2,391 research outputs found

    Knowledge discOvery And daTa minINg inteGrated (KOATING) Moderators for collaborative projects

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    A major issue in any multidiscipline collaborative project is how to best share and simultaneously exploit different types of expertise, without duplicating efforts or inadvertently causing conflicts or loss of efficiency through misunderstanding of individual or shared goals. Moderators are knowledge based systems designed to support collaborative teams by raising awareness of potential problems or conflicts. However, the functioning of a Moderator is limited by the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update their knowledge about team members. This enables the reuse of discovered knowledge from operational databases within collaborative projects. The integration of knowledge discovery in database (KDD) techniques into the existing Knowledge Acquisition Module of a moderator enables hidden data dependencies and relationships to be utilised to facilitate the moderation process. The architecture for the Universal Knowledge Moderator (UKM) shows how Moderators can be extended to incorporate a learning element which enables them to provide better support for virtual enterprises. Unified Modelling Language diagrams were used to specify the ways to design and develop the proposed system. The functioning of a UKM is presented using an illustrative example

    Knowledge discovery in database: A knowledge management strategic approach

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    Knowledge management had been a critical focus and interest in Information Technology, especially as it affects business organizations through the implementation of business intelligence and expertise.Knowledge discovery and knowledge conversion (tacit/implicit to explicit knowledge) play important roles in these aspects; through the application of technologies in the SECI model to aid knowledge management, and identifying the sources of the expertise whether in humans or physical databases serve as the basis for expertise’s knowledge management.This paper presents in detail the significances of knowledge discovery in databases (KDD) in achieving an all encompassing knowledge management strategy.This strategy must comprise of transparent and multiple interrelationships of organizational agents through shared mental maps, collaborative and distributed technologies, and solves all problem in other ways with a special focus on data mining which is also found in the KDD process. Extensive literatures were reviewed to operationalize Knowledge discovery in human and in data ware houses as its affect knowledge management, and bring to the fore the processes involved in KDD process, its applications, understanding using SECI model, possible challenges, and suggest the future research areas to solve the observed challenges

    The Method of Constructing Recommendations Online on the Temporal Dynamics of User Interests Using Multilayer Graph

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    The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time

    CK-RAID: Collaborative Knowledge Repository for Intrusion Detection System

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    Intrusion Detection Systems (IDSs) are an integral part of an organization\u27s infrastructure. Without an IDS facility in place to monitor network and host activities, attempted and successful intrusion attempts may go unnoticed. This study proposed a Collaborative Knowledge Repository Architecture for Intrusion Detection (CK-RAID). It is based on a distributed network of computer nodes, each with their individual IDS with a centralized knowledge repository system, and firewall acting as a defence. When an unfamiliar attack hits any node, the first step the intrusion monitor takes is to request from Knowledge Repository Server the most effective intrusion response. To improve performance, Intrusion Update module collaborates with IDSs sensor and log by updating their expert rule and intrusion information respectively and removing the old intrusion signature from the knowledge base with the aid of Intrusion Detector Pruning. To ensure security of information exchange, RSA encryption and Digital Signature were used to encode information during transit. The result showed that CK-RAID had a detection rate of 97.2%, compared with Medoid Clustering, Y-means, FCM and K-means that have an accuracy of 96.38%, 87.15%, 82.13% and 77.25% respectively. Therefore, CK-RAID can be deployed for efficient detection of all categories of intrusion detection and response
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