September 1, 2013

Co-located with ADBIS 2013, Genoa, Italy

Extended submission deadline: May 5, 2013

Business intelligence systems enable companies to transform their business data into timely and accurate information for the decisional process; they are used by decision makers to get a comprehensive knowledge of the business and of the factors that affect it, to define and support their business strategies and in general, to increase profitability. On the other hand, the planetary success of social networks and the widespread diffusion of portable devices has contributed, during the last decade, to a significant shift in human communication patterns towards the voluntary sharing of personal information. This has resulted in the accumulation of enormous amounts of social data, that include geolocation, preferences, opinions, news, articles, etc.

Social Business Intelligence (SBI) is the discipline of effectively and efficiently combining corporate data with social data to let decision-makers effectively analyze and improve their business based on the trends and moods perceived from the environment. As in traditional Business Intelligence, the goal is to enable powerful and flexible analyses for users with a limited expertize in databases and ICT.

SBI is at the cross-road of several areas in Computer Science such as Database Systems, Information Retrieval, Data Mining, Natural Language Processing, and Human-Computer Interaction. Though the ongoing research in these single fields has made available a bunch of results and enabling technologies for SBI, an overall view of the related problems and solutions is still missing. Besides, the peculiarities of SBI systems open new research problems in all the previous areas. The goal of the SoBI workshop is to put together researchers and practitioners coming from different areas related to SBI for sharing their findings and cross-fertilizing their researches.


The scope of the SoBI workshop includes, but is not limited to:

  1. Algorithms and Techniques
    • Text mining and topic discovery
    • Integration of social data
    • Opinion mining and sentiment analysis
    • Mass opinion estimation and trend detection in social data
    • Emotion detection and analysis in sentences, documents, and speech transcripts
    • Multilingual sentiment analysis
    • SBI and the semantic web
    • Extraction, transformation, and loading for SBI
    • Computational treatment of large amounts of user-generated content
    • Query processing, optimization, and performance
    • Visual interfaces and OLAP on social data
    • Analytics for SBI
    • Mining and what-if analysis on social data
  2. Architectures and Methodology
    • Advanced architectures for SBI
    • Cloud SBI
    • Tools for SBI
    • Spatial data warehouses and location intelligence
    • Methodological support for SBI
    • Requirement analysis, design, implementation, and testing of SBI systems
    • Modeling of social data
    • Methods and benchmarks for evaluating SBI systems
    • Case studies and project experiences