Abstract Today dashboards are a clear factor of differentiation for decision-makers, providing critical business indicators in useful time. They are very important data visualization mechanisms, simple to interpret, easy to deal, and fast on showing pertinent data. Dashboards provide means for reducing time of information analysis, disposing business indicators in quite understandable and attractive interface platforms – not the ability to... Today dashboards are a clear factor of differentiation for decision-makers, providing critical business indicators in useful time. They are very important data visualization mechanisms, simple to interpret, easy to deal, and fast on showing pertinent data. Dashboards provide means for reducing time of information analysis, disposing business indicators in quite understandable and attractive interface platforms – not the ability to evolve, to adapt by themselves to new user needs, tendencies or preferences. In order to provide such abilities, we designed and implemented a personalization system for data visualization platforms. All system’s services, ranging from data gathering to data profiling, were developed using a community of autonomous agents with the ability to act together using a private data cloud as a cooperative environment. In this paper we present how the system was conceived, its functional architecture and services, and how agents were implemented, especially the ones related to the personalization of business dashboards. Read More Read Less

Abstract Different author productivity indexing methods have been proposed in order to rank scientists on the basis of their research work. The author productivity indexing methods present in literature do not consider the topic based contribution of authors for assigning them the weighted citations in a multi-authored paper. This study proposed TSWC-index which assigns Topic Sensitive Weighted Citations to authors of... Different author productivity indexing methods have been proposed in order to rank scientists on the basis of their research work. The author productivity indexing methods present in literature do not consider the topic based contribution of authors for assigning them the weighted citations in a multi-authored paper. This study proposed TSWC-index which assigns Topic Sensitive Weighted Citations to authors of a paper according to their topic relatedness. Topic of co-authors in each paper against its first author has been checked and more weight is assigned to the co-authors if their topic is same as first author. The results are compared with h-index and kth rank index. Proposed method clearly shows significant difference among author’s full citations score, weighted citations score and topic sensitive weighted citations score. Read More Read Less

Abstract Morphological analyzer is a tool which extracts morphological features and grammatical categories associated with the given inflectional word. Kannada is an inflectional and morphologically rich language. This paper presents the design of morphological analyzer for Kannada inflectional words using affix-stripping, rule-based, paradigm-based and questionnaire-based approaches. The proposed model comprises of text normalizer, morphological stemmer, morphological analyzer and a module for... Morphological analyzer is a tool which extracts morphological features and grammatical categories associated with the given inflectional word. Kannada is an inflectional and morphologically rich language. This paper presents the design of morphological analyzer for Kannada inflectional words using affix-stripping, rule-based, paradigm-based and questionnaire-based approaches. The proposed model comprises of text normalizer, morphological stemmer, morphological analyzer and a module for semi-automatic insertion of lexical details of unanalyzed words into Kannada monolingual lexicon. The performance of proposed model is tested on 7 different data sets that are manually created by collecting Kannada text from government official circular documents Kannada newspapers, legal documents and news from All India Radio. The experimental result shows that the accuracy of proposed model on these 7 different data sets is around 90%. Read More Read Less