• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.309-318
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• 2009

The researches on the system that automatically records and retrieves one's everyday life is relatively actively worked recently. These systems, called personal memex or life log, usually entail dedicated devices such as SenseCam in MyLifeBits project. This research paid attention to the digital devices such as mobile phones, credit cards, and digital camera that people use everyday. The system enables a person to store everyday life systematically that are saved in the devices or the deviced-related web pages (e.g., phone records in the cellular phone company) and to refer this quickly later. The data collection agent in the proposed system, called MyMemex, collects the personal life log "web data" using the web services that the web sites provide and stores the web data into the server. The "file data" stored in the off-line digital devices are also loaded into the server. Each of the file data or web data is viewed as a memex event that can be described by 4W1H form. The different types of data in different services are transformed into the memex event data in 4W1H form. The memex event ontology is used in this transform. Users can sign in to the web server of this service to view their life logs in the chronological manner. Users can also search the life logs using keywords. Moreover, the life logs can be viewed as a diary or story style by converting the memex events to sentences. The related memex events are grouped to be displayed as an "episode" by a heuristic identification method. A result with high accuracy has been obtained by the experiment for the episode identification using the real life log data of one of the authors.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.310-316
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• 2022

The smart tourism service provides services such as travel planning and tour guides to tourists using key technologies of the 4th industrial revolution, such as the Internet of Things, communication infrastructure, big data, artificial intelligence, AR/VR, and drones. We are developing smart tourism services such as recommended travel products, my travel itinerary, tourism information, and chatbots for tourists through the smart tourism app. In this paper, we develop a smart tourism service system that provides real-time location-based tourism information and weather information to tourists. The smart tourism service system consists of a smart tourism app, a smart tourism information system, and an IoT data collection device. The smart tourism information system receives weather information from the IoT data collection device installed in the tourist destination. The location-based smart tourism service is provided as a smart tourism app in the smart tourism information system according to the Beacon's UUID in the IoT data collection device. The smart tourism information system stores the Beacon's UUIDs received from tourists and provides a safe hiking service for tourists.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.195-218
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• 2005

The emergence of ubiquitous computing environment will change the service architecture of business information systems such as Decision Support System(DSS), which will be a new application. Recent mobile DSSs allow the decision makers to be benefited from web and mobile technology. However, they seldom refer to context data, which are useful for proactive decision support. Meanwhile, ubiquitous applications so far provide restricted personalization service using context and preference of the user, that is, they do not fully make use of decision making capabilities. Hence, this paper aims to describe how the decision making capability and context-aware computing are jointly used to establish ubiquitous applications. To do so, an amended DSS paradigm: CKDDM(Context-Knowledge-Dialogue-Data-Model) is proposed in this paper. What will be considered for the future decision support systems when we regard ubiquitous computing technology as an inevitable impact that enforces the change of the way of making decisions are described. Under the CKDDM paradigm, a framework of ubiquitous decision support systems(ubiDSS) is addressed with the description of the subsystems within. To show the feasibility of ubiDSS, a prototype system, CAMA-myOpt(Context-Aware Multi Agent System-My Optimization) has been implemented as an illustrative example system.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.268-270
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• 2023

기존 Web2.0 시대의 플랫폼 기업은 서비스를 통해 생성된 개인 데이터로 다양한 비즈니스를 창출해왔다. 하지만 데이터 제공자인 개인은 해당 수익에서 제외되는 모순된 상황에 놓였다. 이에 개인이 자신의 데이터를 적극 관리·통제하면서 능동적으로 활용할 수 있는 개념인 마이데이터(MyData)가 등장했다. 국내에서는 '20.8월 데이터3법(개인정보보호법, 신용정보법, 정보통신망법)이 통과되면서 신용정보법에 근거해 금융 분야 마이데이터 서비스가 활성화되기 시작했다. 그러나 현존하는 마이데이터 플랫폼은 중앙화된 시스템으로 본래 취지와 다르게 개인의 데이터 소유권과 통제권을 보장하기에 부족하다. 이에 본 논문에서는 기존 마이데이터 플랫폼의 한계점을 분석하고, Web3.0 등 변화하는 환경에서 개인의 데이터 주권을 보장하고, 데이터 가치를 공정하게 분배받을 수 있는 DID 플랫폼 기반의 마이데이터 서비스 모델을 제안한다.

• Journal of Information Management
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• v.42 no.2
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• pp.103-120
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• 2011

The astronomical data obtained by observations of celestial objects movements are useful in our daily life. These data - such as 1) sunrise and sunset data, 2) data transformation between lunar calendar and solar calendar, 3) solar eclipse and lunar eclipse data, and 4) sun's altitude and azimuth data - were used to important things in the agricultural society and ancient kingdom. In the modern society, these data have been used continuously for the legal disputes, construction of buildings and the other areas. This is a good example of pure science data using directly in our life. However there are difficulties to public service because all the data were printed as an Almanac. Using MySQL, we constructed web-based service system which makes to use them easily. This database system was made for the quick search using the calculated orbital data of celestial bodies.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.267-276
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• 2016

This study was conducted to explore the possibilities of educational use of social curation service, which is the third-generation social networking service called the "matters of interest type." To this end, the study selected nine college students, then have them use social curation service for about 50 days, and analyzed their responses by performing the focus group interview (FGI). As a result of the analysis, social curation service was found to have strong points, including collection of various types of information, classification and summary through creation of my own category, possibility of constant updating of matters of interest, and reduction of relative deprivation compared to existing social networking services. To solve these issues, constant promotion was needed. From the aspect of educational use, it was found that social curation service had possibilities for individuals and teams: For individuals, the service enabled voluntary search and use of information, configuration of my own data, and facilitation of self-directed learning. For teams, it enabled discussion and presentation activities through sharing.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.43-50
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• 2015

Since the popularity of social network services (SNS) rise, the data produced from them is rapidly increased. The SNS data includes personal propensity or interest and propagates rapidly so there are many requests on analyzing the data for applying the analytic results to various fields. New technologies and services for processing and analyzing big data in the real-time are introduced but it is hard to apply them in a short time and low coast. In this paper, an efficient method to build a tweet analysis system without inducing new technologies or service platforms for handling big data is proposed. The proposed method was verified through building a prototype monitoring system to collect and analyze tweets using the MySQL database and the PHP scripts.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Proceedings of the KSRS Conference
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• v.2
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• pp.892-895
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• 2006

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors are examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water and water vapor and surface wind on surface emissivity on the microwave signatures. A plane parallel radiative transfer model is used to compute brightness temperatures at SSM/I frequencies over surfaces that contain open water, first-year (FY) ice and multi-year (MY) ice and their combinations. Synthetic retrievals in this study use the NASA Team (NT) algorithm for the estimation of sea ice concentrations. This study shows that if the satellite sensor’s field of view is filled with only FY ice the retrieval is not much affected by the atmospheric conditions due to the high contrast between emission signals from FY ice surface and the signals from the atmosphere. Pure MY ice concentration is generally underestimated due to the low MY ice surface emissivity that results in the enhancement of emission signals from the atmospheric parameters. Simulation results in marginal ice areas also show that the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentration. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. In particular, our results suggest that strong surface wind is more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.1
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• pp.1-6
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• 2004

The 490 and 380 performance records of Karan Fries and Karan Swiss cows developed through crossbreeding followed by inter-se mating were evaluated for production, reproduction performance and disposal rate. Duration of study (1982-92) was grouped into five periods (1982-83; 1984-85; 1986-87; 1988-89; 1990-92) and each year was divided into four seasons (Winter: Dec.-Jan.; Spring: Feb.-March; Summer: April-June; Rainy: July-Sept.; Autumn: Oct.-Nov). Data were also classified according to sire and level of inbreeding. The least squares means of FLY (first lactation yield -305 days), FLL (first lactation length), FSP (first service period), FDP (first dry period), FCI (first calving interval ), MY/FLL (milk yield per day of first lactation length), MY/FCI (milk yield per day of first calving interval), EBV (expected breeding value) and EBE (expected breeding efficiency) were $3,173{\pm}82$ kg, $34611{\pm}$days, $143{\pm}11$ days, $75{\pm}6$ days, $423{\pm}11$ days, $10.6{\pm}0.2$ kg, $8.9{\pm}0.2$ kg, $3,380{\pm}26$ kg, and $88.2{\pm}1.3$% respectively in Karan Fries. Corresponding estimates in Karan Swiss cows were $2,616{\pm}82$ kg, $328{\pm}8$ days, $148{\pm}12$ days, $103{\pm}9$ days, $435{\pm}13$ days, $8.9{\pm}0.2$ kg, $7.2{\pm}0.3$ kg, $2,924{\pm38}$ kg and $86.0{\pm}3.8$% respectively. The effect of sire was significant on FLY, MY/FLL, MY/FCI and EBV in both the herd (p<0.01). The 10 and 15% sires showed superiority (FLY) over herd average of 11 and 7% in Karan Fries and 32 and 21% in Karan Swiss cattle respectively. Inbreeding has adversely and significantly (p<0.05) affected the FLY, MY/FLL, MY/FCI and survivability of Karan Fries females; FDP and disposal through culling of Karan Swiss heifers. The Karan Fries heifers with inbreeding above 12.5% performed 16% lower FLY to herd average. The effect of season of calving was significant on FLL, FSP and FCI (p<0.05) in Karan Fries. Summer calvers ($361{\pm}12$) were have higher lactation length and autumn calvers ($329{\pm}14$) had minimum. Period of calving significantly influenced the FSP, FCI, MY/FLL and MY/FCI in Karan Fries and FLY, MY/FLL and MY/FCI in Karan Swiss. Production efficiency traits in Karan Fries herd witnessed higher yield in the last two periods whereas, Karan Swiss heifers showed fluctuating performance. The findings suggested judicious use of available genetic variability, keeping of inbreeding under safer level and managemental interventions for the consistent improvement of both herds.