|Short Talk Session 6: BIGSEA- A Big Data Analytics Platform for Public Transportation Management|
|Urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications), a platform which combines cloud orchestration, quality of Service, automatic parallelisation, data quality enhancement, entity matching algorithms as well as advanced services for sentiment analysis is presented. As a result, services were used for three applications: 1) a web application integrating multiple services; 2) an android application for bus visualization and prediction and 3) a dashboard focused on applying exploratory data analysis techniques on ticketing data. All developments are available under Open Source licenses (http://github.org/eubr-bigsea, https://hub.docker.com/u/eubrabigsea /).
Nádia P. Kozievitch has joined the Department of Informatics of UTFPR in 2012, where now she is an Associate Research Professor. She holds a Ph.D. in Computer Science from the State University of Campinas (Unicamp-2011). Since 2006 she has been actively involved in database topics, concerning integration, and optimisation (in academia and industry). Her areas of research include Databases, Digital Libraries, Information Retrieval and Geographical Information Systems. Since 2014 she has participated in the research group ‘‘Infrastructure for Sustainable Cities", applying database techniques to explore the open data available in the city of Curitiba (Paraná, Brazil).
|Short Talk Session 6: Managing heterogeneous geospatial observation and modeling data in the TRAFAIR project|
|The main objective of the TRAFAIR project is the development of a service for the monitoring, prediction and publication of air quality at urban scale in smart cities. Regarding air quality observation, low cost sensors are combined with intelligent calibration methods to estimate the real time evolution of pollutants at specific locations. For air quality forecasting, a Lagrangian particle dispersion model is used to generate a urban scale prediction from pollutant emissions and meteorological conditions. Special attention is given to the NOx emissions generated by traffic. The modeling, storage, management and open publication of the involved data is based on the use of well-known international standards from OGC, ISO and W3C. The main data management challenges are related to the heterogeneity and the complex spatio-temporal nature of the data.
José R.R. Viqueira is Associate Professor at the Department of Electronics and Computer Science and member of the scientific staff of CITIUS (Research center on intelligent technologies) of the Universidade de Santiago de Compostela (Spain). He obtained a master (1998) and a Ph.D. (2003) in Computer Science at the University of A Coruña. During his career, he has been involved in many research and development projects, where he led tasks related to complex data management, most frequently with spatial and spatio-temporal dimensions. He is author of numerous publications in these topics and also founding partner of Enxenio S.L., a spin-off company of the University of A Coruña. His current research interests are related to the management of very large scientific datasets, with special emphasis on spatio-temporal and sensor data.
|Short Talk Session 2: Lineage-Preserving Anonymization of the Provenance of Collection-Based Workflows|
|We examine in this talk the problem of anonymizing the provenance of collection-oriented workflows, in which the constituent modules use and generate sets of data records. Despite their popularity, this kind of workflows has been overlooked in the literature w.r.t privacy. We, therefore, set out in this paper to examine the following questions: How the provenance of a collection-based module can be anonymized? Can lineage information be preserved? Beyond a single module, how can the provenance of a whole workflow be anonymized? As well as addressing the above questions, we report on evaluation exercises that assess the effectiveness and efficiency of our solution. In particular, we tease apart the parameters that impact the quality of the obtained anonymized provenance information.
Khalid Belhajjame is an associate professor at the University Paris-Dauphine, where he is member of the Data Science Team in the LAMSADE research lab. Before moving to Paris, he has been a researcher for several years at the University of Manchester, and prior to that a PhD student at the University of Grenoble. His research interests lie in the areas of information and knowledge management. In particular, he has made key contributions to the areas of pay-as-you-go data integration, e-Science, scientific workflow management, provenance tracking and exploitation, and knowledge graphs.
|Short Talk Session 5: A Commitment to Services|
|A brief overview of the IBM evolution to a cognitive enterprise will be shown. From a company based on hardware and its management, to Services, to Cloud, to AI, to Blockchain and so on. Both the SSME concept and how IBM goes over the IT Services professional will be introduced. Finally, the lecturer will talk about the implementation of the Service Science degree at Universidad Rey Juan Carlos (Madrid).
Luis Martínez, ex-Executive IT Architect at IBM, has worked for the company for 34 years, and where he has held different responsibilities in the areas of Technology, Consulting and Architecture, having worked with more than 150 customers. He was member of the IBM Technical Expert Council for 17 years and its chairman from 2007 to 2010. Luis has been teaching various subjects at different universities and business schools for 24 years. He is currently Visiting Professor at Universidad Rey Juan Carlos in Madrid. Luis is author of several books in the areas of large systems and databases.