Abstract While modern online social networks offer unprecedented amount of data and opportunities for mining, we acknowledge that other forms of social networks have been around throughout human history. In particular, we recognize the classic books as valuable sources for the analysis of interpersonal interactions as they have had positive influence on human mind and continue to enlighten the human race. In this paper, we propose a formal framework... While modern online social networks offer unprecedented amount of data and opportunities for mining, we acknowledge that other forms of social networks have been around throughout human history. In particular, we recognize the classic books as valuable sources for the analysis of interpersonal interactions as they have had positive influence on human mind and continue to enlighten the human race. In this paper, we propose a formal framework for the mining of interpersonal interactions from classic books. Our approach employs a heuristic over a traditional named-entity recognition for extracting people names. We demonstrate the effectiveness of our approach by presenting the encouraging result from a network analysis conducted over the persons appearing in the book of Genesis. The result aids the understanding of the persons featured in the book and their interactions. We also present the merits and challenges of visualization techniques pertaining to our work. Read More Read Less

Abstract The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions.... The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information. Read More Read Less

Abstract In this research paper, we had observed and proposed the characteristic of Big Data Analytics (BDA) and Business Intelligence (BI) in observing the voluminous picture of organizational strategic performance management diagnostics framework. In our study, we are interested in handling BDA and BI as a dynamic research that had enabled organizations to invade and generate greater knowledge formation and decision-making. Our research goal is to advance a real... In this research paper, we had observed and proposed the characteristic of Big Data Analytics (BDA) and Business Intelligence (BI) in observing the voluminous picture of organizational strategic performance management diagnostics framework. In our study, we are interested in handling BDA and BI as a dynamic research that had enabled organizations to invade and generate greater knowledge formation and decision-making. Our research goal is to advance a real world understanding of emerging knowledge derived from organizing big data scenarios and BI framework development. In order, to overcome the ambiguity scenarios of BDA, we propose a framework that involve the present BDA and BI stages with their analytic features in designing the organizational strategic performance management framework. The outcome will be a design of a typical strategic performance management application - the organizational strategic diagnostic dashboard. Read More Read Less

Abstract Ontology matching is one of the most important works to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes multiple measures to compute the similarities among elements, and then combine these similarities using a weighted sum formula to determine later the semantic correspondences. The proper selection and... Ontology matching is one of the most important works to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes multiple measures to compute the similarities among elements, and then combine these similarities using a weighted sum formula to determine later the semantic correspondences. The proper selection and combination of these similarities strongly influence the final quality of the matching system. In this paper, we introduce element-level ontology matcher as machine learning system which utilizes regression algorithms to automatically find many possible combination relations of similarity measures. We adopt different similarity measures to extract learning features, and train the system on sample data from Benchmark and Conference tracks OAEI 2015. To match two ontologies, after training, we measure different similarities for each entity pair, and predict the overall similarity of each pair using the learned regression model to get similarity matrix. After that we extract correspondences by applying naive descending algorithm on the similarity matrix. These correspondences are filtered by previously known semantic techniques to enhance matching precision while preserving matching recall. Experimental results, using dataset in conference track from OAEI 2015, show that our extracted similarity features are efficient in terms of f-measure evaluation criteria, and outperform the widely used measures in ontology matching systems. Moreover, our method for similarity combination which depends on regression model outperforms the present combination methods. Besides, in comparison to the matching systems participated in OAEI 2015 campaign, our matching system showed to be highly competitive and had a high ranking position. Read More Read Less