Call For Papers
Enterprises today are faced with the challenge of providing data management solutions for large, heterogeneous datasets that are often the amalgamation of disparate data and programming models. Developing analytics that can use the content of such datasets requires solutions that leverage many “sizes.” Parallel database management systems (DBMSs) and federated DBMSs may provide high performance solutions for parts of the data, but it is unlikely that any single engine can provide high performance access to all parts of complex datasets. New techniques such as polystore databases show promise in supporting heterogeneous datasets by supporting complete functionality of underlying DBMSs, and multiple query notations or languages.
In this workshop, we will bring together experts in the field to discuss how novel techniques for managing heterogeneous data such as polystore databases can be applied to highly diverse datasets.
Research topics included in workshop:
- New Computational Models for Big Data
- Languages/Models for integrating disparate data such as graphs, arrays, relations
- Query evaluation and optimization in federated and polystore systems
- Efficient data movement and scheduling, failures and recovery for polystore analytics
- High Performance/Parallel Computing Platforms for Big Data
- Integration of HPC and Big Data platforms
- Data Acquisition, Integration, Cleaning, and Best Practices
- Mathematics for Polystore systems
- Machine learning using complex data management
- Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Transportation, Retailing, Telecommunication, Government and Defense applications