Recently, we have been witnessing huge advancements in the scale of data we routinely
generate and collect in pretty much everything we do, as well as our ability to exploit modern …

Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

Analyzing large graphs provides valuable insights for social networking and web companies
in content ranking and recommendations. While numerous graph processing systems have …

Growing main memory capacity has fueled the development of in-memory big data
management and processing. By eliminating disk I/O bottleneck, it is now possible to support …

We present Stratosphere, an open-source software stack for parallel data analysis.
Stratosphere brings together a unique set of features that allow the expressive, easy, and …

Sprocket is a highly configurable, stage-based, scalable, serverless video processing
framework that exploits intra-video parallelism to achieve low latency. Sprocket enables …

Resource-disaggregated architectures have risen in popularity for large datacenters.
However, prior disaggregation systems are designed for native applications; in addition, all …

Shark is a new data analysis system that marries query processing with complex analytics
on large clusters. It leverages a novel distributed memory abstraction to provide a unified …

MADlib is a free, open source library of in-database analytic methods. It provides an
evolving suite of SQL-based algorithms for machine learning, data mining and statistics that …

Machine learning (ML) and statistical techniques are key to transforming big data into
actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML …