• The KIPS Transactions:PartD
• /
• v.16D no.3
• /
• pp.307-316
• /
• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2006.05a
• /
• pp.37-42
• /
• 2006

GIS분야에 대한 다양한 연구가 진행됨에 따라 그 활용에 대한 관심도 확대되고 있다. Open GIS Consortium에서는 GML(Geography Markup Language)을 개발하여 이를 GIS 응용분야에 활용하고자 하는 연구가 활발히 진행되고 있다. GML데이터에서 의미 있는 정보를 추출하기 위해서는 데이터 마이닝 기법 활용이 필수적이다 최근에 데이터마이닝 기법 중 연관규칙을 이용한 탐색 방법이 제안되었다. 그러나 이 방법은 전체 데이터를 대상으로 의미 있는 정보를 탐색 하므로, 데이터 내에 포함되어 있는 부분 속성인 지리 공간적 연관성을 탐색하는데 한계를 가지고 있다. 따라서 녈 연구에서는 GML데이터에서 부분적 속성을 고려한 지역적 연관규칙 탐색 기법을 제안한다.

• Spatial Information Research
• /
• v.22 no.4
• /
• pp.11-20
• /
• 2014

This study aims to provide information about the research-trends in the area of Geospatial Information using text-mining methods. We derived the National R&D Reports and papers from NDSL(National Discovery for Science Leaders) site. And then we preprocessed their key-words and classified those in separable sectors. We investigated the appearance rates and changes of key-words for R&D reports and papers. As a result, we conformed that the researches concerning applications are increasing, while the researches dealing with systems are decreasing. Especially, with in the framework of the keyword, '3D-GIS', 'sensor' and 'service' xcept ITS are emerging. It could be helpful to investigate research items later.

• Journal of KIISE:Databases
• /
• v.29 no.5
• /
• pp.335-346
• /
• 2002

LBS(Location Based Services) provide the location-based information to its mobile users. The primary functionality of these services is to provide useful information to its users at a minimum cost of resources. The functionality can be implemented through data mining techniques. However, conventional data mining researches have not been considered spatial and temporal aspects of data simultaneously. Therefore, these techniques are inappropriate to apply on the objects of LBS, which change spatial attributes over time. In this paper, we propose a new data mining technique for identifying the temporal patterns from the series of the locations of moving objects that have both temporal and spatial dimension. We use a spatial operation of contains to generalize the location of moving point and apply time constraints between the locations of a moving object to make a valid moving sequence. Finally, the spatio-temporal technique proposed in this paper is very practical approach in not only providing more useful knowledge to LBS, but also improving the quality of the services.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10b
• /
• pp.78-80
• /
• 2000

클러스터링이란 주어진 데이터들을 유사한 성질을 가지는 군집으로 나누는 것으로 많은 분야에서 응용되고 있으며, 특히 최근 관심의 대상인 데이터 마이닝의 중요한 기술로서 활발히 응용되고 있다. 클러스터링에 있어서 기존의 알고리즘들은 지역적 최적해에 수렴하는 것과 사전에 클러스터 개수를 미리 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적해를 찾는 진화알고리즘을 사용하여 지역적 최적해에 수렴되는 문제점을 개선하였으며, 자동으로 적절한 클러스터 개수를 결정할 수 있게 하였다. 또한 진화알고리즘의 단점인 탐색공간의 확대에 따른 탐색시간의 증가는 휴리스틱 연산을 정의하여 개선하였다. 제안한 알고리즘의 성능 및 타당성을 보이기 위해 가우시안 분포 데이터를 사용하여 제안한 알고리즘의 성능이 우수함을 보였다.

• Journal of KIBIM
• /
• v.13 no.3
• /
• pp.39-47
• /
• 2023

This study aims to introduce a model for enhancing community well-being through the utilization of public open data. To objectively assess abstract notions of residential satisfaction, text data from complaints is analyzed. By leveraging accessible public data, costs related to data collection are minimized. Initially, relevant text data containing civic complaints is collected and refined by removing extraneous information. This processed data is then combined with meaningful datasets and subjected to topic modeling, a text mining technique. The insights derived are visualized using Geographic Information System (GIS) and Application Programming Interface (API) data. The efficacy of this analytical model was demonstrated in the Godeok/Gangil area. The proposed methodology allows for comprehensive analysis across time, space, and categories. This flexible approach involves incorporating specific public open data as needed, all within the overarching framework.

• The Journal of Society for e-Business Studies
• /
• v.15 no.4
• /
• pp.101-121
• /
• 2010

This paper presents an intelligent prefetching technique that significantly improves performance of hybrid fash-disk storage, a combination of flash memory and hard disk. Since flash memory embedded in a hybrid device is much faster than hard disk in terms of I/O operations, it can be utilized as a 'cache' space to improve system performance. The basic strategy for prefetching is to utilize sequential pattern mining, with which we can extract the access patterns of objects from historical access sequences. We use two techniques for enhancing the performance of hybrid storage with prefetching. One of them is to modify a FAST algorithm for mapping the flash memory. The other is to extend the unit of prefetching to a block level as well as a file level for effectively utilizing flash memory space. For evaluating the proposed technique, we perform the experiments using the synthetic data and real UCC data, and prove the usability of our technique.

• Tunnel and Underground Space
• /
• v.25 no.4
• /
• pp.303-319
• /
• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.

• Tunnel and Underground Space
• /
• v.32 no.3
• /
• pp.173-193
• /
• 2022

In this study, international standardization trend of smart mining technology was analyzed focusing on the guidelines developed by GMG (Global Mining Guidelines Group). GMG is a non-profit organization that unites the global mining community. It was established to promote mining safety, innovation and sustainability. Currently, GMG's working group consists of artificial intelligence, asset management, autonomous mining, cybersecurity, data access and usage/interoperability, the electric mine, mineral processing, underground mining, and sustainability. Guideline development projects related to smart mining technology are being conducted in artificial intelligence, autonomous mining, cybersecurity, data access and usage/interoperability, and underground mining. As of April 2022, eight types of smart mining-related guidelines have been published through pre-launch, launch, guideline definition, contents generation, technical editing/layout/final review, and voting process. It is judged that the GMG guidelines can be an important reference for the development of domestic smart mining technology standards.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.5
• /
• pp.37-47
• /
• 2013

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.