… storage itself to robustly speed up analytics jobs. We make the following contributions: We
propose SwiftAnalytics, an enhanced object storage … analytics frameworks and object storage …

… To achieve the optimal performance of big-data analytics … in a data center, and how to
apportion given big-data to chosen … employed as a big-data analytics service (see Section V). …

… optimizing social locality we aim to minimize the cost incurred by storage and communications,
while through optimizing … More importantly, we combine data placement and data …

… timeliness of data migration, and not enough innovation in data analysis, we design a
more efficient, convenient and easy-to-use big data management platform. Firstly, the big …

… This paper focuses on high performance, scalability and memory efficiency for data shuffling
in the context of big data analytics over high end infrastructure. To our best knowledge, we …

… system and different types of data should be handled … the storage manager to simultaneously
manage different data … utilizes that information to optimize page replacement decisions …

… for a distributed database designed to optimize … distributed query execution plans with fast
optimization times. We discuss the problem of query rewrite decisions in a distributed database…

… scope of data analytics problems. We describe commonly used hardware platforms for executing
analytics … The obvious advantage is the immediate sharing of optimizations in the virtual …

… on different cloud storage services, and … data placement and storage provisioning plan.
Furthermore, we build Cast++ to enhance Cast’s optimization model by incorporating data reuse …

… the storage layer. To this end, a new generation of in-memory big data analytics frameworks
… By making heavy use of in-memory data caching, Spark minimizes the interactions with the …