Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Knowledge assets (KAs) are the main focus for knowledge management as these are considered most valuable in this knowledge-driven economy. However, the tasks of understanding, valuation, and management of knowledge assets have been daunting in contrast to their physical counterparts. There have been no generally agreed frameworks for carrying out the above tasks. Organizations strive to minimize the cost of creating and managing knowledge assets and to maximize value addition from knowledge assets. Also, they provide knowledge services to its internal and external customers using knowledge assets and processes with the help of knowledge workers. Hence, efficient management of knowledge assets is often the central theme in most organizational knowledge management (KM) programs. In this paper, we discuss knowledge asset management from multiple perspectives. We propose a set of generic steps involved in knowledge asset management (KAM) and also propose an architectural design for managing knowledge assets in organizational settings.

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Analytics

School of Computer Science and Engineering

VIT Unitversity

System and Architecture Advances in Intelligent Systems and Computing

An Architectural Design for Knowledge Asset Management System

Electronics

School of Electronics Engineering

Chennai Campus

2016 International Conference on Sustainable Energy Engineering and Application (ICSEEA)

Preface

From a strategic point of view, the most valuable assets in the present era are knowledge assets. Effective management of knowledge assets is essential to use existing knowledge bases as well as to create new knowledge bases by knowledge workers. Therefore, knowledge assets and knowledge workers are crucial aspects of any Knowledge Management (KM) Framework. Most of the researchers have viewed knowledge management either from technical or management perspectives. From architectural perspectives either a centralized or a peer-to-peer approach has been mentioned in the literature. We present a holistic view of knowledge management considering its multiple dimensions and stakeholders to achieve specific organizational knowledge vision and goals. In this paper, we propose a five-layer knowledge management framework comprising of knowledge vision, knowledge processes and services, knowledge networks, knowledge asset management, and knowledge assets. Also, we describe an&nbsp;architecture for interaction among knowledge workers.

Proceedings of the International Congress on Information and Communication Technology Advances in Intelligent Systems and Computing

A Five-Layer Framework for Organizational Knowledge Management

Cloud computing represents a paradigm shift - a transition from computing-as-a-product to computing-as-aservice. It can be applied to a range of areas, including ecommerce, health, education, communities, etc which are emerging as the important sectors in today's market. Day-byday more knowledge is added to the internet and is shared amongst the users in addition to the official use over the cloud. As a result the energy consumption by the networks is increasing and it needs to be managed. This usage can be brought into account for measuring and hence conserving the energy. The consumption is all together considered for the processing, storage and transport of the knowledge granules over the cloud. Since the data accessed in the cloud is “ondemand”, the prediction techniques like those using rough sets can be used to minimize the transfer of data over the cloud networks. The data over the cloud can be procured with the help of rough set based methods efficiently which can help in conserving the energy. Recently, the basic rough set theory has been extended to the notion of neighbourhood based rough sets where information systems with heterogeneous features are prevalent. In this paper, we propose a neighbourhood based rough set approach for knowledge acquisition using MapReduce from Big Data.

2014 Conference on IT in Business, Industry and Government (CSIBIG)

Neighbourhood systems based knowledge acquisition using MapReduce from Big Data over cloud computing

Department of Electrical Engineering

School of Electrical Engineering

Proceedings of the 2014 Conference on IT in Business, Industry and Government: An International Conference by CSI on Big Data, CSIBIG 2014

Message

World University root node

Proceedings of International Conference on Software Engineering: Software Quality: The Road Ahead, CONSEG 2011