the 16th international conference on Web Information Systems Engineering (WISE'15)

1st – 3rd November 2015

Miami, Florida .

Call For Papers

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. Topics of Interest.

The QUAT workshop is a qualified forum for presenting and discussing novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for "Big Data". It is to provide the researchers in the areas of web technology, e-services, social networking, big data, data processing, trust, and information systems and GIS a forum to discuss and exchange their recent research findings and achievements. The workshop is expected to have two types of papers – regular ones (12 pages) and short ones (6 pages), in order to attract the most up-to-date research results and research in progresses. Topics include, but are not limited to, the following

 

Topics

• Data quality
  - Data quality in big data
  - Data quality assessment, measures and improvement methodologies
  - Data quality mining Data quality on novel data management architectures (cloud, streaming data, ...)
  - Data quality in environmental, transport, manufacture data
  - Data quality in the web data
  - Privacy-preserving data quality
  - Quality in data collection, processing, and storage
  - Quality-aware analytics solutions
  - Quality for data, information, and knowledge
  - Quality of scientific, geographical, and biologic databases
  - information quality in information systems
  - information quality in geo-information systems (GIS)
  Trust issues
  - Conflict resolution and data fusion
  - Cleaning extremely large datasets
  - Data scrubbing, data standardization, data cleaning techniques
  - Trust in big data,
  - Trust in social networking data,
  - Trust distribution, propagation, and computation
  - Identity and Trust Management
  - Conceptual models and algebra for trust

IMPORTANT DATES            

Paper submission: July 25, 2015

                       

Notification of acceptance: August 5, 2015

           

Final manuscripts due: August 15, 2015

                      

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