the first time, the paper systematizes up-to-date information of 2021 about the most popular programs, online services, platforms and cloud services that are used to improve accounting and analytical processes in enterprises of various sizes. The main global trends in software development in terms of COVID-19 pandemic have been identified. In particular, the study defines the countries that occupy the leading positions in the informatization of business processes. An attempt was made to classify information technologies by their use by various volume of businesses. The analysis of research results of the Internet search query frequency regarding the use of information technologies enabled to determine the most popular software products worldwide. The peculiarities of information technologies, their advantages and disadvantages were examined and the common and distinctive features were compared. It was determined that for the new enterprises to implement information technologies, it is necessary to conduct a step-by-step study of all available software products. The software evaluation algorithm was described to help select the optimal software for the specific business processes. The paper also describes the way to solve the problem of using accounting and analysis software for the businesses of a specific kind of activity.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.406-412
• /
• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.6
• /
• pp.63-68
• /
• 2022

Currently, there are a total of 68 emergency rooms based on Seoul, South Korea, and there is a portal site that allows you to inquire the location of the emergency room, but it is difficult to use in an actual emergency situation because it consists of selecting a gu and a self-governing dong. In addition, it may be more efficient to go to the emergency room directly because you may miss the golden time necessary for survival in a situation where you call 119 and wait for the rescue team. Therefore, in this paper, we propose a service that can quickly search the location of the emergency room based on a specific location through various functions supported by MongoDB. After downloading emergency room location data based on Seoul Metropolitan City, storing it in MongoDB, processing the data through various processing techniques, and applying a spatial index, you can query the emergency room based on distance from a specific location in real time.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.1
• /
• pp.25-33
• /
• 2009

In various fields, ISO/IEC 11179-based MDR (Metadata Registry) systems have been developed. However, the current systems do not observe the standard, so inconsistency issue between metadata arises. Most of all, there exist several problems because ISO/IEC 11179 provides no standardized access method. SQL/MDR has been suggested to resolve those problems. SQL/MDR supports search operations, but it does not provide operations for vaild building and safe access for MDR. This paper, in the aforementioned issues, suggests MCL(Metadata Control Language) to guarantee safe and easy access control. MCL offers predefined roles and authority of user groups defined in ISO/IEC 11179 Part 6, and users are assigned to a proper user group. With such a way, MCL increases usability and security.