Earticle

최근 미국과 EU 등 주요 선진국은 빅테크 플랫폼 기업의 반독점 이슈와 데이터 주권에 대한 규 제를 강화하고 있다. 구글, 아마존, 메타, 애플 등이 대표적인 빅테크 플랫폼 기업이다. 이들은 단기간 에 사회·경제·문화 등 많은 부분의 혁신을 견인하며 거대 기업으로 성장했으나, 반면 감시 자본주의의 도래, 독과점 이슈, 대규모 개인정보 유출 등 많은 사회적 문제를 야기하고 있다. 이에 빅테크 플랫폼 기업에 의존하는 현재의 체제를 전환하기 위한 새로운 패러다임에 대한 요구가 꾸준히 제기되고 있으 며, 대표적인 예로 웹3.0을 들 수 있다. 웹3.0은 과거의 지능형 웹과는 달리 블록체인, 가상화폐, NFT, 인공지능, 메타버스 등 다양한 용어와 기술들을 포함한다. 그러나 아직은 웹3.0에 대한 개념적 정의 및 범위가 명확하지 않은 초기 단계로, 새로운 미래 인터넷의 시작이라는 긍정적인 시각과, 벤처 캐피 탈 중심의 새로운 독과점의 형태 또는 단순한 마케팅 용어라는 부정적인 시각이 공존하고 있다. 그러 나 기존 웹의 부작용에 대한 반발로 패러다임의 전환에 대한 시도가 활발하게 전개되는 것은 사실이 며, 이러한 흐름에 대응하기 위해 웹3.0의 실체를 파악하고, 웹3.0의 시대에 대비하는 노력은 필요하 다. 이에 본 논문은 웹3.0의 등장배경과 의미를 포함하여 개념을 정의하고, 웹3.0을 실현하는 핵심기 술의 개발동향과 시장분석을 바탕으로, 웹3.0 시대에 대비하여 산업 활성화를 위한 전략을 제안한다.

Recently, major developed countries such as the US and EU are strengthening regulations on antitrust issues and data sovereignty of big tech platform companies. Google, Amazon, Meta, and Apple are representative big tech platform companies. They have grown into giant corporations by driving innovation in many areas such as society, economy, and culture in a short period of time. Accordingly, a demand for a new paradigm to transform the current system dependent on big tech platform companies is constantly being raised, and a representative example is Web 3.0. Unlike the intelligent web of the past, Web 3.0 includes various terms and technologies such as blockchain, cryptocurrency, NFT, artificial intelligence, and metaverse. However, as the conceptual definition and scope of Web 3.0 are still in the early stage, there is a positive view that it is the beginning of a new future Internet, and a negative view that it is a form of a new monopoly centered on venture capital or a simple marketing term. However, it is true that attempts at a paradigm shift are being actively pursued as a reaction against the side effects of the existing web. In this regard, this paper defines the concept including the background and meaning of Web 3.0, and proposes a strategy for revitalizing the industry in preparation for the Web 3.0 era based on market analysis and development trends of core technologies that realize Web 3.0. .

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

본 연구에서는 e-마켓플레이스 로그 데이터 분석과 지능형 검색 방법을 활용하여 특정 기업을 위한 최적의 타겟 시장을 추천하는 시스템을 제안한다. 특정 시장에 속한 e-마켓플레이스 사용자의 관 심과 트렌드 분석을 위해 구글 애널리틱스를 활용한 로그 분석이 가능하다. 또한 본 연구에서는 특정 기업의 제품을 위한 최적 수출 시장 검색을 위해 개념 계층 구조에 기반한 지능형 검색 방법론을 활 용한다. 본 연구에서 제안한 방법론을 활용하면 기업은 개념 계층 구조 상의 지식 표현 방법을 이용 하여 자사 제품을 위한 최적의 시장 정보를 제공받을 수 있다.

This study aims to develop a new system that recommends the best market for a company using e-marketplace log data analysis and intelligent search methods, including both business- to-business transactions(B2B) and business-to-consumer transactions(B2C). To analyze the interests and trends of e-marketplace users, we can use available online services such as Google Analytics to analyze transactions by performing log analysis. Also this paper takes advantage of an intelligent search methods based on the concept hierarchy in order to increase the satisfaction level of its potential market search for certain goods which a company wants to export. The intelligent search methods for an optimal market recommendations proposed in this paper utilizes a knowledge representation methodology in the concept hierarchy.

The development of distance education put forward higher requirements for WEB courseware, it should update teaching content based on the degree of students. This article proposes a resource sharing model of courseware based on semantic web so as to solve the problem of courseware in the teaching process. It takes Resource sharing based on SCORM courses object model as a framework and use XML to describe each learning content objects which makes teachers organize course content and create courseware more easy as well as makes excellent courseware resources can be shared in greatest degree. System defines two agents, one is the teacher interface agent for receiving input information from teachers and curriculum recommendation agent and another agent is communicate agent. Course recommendation agents mainly complete the selection of appropriate curriculum content recommended according to teachers in advance and adjust the course object order in accordance with the repository attribute settings.

Finding and obtaining information eciently from the Web is one of the important ele- ments in realizing Smart Home environment. Users expect to nd most relevant information within the shortest possible time. In this paper, we investigate the similarity of Web pages within Strongly Connected Components (SCCs). SCCs are overlapping groups of Web pages that may imply a relationship between the Web pages of the same component. Therefore, we seek to trace the similarity of these groups of Web pages using Cosine Similarity. Our experiment performed on Malaysian Web pages indicates that Web pages within same SCC carry a common topic or theme. This nding proves that we may locate Web pages with similar topic using the hyperlinks structure, without performing expensive analysis on the contents of the Web pages.

The development of the Internet brought us into an era of big data information, give people bring convenient while and also make people ragged when choosing the required information and recommendation system arises at the historic moment, and get the wide attention and applications. Therefore, to enhance the traditional method, we propose a novel intelligent recommendation algorithm based on Web data mining technique under the background of the deep neural network. Firstly, we review the state-of-the-art web data mining algorithms and revise the traditional ones with the parallel data mining algorithm on the multiple processors to perform tasks that will enhance the accuracy and efficiency. Then, we analyze basic neural network model through the inner connection and weight transfer. Later, we introduce the deep network structure to enhance the traditional network. Finally, we combine the revised prior theories into the recommending tasks for enhancement. The experimental analysis show that our algorithm accuracy is enhanced by the extent of 56% and overall time is reduced to the 87% of traditional ones which proves the feasibility. Later, more optimization work will be introduced to modify the current methodology.

Main challenges to the current semantic web service technologies are exponential continuous growth in the number of services on the Internet, syntax based discovery, lack of common agreed upon semantic service standards and heterogeneity of ontologies. In this paper, a service discovery approach independent of semantic service description models is proposed to solve the challenges of the current web service discovery. The idea is to combine principles from machine learning, data mining, statistical techniques and measures of semantic relatedness to make the semantic web service discovery process more intelligent, efficient and effective. The proposed approach exploits the use of semantic as well as syntactic information present within the service description profiles. Our approach is unique in terms of its application to any web service description language and the use of Omiotis measure of semantic relatedness for service discovery. The proposed approach has been implemented on OWL-S based service descriptions profiles and is able to find semantic relationship between the services which were otherwise discarded by the OWL-MX matchmaker. Empirical analysis shows that the proposed method out performs the TF - IDF based cosine similarity method, Jaccard method and LSA based approach.

Performance of web query processing becomes slow caused by increasing number of data. In this paper, intelligent algorithm was created to fix this problem. Four main components involved in this intelligent algorithm are assigning initial query, exploit query, assign to any possible query and query matching. Firstly, web user is needed to enter a keyword then this keyword automatically will assign as an initial query. After that, this initial query must be exploited before assigning process to any possible queries. These possible queries will match to existing query form temporary file that contains data schema and map to only select data sources. In this methodology, XML will use for mapping to heterogeneous databases. XML is really effective for mapping to heterogeneous databases. This methodology has been implemented in a prototype and applied to web queries. Another issue in with web query is difficult to search for information that best reflects the user’s need information. According this problem, intelligent algorithm was created and tested by developing simple application based on system architecture. This intelligent algorithm that was good for fixed problems by improving web query processing for heterogeneous databases. The intelligent algorithm was implemented and tested for heterogeneous database environment.

There are numbers of web tools available to spread and explore information on internet. With the increasing use of Blogging sites, people are able to share their opinions, interests, experiences, and their views with others. However, it is not so easy to fetch required information from various Blogs in available time, which normally is very short. From last few years our major concern is on web searching and web mining. In this paper, a searching and curating model is discussed which introduces an approach of fetching the relevant information automatically from various Blog sites and fetches the personalized required information. That exactly is the need of the hour.

A web-based intelligent education system(WEST) was designed and implemented to improve the self-directed study for students by providing them the study according to one's ability. WEST is composed of a tutor agent, an administrator module, a teacher module and a student module. The tutor agent analyzes students' ability and selects questions from a database according to their ability. The administrator module administrates subjects and students. The teacher module makes questions with three levels and administrates questions. The students module gives the questions students and the result of study. WEST was used in a mathematics subject for high-school students. Especially our system was better than the others in study according to students' ability.

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

이 논문에서는 멀티에이전트 기반 지능형 웹기반 이러닝 시스템을 구현하였다. 이 시스템 구현을 위해 사용자들의 취향검사를 수행하였고, 결과 사용자 그룹에 맞는 적절한 이러닝 커뮤니티를 형성하였다. 제안하는 시스템인 IMAS는 신경회로망에 의해 이러닝 커뮤니티를 학습하였고, 새로운 분산기반 멀티에이전트 프레임워크를 이용하여 에이전트를 생성한다.

In this paper, we developed an intelligent web based e-learning system based on multi agents. To do development of the system, we applied an inclination test that is based on the education theory to do grouping the desirable e-learning community. The proposed system, Intelligent Web based e-learning Multi Agent System (IMAS), is used the multi agents paradigm including learning manner by neural network for grouping of e-learning community and a new distributed multi agent framework proposed here.

Both browsing and retrieval with search engines are major operations that establish the interaction between users and the Web. Although both operations are usually combined to locate information from the Web, recent growth of the Web has overtaken the potential of this conventional interaction. This paper proposes the concept of Retrieve, Browse, and Analyze (RBA)-based interactions, as the improvement of the conventional Retrieve and Browse (RB)-based interaction. The prototype interface based on RBA-based interaction is also presented.

This paper proposes a human action recognition scheme for Intelligent Web Browser. Based on the principle that a human action can be defined as a combination of multiple articulation movements, the inference of stochastic grammars is applied to recognize each action. Human actions in 3 dimensional (3D) world coordinate are measured, quantized and made into two sets of 4-chain-code for xy and zy projection planes, consequently they are appropriate for applying the stochastic grammar inference method. We confirm this method by experiments, that various physical actions can be classified correctly against a set of real world 3D temporal data. The result revealed a comparatively successful achievement of $93.8\%$ recognition rate through the experiments of 8 movements of human head and $84.9\%$ recognition rate of 60 movements of human upper body. We expect that this scheme can be used for human-machine interaction commands in a web browser.

During over decades, metadata on spatial data have been developed, and they have been widely applied at the national and international metadata standards such as file structure, format, and data model. However, in the web 2.0 paradigm toward user participation and openness, sources and contents of geospatial products are also diversified, not being limited to well-organized and structured data sets or databases. Especially, software products in both open source software and commercially packaged software are considered into important resources in the geospatial domain. But there are no reports or studies regarding software metadata from the side of software engineering or information technology, till now. The motivation of this study is based on practical needs to build search engine in the intelligent geospatial web. Brief review on current metadata standards is presented, and necessity for software metadata is discussed as well as related works. Basic elements, initially considered, of software metadata are presented. This work is the first attempt for software metadata, although it just covers geospatial software products. Further practical works to meet industrial demands need to actual applications of software metadata.

The existing building control and automation systems which have been applied to buildings are provided by many different vendors and each vendor has its own proprietary protocol. There are many problems in system integration since those building control and automation systems belong to each vendor. Also, the problems of cost and space increase, due to the usage of personal computers as the control servers of DDC(Direct Digital Control) in each facility. Those problems mentioned above can be effectively resolved by using XML Web Services and Embedded Web Server. Embedded Web Server and XML Web Services are technologies emerging out of the IT industries and there are many researches to apply them into many fields. In this paper, Embedded Web Server and XML Web Services are proposed to be used in building control and automation system to resolve problems in building system integration.