Topic-Based Coordination for Visual Analysis of Evolving Document Collections.
ABSTRACT Document interpretation is a crucial task in many visual analytics applications, made harder by the widespread availability of freely available textual files. In this paper we propose an approach based on topic detection coupled with multiple coordinated views to assist analysis of time varying document collections. Given multiple document maps built from a set of text files, we define a strategy to support users locating the evolution of topics addressed by the documents, along various time steps. The approach is supported by a new algorithm for topic extraction from texts, also introduced. Finally, we show several examples illustrating how the proposed strategy may be applied in the analysis of document collections.
Conference Paper: Coordinating Multiple Views Using an Ontology-Based Semantic Mapping[Show abstract] [Hide abstract]
ABSTRACT: Multiple views of data sets from the same domain can support to discover unforeseen associations among data elements, but requires users to interact with them. The coordination mechanism must relate elements across multiple views. The mapping among data elements are constrained by using data attributes, and such mapping influences on how multiple views are coordinated. We propose the application of ontology to link data elements based on semantic for specific context. Representing the underlying data into ontology, semantic representation to create the mappings can benefit exploratory visualization. In this paper we show how to use ontology on coordinating multiple views, the initial results using document collections are presented and discussed, in comparison with traditional techniques.Information Visualisation (IV), 2013 17th International Conference; 01/2013
- [Show abstract] [Hide abstract]
ABSTRACT: Context: Systematic mapping provides an overview of a research area to assess the quantity of evidence existing on a topic of interest. In spite of its relevance, the establishment of consistent categories and classification of primary studies in these categories are manually conducted. Objective: We propose an approach, named SM-VTM (Systematic Mapping based on Visual Text Mining), to support categorization and classification stages in the systematic mapping using Visual Text Mining (VTM), aiming at reducing time and effort required in this process. Method: We established SM-VTM, selected a VTM tool and conducted a case study comparing results of two systematic mappings: one performed manually and another using our approach. Results: The results of both systematic mappings were very similar, showing the viability of SM-VTM. Furthermore, since our approach was applied using a tool, reduction of time and effort can be achieved. Conclusions: The application of VTM seems to be very relevant in the context of systematic mapping.Proceedings of the 14th international conference on Evaluation and Assessment in Software Engineering; 04/2010
- [Show abstract] [Hide abstract]
ABSTRACT: We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input material—either single texts or collections of texts—and their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11/2012; 2(6):476-492. DOI:10.1002/widm.1071 · 1.42 Impact Factor