QUAT 2014 - Workshop on Data Quality and Trust in Big Data

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.
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”. Topics include, but are not limited to, the following.
- 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)
- 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,