BDQM 2016 - 1st International Workshop on Big Data Quality Management(BDQM 2016)

With the development of information technology, big data arise in various applications and areas. On one hand, big data bring new value. On the other hand, new challenges are brought. One of the challenges is the data quality problem.
The features of big data bring more serious data quality problems. Due to volume, the harvest, storage, transmission and computation will cause more errors. Current data get outdated for velocity. The variety leads to more inconsistency and conflicts. Data quality problem will do harm to the applications of big data, even result in disaster.
As a result, big data quality management is in demand to decrease the harm of data quality problems and computes high-quality problem from big data. Big data management has become one of the hottest issues not only in database community but also in artificial intelligence, data mining and other related area.
The goal of the Workshop on Big Data Quality Management is to raise the awareness of quality issues in Big data and promote approaches to evaluate and improve big data quality.
The workshop topics include, but are not limited to:
Data Quality Models and Theory
Data Quality Measures and Evaluations
Data Cleaning Algorithms
Record Linkage and Entity Resolution
Privacy Preservation and Security Issues in the Process of Data Cleaning
Data Quality Policies and Standards
Data Provenance and Annotation
Data Quality in Information Retrieval and Extraction
Probabilistic, Fuzzy, and Uncertain Data Management
Data Quality in Sensor Networks and CPS
Data Quality in Information Integration
Crowdsourcing for Data Quality
Master Data Management
Applications for Data Quality Management
Error-Tolerate Computation