• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.79-81
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• 2002

정보 및 네트워크 기술의 발전과 시장의 세계화는, 기업들로 하여금 e-business 환경에서 전자적으로 협업을 수행하고 보다 원활하게 정보를 교환할 수 있는 시스템을 필요하도록 만들고 있다. 본 논문은 국제 전자상거래 표준으로 권고되고 있는 ebXML e-business framework에 기반한 업무 프로세스 자동화 시스템을 제시함으로써, 여러 기업이 인터넷을 통해 업무 프로세스를 통합하는 것을 효율적으로 지원하는 것을 목적으로 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• The Journal of Society for e-Business Studies
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• v.11 no.1
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• pp.113-126
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• 2006

The scope of e-Commerce process becomes wider as the emphasis on the enterprise collaboration grows. It has expanded from its initial settings of order management or billing processes to cover various collaborative processes in the company's value-chain. In order for those collaborative e-Commerce processes to be successful, corresponding business processes should be fully supported by standard bodies. The RosettaNet consortium, one of the most representative international B2B standard bodies, has steadily provided new PIPs to support those expansions. Since individual RosettaNet PIPs correspond to unit tasks that are executed separately in or between companies, multiple PIPs have to be integrally used to properly handle larger business cases. RosettaNet implementation, however, has suffered from the lack of standard guidelines or deliverables to refer under this multi-PIP environment. In this research, a contents-based document routing system is implemented. By applying this routing system to the RosettaNet e-Logistics program where multi-PIP environment is inevitable, we verified our contents-based document routing system is effective to support multi-PIP environment flexibly.

• Journal of the Korea Computer Industry Society
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• v.5 no.8
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• pp.791-800
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• 2004

According to the cooperation between organizations increases, e-Commerce and virtual organizaions spread, because of IT development, the number of business processes increased from bounds of an organization to crossing organizational boundaries. In order to support dynamic setup of business processes among independent organizations, a formal standard schema of describing the business processes is basically required because of business process models over multiple organizations, which are defined in different fromat by business process modeling systems their own business process modeling systems. The vision of ebXML is to create a single global electronic marketplace where enterprises, without any concerns about size and geographical location, can meet and conduct their businesses with each other through the exchanges of XML-based messages. Therefore, this thesis also analyzes and implements an ebXML based choreography modeling system for supporting collaborations between organizations.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used