BigStorage is one of the successful stories described in the CA Strategic Research Booklet:, booklet shown in the BTC Summit in Santa Clara.

The booklet is available here: (BigStorage in page 11).

Some members of BigStorage have participated in the Dagstuhl Seminar "Challenges and Opportunities of User-Level File Systems for HPC", from May 14th to May 19th, 2017. In particular, Andre Brinkmann was one of the organizers of the Seminar, which gathered many experts in the area of I/O and HPC.

More information about the Seminar:



URL of the 5th JLESC workshop:

Talk by Pierre Matri: Ty ́r: Blob Storage Systems Meet Built-In Transactions

Abstract: Concurrent Big Data applications often require high-performance storage, as well as ACID (Atomicity, Consistency, Isolation, Durability) transaction sup- port. Blobs (binary large objects) are an increasingly popular low-level model for addressing the storage needs of such applications, providing a solid base for developing higher-level storage solutions, such as object stores or distributed file systems.

URL of the LSDMA Technical Forum

URL of the BigStorage contribution:

LSDMA Technical Forum is a platform for novel and running projects to present their technical challenges, goals as well as currently open challenges. This creates an environment where the technical people can exchange expertise about state of the art solutions, discuss common challenges and possibly identify future joint projects or proposals. Topics are centered to the fields of storage, big data, identity management and performance.



URL of the 5th JLESC workshop:

Talk by Gabriel Antoniu: Spark versus Flink: Understanding Performance in Big Data Analytics Frameworks

Abstract: Big Data analytics has recently gained increasing popularity as a tool to process large amounts of data on-demand. Spark and Flink are two Apache-based data analytics frameworks that facilitate the development of multi-step data pipelines using directly acyclic graph patterns. Making the most out of these frameworks is challenging because efficient executions strongly rely on complex parameter configurations and on an in-depth understanding of the underlying architectural choices.