Visualization, Exploration and Data Analysis of Complex Astrophysical Data

In this paper we show how advanced visualization tools can help the researcher in investigating and extracting information from data. The focus is on VisIVO, a novel open source graphics application, which blends high performance multidimensional visualization techniques and up-to-date technologies to cooperate with other applications and to access remote, distributed data archives. VisIVO supports the standards defined by the International Virtual Observatory Alliance in order to make it interoperable with VO data repositories. The paper describes the basic technical details and features of the software and it dedicates a large section to show how VisIVO can be used in several scientific cases.Comment: 32 pages, 15 figures, accepted by PAS

Identifying Place Histories from Activity Traces with an Eye to Parameter Impact.

Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Important and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile phone operators or photos on photo sharing web sites. We developed a suite of visual analytics methods for reconstructing past events from these activity traces. Our tools combine geocomputations, interactive geovisualizations, and statistical methods to enable integrated analysis of the spatial, temporal, and thematic components of the data, including numeric attributes and texts.We also support interactive investigation of the sensitivity of the analysis results to the parameters used in the computations. For this purpose, statistical summaries of computation results obtained with different combinations of parameter values are visualized in a way facilitating comparisons. We demonstrate the utility of our approach on two large real data sets, mobile phone calls in Milano during 9 days and flickr photos made on British Isles during 5 years

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.This paper has been partially supported by the MESOLAP (TIN2010-14860) and GEODAS-BI (TIN2012-37493-C03-03) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

abstract: Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR being generated. A case control study has been performed on a data set of twitter timelines of women that announced their pregnancy, the goals of the study is to compare the ADR risk of medication usage from each medication category during the pregnancy. In addition, to evaluate the prediction power of this approach, another important aspect of personalized medicine was addressed: the prediction of medication usage through the identification of risk groups. During the prediction process, the health information from Twitter timeline, such as diseases, symptoms, treatments, effects, and etc., is summarized by the topic modelling processes and the summarization results is used for prediction. Dimension reduction and topic similarity measurement are integrated into this framework for timeline classification and prediction. This work could be applied to provide guidelines for FDA drug risk categories. Currently, this process is done based on laboratory results and reported cases. Finally, a multi-dimensional text data warehouse (MTD) to manage the output from the topic modelling is proposed. Some attempts have been also made to incorporate topic structure (ontology) and the MTD hierarchy. Results demonstrate that proposed methods show promise and this system represents a low-cost approach for drug safety early warning.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Interactive Multidimensional Modeling of Linked Data for Exploratory OLAP

Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll- up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users