DC3K 2016 Abstracts

Full Papers
Full Papers
Paper Nr: 1
Title:

Tacit Knowledge Management in Public Sector - A New Perspective for Organizational Knowledge Management
Authors:

Mauro Araújo Câmara
Abstract: The Knowledge Management has been used as a tool to organize the knowledge produced by social actors in order to create the organizational knowledge itself. A wide variety of practices has helped to organize and formalize the knowledge that flows within organizations. Usually, it is made with the support of technologies through databases, repositories, intranet, SharePoint, and others. These tools have been implemented and described in different knowledge management models. These mechanisms have proven effective to deal with explicit knowledge allowing their setup and dissemination. However, the gap that is identified is precisely the superficiality when it comes to tacit knowledge. The main point of this research is to explore the importance of this kind of knowledge, i.e., the individual experience in the tasks that can contribute to positive outcomes, and needs to be preserved. The problem that concerns this research and which it intends to investigate is how to identify and transfer of tacit knowledge of experienced employees in order to protect the organizational knowledge. Organizing and disseminating information by simply using technology support does not guarantee that the knowledge developed over the years will remain in the organization. It is necessary to do more than that.
Paper Nr: 2
Title:

Barriers and Perceived Benefits to IT Service Management Adoption in SMEs – A Literature Review
Authors:

David Molamphy
Abstract: The continuous development of technology and its increasing criticality in organisations across all sectors of the economy has led to the creation of models and frameworks for ensuring delivery of high quality IT services. Models such as the Information Technology Infrastructure Library (ITIL), Microsoft Operating Framework (MOF) and HP ITSM amongst others have been adopted by many firms to achieve this goal - ultimately to reduce costs and increase organisational productivity. Such models and frameworks however require substantial commitment, investment and human capital to both implement and operate. Whilst many large firms have the resources available to implement existing popular IT Service Management (ITSM) frameworks, SMEs most often do not, nor do they necessarily stand to benefit from these existing frameworks. As a result, there is a general consensus that all major ITSM frameworks currently published are designed to cater for the needs of the large firm. The aim of this doctoral consortium contribution is to present the current state of the art relating to the barriers and perceived benefits to ITSM adoption in smaller organisations. The paper follows a systematic review process, and concludes by presenting the expected outcome of the research project of the author.
Paper Nr: 4
Title:

Semantic Web Technologies to Enhance the Knowledge Discovery Process in Predictive Analytics
Authors:

Iker Esnaola-Gonzalez
Abstract: Knowledge Discovery in Databases (KDD) is the process which leads from raw data to actionable knowledge. The knowledge to discover can be contained in the primary data itself, from where it is discovered using appropriate algorithms and tools. However, this is not the only knowledge source: it can be contained in external data sources, or even only in the data analyst’s mind. It has been pointed out that Semantic Web Technologies (SWT) can be used to improve each step of the KDD process. The approached research problem is how the SWT can be used to improve a Predictive Analytics process in the domain of the Energy Efficiency in Buildings, and our approach is expected accomplish a high level of abstraction of the problem to solve, which in turn should allow to replicate the process in similar use cases of the same domain with very little effort. Besides, the obtained predictions are also expected to improve.
Paper Nr: 5
Title:

Semantic Coverage Measures: Analytic Operators for Ontologists
Authors:

Pallavi Karanth and Kavi Mahesh
Abstract: The field of Analytics has grown over the past decade with the promise of delivering insights from data in all its forms ranging from well structured to highly unstructured forms such as documents and web pages. With open data and open science initiatives and the development of Semantic Web, semantic data which includes ontological concepts and relations is becoming available in increasing amounts from diverse applications. Such semantic data is made up of knowledge structures typically in the form of graphs with well defined hierarchies of concepts and relations specified in an ontology of the domain. Analytics is still in its nascent stage in taking advantage of such semantically rich ontological data to deliver better insights. Ability to analyze semantically rich data enriched by various semantic constraints and knowledge structures can generate insights which can take the field of Analytics to a new level. Ontological instances can be analyzed for coverage which leads to new analytic metrics which we call Semantic Coverage measures. In this paper, we present different types of Semantic Coverage measures to analyze the A-Box against the T-Box. Such analytic metrics help ontologists fine tune ontologies and improve data collection and sampling techniques for better semantic coverage.