The WISE-2017 workshops are an international forum for researchers, professionals, and industrial practitioners to share their knowledge in topics related to the main theme of the conference, either on specific research themes or in emerging areas of research.

Accepted Workshops

  1. The 5th International Workshop on Data Quality and Trust in Big Data (QUAT 2017)

    The problem of data quality in data processing, data management, data analysis, and information systems largely and indistinctly affects every application domain, especially at the era of "Big Data". "Big Data" has the characteristics of huge volume in data and a great variety of structures or no structure. "Big Data" is increased at a great velocity everyday and may be less trustable. The use of big data underpins critical activities in all sectors of our society. Many data processing tasks (such as data collection, data integration, data sharing, information extraction, and knowledge acquisition) require various forms of data preparation and consolidation with complex data processing and analysis techniques. Achieving the full transformative potential of "Big Data" requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive scale analytics. The consensus is that the quality of data and the veracity of data have to span over the entire process of data collection, preparation, analysis, modelling, implementation, use, testing, and maintenance, including novel algorithms and usable systems.
    The workshop website is at:

  2. The 1st International Workshop on Knowledge Acquisition, Representation and Management (KARM 2017)

    As the development of World Wide Web, social networking sites, wikis and folksonomies are becoming more and more popular, where the Knowledge Acquisition, Representation and Management (KARM) are the crucial aspects of successful intelligent systems. Knowledge Acquisition is central to the design of cognitive systems. Knowledge should be in a form that allows systems to explain their inferences and accept user feedback. At the same time, knowledge acquisition should exhibit characteristics akin to those of human learning, so that humans can relate to it and be able to interact with it as if it were a knowledgeable colleague. Moreover, the new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets. Furthermore, Knowledge Management stresses the importance of using KM to enhance knowledge production in organizations, not just knowledge sharing or integration. The goal of this workshop is to bring together the researchers involved in the development and application of Knowledge Acquisition, Representation and Management techniques.
    The workshop website is at: