Workshop Overview

Big Data becomes a huge opportunity for computer science research but it also revolutionizes many fields, including business, social science, medicine, public administration and so on. For example particular recent trend for researchers from social sciences is to understand the potential of big data in complementing traditional research methods and their value in making decisions. In this case, big data requires a revisit of data management and data analysis techniques in fundamental ways at all stages from data acquisition and storage to data transformation and interpretation. In particular, the task of collecting and analyzing data — which is at the heart of the big data analytics pipeline — underwent pressing (and somewhat daunting) challenges in the domain of Social Sciences. The types of available data fall into various categories: social data (e.g., Twitter feeds, Facebook likes), data about mobility and geospatial locations (e.g., sensor data collected through mobile phones or satellite images), data collected from government administrative sources and multilingual text datasets, only to name a few. Big data bring us into a new scientific and technological era offering architectures and infrastructure (clouds, Hadoop-like computing, NoSQL databases) that allow better data management and analytics for decision-making.

Topics of interest

Several major issues have to be closely investigated around BI & big data applications in various fields. Topics include, but are not limited to (* refers to social, medicine, agriculture and so on):

  • Data warehousing, OLAP and ETL tools for *data
  • BI applications: administration, science, health, society, bioinformatics…
  • BI for social networks
  • BI analytics
  • Big data Applications: administration, science, health, society, bioinformatics…
  • Big data analytics for social and humanities, medicine…
  • BI & Big Data for Text, graph, stream… data
  • Novel decision information systems applications
  • Digital humanities
  • NoSQL data storage
  • Data lakes, Metadata management
  • Data Heterogeneity issues in Social data
  • Novel data collection techniques for reliable Social data
  • Open data exploitation
  • Alert systems