Narasimha Rao Vajjhala

Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

This study addressed the literature gap, identified by other researchers, that there are too few examples of applied empirical open big data analytics. Using correspondence analysis as a big data analytical technique, this study demonstrates how qualitative big data type could be analyzed to identify hidden factor relationships that may assist strategic decision making. We use a 64MB open meta big dataset developed by summarizing terrorist activity as keyword frequencies collected from trillions of public news articles published during a 43 year period from 1970-2013 and readily available statistical software, SPSS, to visually summarize the findings on a global terrorism big dataset. The approach in this paper might facilitate the research and development of open big data, big data analytics against global terrorism.

Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies

Knowledge sharing is subject to organisational and national cultural influences, but medium-sized firms often implement general knowledge-sharing models without considering cultural factors. The purpose of this study was to investigate the influence of cultural factors on knowledge-sharing activities in medium-sized enterprises in transition economies. The study was driven by the central research question: How do cultural factors influence employees’ perceptions of knowledge-sharing initiatives in medium-sized enterprises in transition economies? Qualitative in-depth interviews were conducted with 20 managers working in ten medium-sized enterprises in Albania, which is a transition economy. Six themes revealed the influence of cultural factors on employees’ perceptions of knowledge-sharing initiatives in medium-sized enterprises in transition economies. Specifically, national culture and organisational culture influence employee behaviour and participation in knowledge-sharing activities. Thus, it is crit...

Measuring Organizational-Fit Through Socio-Cultural Big Data

We propose that businesses, government, and not-for-profit entities could benefit from a better understanding of organizational behavior through the lens of a contemporary global culture model. Human resourcing and partnering decisions could be improved by using global culture to ensure a better organizational-fit as well as to reduce the risk of destructive relationship dependencies. For an extreme-limits example, a company could inadvertently hire a terrorist or a social loafer seeking to steal competitive intelligence. A big data approach supported by a socio-cultural framework could help in hypothesis testing which is essential for advancing the body of knowledge in organizational behavior. This paper will make a scholarly contribution by identifying literature relevant to collecting and analyzing organizational big data that could explain beneficial socio-cultural behavior. This paper will explore how sources of qualitative big data could be collected and then analyzed to measure organizational-fit factors relevant for decision-making.

Gaps to Address in Future Research Design Practices

In keeping with the unique visual exciting style of the handbook, we wanted to finish with a thinking-outside-the-box implication for future research design practices to question the status quo rather than summarize what is already articulated in the preface and introductory chapters. Four contributing authors volunteered to collaborate on this final concluding chapter. Each author brings a distinct sociocultural and ideological perspective to the table based on his or her contribution being in different sections of this book and his or her research experience being grounded in diverse epistemological disciplinary roots. In other words, each of us works in a different discipline, and we have different dominant research ideologies and ontological approaches to research.

A Novel Structure Refining Algorithm for Statistical-Logical Models

Statistical Relational Learning (SRL) is a growing field in Machine Learning that aims at the integration of logic-based learning approaches with probabilistic graphical models. Markov Logic Networks (MLNs) are one of the state-of-the-art SRL models that combine first-order logic and Markov networks (MNs) by attaching weights to first-order formulas and viewing these as templates for features of MNs. Learning models in SRL consists in learning the structure (logical clauses in MLNs) and the parameters (weights for each clause in MLNs). Structure learning of MLNs is performed by maximizing a likelihood function over relational databases and MLNs have been successfully applied to problems in relational and uncertain domains. Theory revision is the process of refining an existing theory by generalizing or specializing it depending on the nature of the new evidence. If the positive evidence is not explained then the theory must be generalized, whereas if the negative evidence is explained the theory must be specialized in order to exclude the negative example. Current SRL systems do not revise an existing model but learn structure and parameters from scratch. In this paper we propose a novel refining algorithm for theory revision under the statistical logical framework of MLNs. The novelty of the proposed approach consists in a tight integration of structure and parameter learning of an SRL model in a single step inside which a specialization or generalization step is performed for theory refinement.