• Journal of Arbitration Studies
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• v.20 no.2
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• pp.67-90
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• 2010

Electronic discovery refers to the discovery of electronically stored information. The differences between producing paper documents and electronic information can be categorized into seven groups: massive volume, persistence, dynamic and changeable contents, metadata, environment-dependence, dispersion and searchability. Since these differences make the discovery more expensive and less expeditious, it is necessary to limit the scope of discovery. Accordingly, a number of arbitration institutions have already introduced rules, guidelines or protocols on electronic discovery. ICDR guidelines take a minimal approach and address only the proper form of electronic document. CIArb Protocol is intended to act as a checklist for discovery of electronic data. CPR Protocol offers four modes of discovery of electronic documents ranging from minimal to extensive among which the parties may choose the way of electronic discovery. IBA Rules on Evidence and ICC Rules are silent on the issue of electronic discovery, however, working parties of the ICC are considering updates to the rules to deal with electronic discovery. It is disputed whether rules, guidelines or protocols on electronic discovery is necessary or appropriate. Although some have suggested that existing rules can make adequate provision for electronic discovery, it is more desirable to prepare new rules, guidelines or protocols to make arbitrators and counsels be familiar with electronic discovery process, to provide an adequate standard for electronic discovery and to limit the time and cost of electronic discovery. Such rules on electronic discovery should include provisions regarding the form of electronic document production, conference between parties regarding electronic discovery, keyword search, bearing the expenses to reduce disputes over electronic discovery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2316-2332
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• 2024

In this paper, we introduce a web-based system that leverages the capabilities of the ρ(rho)-algorithm, which is a Structure Information Control Net (SICN)-oriented process mining algorithm, with open-source platforms, including Django, Graphviz, and Cytoscape, to facilitate the rediscovery and visualization of business process models. Our approach involves discovering SICN-oriented process models from process instances from the IEEE XESformatted process enactment event logs dataset. This discovering process is facilitated by the ρ-algorithm, and visualization output is transformed into either a JSON or DOT formatted file, catering to the compatibility requirements of Cytoscape or Graphviz, respectively. The proposed system utilizes the robust Django platform, which enables the creation of a userfriendly web interface. This interface offers a clear, concise, modern, and interactive visualization of the rediscovered business processes, fostering an intuitive exploration experience. The experiment conducted on our proposed web-based process discovery system demonstrates its ability and efficiency showing that the system is a valuable tool for discovering business process models from process event logs. Its development not only contributes to the advancement of process mining but also serves as an educational resource. Readers, students, and practitioners interested in process mining can leverage this system as a completely free process miner to gain hands-on experience in rediscovering and visualizing process models from event logs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1134-1149
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• 2013

Pub/sub system enables data users to access any necessary data without knowledge of the data producer and synchronization with the data producer. It is widely used as the middleware technology for the data-centric services. DDS (Data Distribution Service) is a standard middleware supported by the OMG (Object Management Group), one of global standardization organizations. It is considered quite useful as a standard middleware for US military services. However, it is well-known that it takes considerably long time in searching the Participants and Endpoints in the system, especially when the system is booting up. In this paper, we propose a discovery scheme to reduce the latency when the participants and Endpoints are densely distributed in a small area. We propose to modify the standard DDS discovery process in three folds. First, we integrate the Endpoint discovery process with the Participant discovery process. Second, we reduce the number of connections per participant during the discovery process by adopting the concept of successors in Distributed Hashing scheme. Third, instead of UDP, the participants are connected through TCP to exploit the reliable delivery feature of TCP. We evaluated the performance of our scheme by comparing with the standard DDS discovery process. The evaluation results show that our scheme achieves quite lower discovery latency in case that the Participants and the Endpoints are densely distributed in a local network.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.85-102
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• 2004

Currently, developed technologies for semantic web services discovery are based on ontologies. These ontologies are DAML-S(DARPA Agent Markup Language for Services) and Process Handbook Project of MIT. These technologies have some problems for intelligent web services discovery. So, in this paper we analyzed those ontologies and proposed TM-S, Topic Maps for Services. TM-S is the presentation model for semantic web services. And TM-S includes benefits and complements weaknesses of those ontologies. And we proposed TMS-QL, TM-S Query Language. TMS-QL is query language for intelligent web services discovery. At last, we designed and implemented intelligent web service discovery system that deals TM-S ontology and TMS-QL

• KNOM Review
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• v.24 no.2
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• pp.48-55
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• 2021

Ethereum is an open software platform based on blockchain technology that enables the construction and distribution of distributed applications. Ethereum uses a fully distributed connection method in which all participating nodes participate in the network with equal authority and rights. Ethereum networks use Kademlia-based node discovery protocols to retrieve and store node information. Ethereum is striving to stabilize the entire network topology by implementing node discovery protocols, but systems for monitoring are insufficient. This paper develops a WireShark dissector that can receive packet information in the Ethereum node discovery process and provides network packet measurement results. It can be used as basic data for the research on network performance improvement and vulnerability by analyzing the Ethereum node discovery process.

• Knowledge Management Research
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• v.2 no.1
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• pp.109-132
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• 2001

The purpose of this article is to develop a dynamic model of organizational capabilities and knowledge creation, and at the same time identify the organizational capability factors for effective knowledge creation, by empirically analyzing the history of new Quinolone antibacterial drug compound (LB20304a) discovery process at LG, as a case in point. Major findings of this study are as follows. First, in a science-based area such as drug development, the core of successful knowledge creation lies in creative combination of different bodies of scientific explicit knowledge. Second, the greater the difficulty of learning external knowledge, the more tacit knowledge is needed for the recipient firm to effectively exploit that knowledge. Third, in science-based sector such as pharmaceutical industry, the key for successful knowledge creation lies in the capability of recruiting and retaining star scientists. Finally, for effective knowledge creation, a firm must keep its balance among three dimensions of organizational capabilities: local, process, architectural capabilities.

• Journal of Korean Elementary Science Education
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• v.23 no.1
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• pp.61-73
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• 2004

The purpose of this study was to suggest a grounded theory on the process of undergraduate students' generating pattern-knowledge about scientific episodes. The pattern-discovery tasks were administered to seven college students majoring in elementary education. The present study found that college students show five types of procedural knowledge represented in the process of pattern-discovery, such as element, elementary variation, relative prior knowledge, predictive-pattern, and final pattern-knowledge. Furthermore, subjects used seven types of thinking ways, such as recognizing objects, recalling knowledges, searching elementary variation, predictive-pattern discovery, confirming a predictive-pattern, combining patterns, and selecting a pattern. In addition, pattern-discovering process involves a systemic process of element, elementary variation, relative prior knowledge, generating and confirming predictive-pattern, and selecting final pattern-knowledge. The processes were shown the abductive and deductive reasoning as well as inductive reasoning. This study also discussed the implications of these findings for teaching and evaluating in science education.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.146-146
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• 1996

Although knowledge discovery is increasingly important in databases, the discovered knowledge sets may not be effectively used for application domains. It is partly because knowledge discovery does not take user's interests into account, and too many knowledge sets are discovered to handle efficiently. We believe that user's interests are conveyed by a query and if a nested query is concerned it may include a user's thought process. This paper describes a novel concept for discovering knowledge sets based on query processing. Knowledge discovery process is performed by: extracting features from databases, spanning features to generate range features, and constituting a knowledge set. The contributions of this paper include the following: (1) not only simple queries but also nested queries are considered to discover knowledge sets regarding user's interests and user's thought process, (2) not only positive examples (answer to a query) but also negative examples are considered to discover knowledge sets regarding database abstraction and database exceptions, and (3) finally, the discovered knowledge sets are quantified.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.385-389
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• 2012

With the development of computer storage and the rapidly growing ability to process large amounts of data, the multivariate control charts have received an increasing attention. The existing univariate and multivariate control charts are a single hypothesis testing approach to process mean or variance by using a single statistic plot. This paper proposes a multiple hypothesis approach to developing a new multivariate control scheme. Plotted Hotelling's $T^2$ statistics are used for computing the corresponding p-values and the procedure for controlling the false discovery rate in multiple hypothesis testing is applied to the proposed control scheme. Some numerical simulations were carried out to compare the performance of the proposed control scheme with the ordinary multivariate Shewhart chart in terms of the average run length. The results show that the proposed control scheme outperforms the existing multivariate Shewhart chart for all mean shifts.