Big Data Engineering Minitrack
This minitrack will cover advances in the broad range of activities that are required to cost-effectively plan, design, build, evolve and manage big data systems. The sheer scale and pace of change mandates the computer science and software engineering communities develop new methods and tools to plan, design, build, evolve and manage big data systems that derive on-going business value. Big Data Engineering (BDE) provide a focus for the synergies that must be developed across technical disciplines to reliably deliver production- level applications that handle unprecedented amounts of data, new variety of data type and real time velocity.
To this end, this minitrack will solicit papers in the following areas:
- New system architectures and methods for big data sourcing/harnessing, ingestion, storage, processing, exploration, analysis and visualization.
- Methods for integration, interoperability and metadata modeling for big data access.
- Hybrid architectures/models for big data systems coexisting with traditional data warehouses
- Software engineering methods and decision support tools for massively scalable systems development
- Advanced technologies: MPP, NoSQL databases, real time stream processing, in-memory processing frameworks, scalable analytics, big data cloud technologies and platforms,
- Engineering methods for generating and facilitating innovation and/or new business models from big data collections and analytics.
- New programming models and languages for big data
- Engineering approaches for big data governance, compliance, privacy and security
- Novel big data engineering approaches in specific application domains.
- Other relevant topics related to big data engineering such as education/curriculum issues for producing big data engineers.
Hong-Mei Chen (Primary Contact)
University of Hawaii at Manoa
University of Colorado
Wietske van Osch
Michigan State University