The Big Data era is upon us: data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of technology and society. Since the value of data increases exponentially when it can be linked and fused with other data, addressing the big data integration challenge is critical to realizing the promise of Big Data -- and conversely, Big Data techniques are critical to the goals of simplifying data integration.
The convergence of Big Data and data integration is emerging in many forms, largely motivated by the goals of integrating structured data on the Web or across communities. Increasingly we are seeing problems where (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as large volumes of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for conceptually similar entities, and (iv) the data sources are of widely differing quality, with significant differences in the coverage, accuracy and timeliness of data provided.
This workshop will focus on the progress that is being made to address these novel challenges faced by big data integration. It will bring together researchers from data integration, data cleaning, machine learning, and data analysis to address these issues, and we expect to identify a range of open problems for the community. Questions of interest during the workshop include: