Heterogeneous memory management combined with server virtualization in datacenters is
expected to increase the software and OS management complexity. State-of-the-art …

Large quantities of data are generated continuously over time and from disparate sources
such as users, devices, and sensors located around the globe. This results in the need for …

Many applications must ingest rapid data streams and produce analytics results in near-real-
time. It is increasingly common for inputs to such applications to originate from …

Streaming analytics require real-time aggregation and processing of geographically
distributed data streams continuously over time. The typical analytics infrastructure for …

Rapid data streams are generated continuously from diverse sources including users,
devices, and sensors located around the globe. This results in the need for efficient geo …

Popular big data frameworks, ranging from Hadoop MapReduce to Spark, rely on garbage-
collected languages, such as Java and Scala. Big data applications are especially sensitive …

For hosting data-serving and caching workloads based on key-value stores in clouds, the
cost of memory represents a significant portion of the hosting expenses. The emergence of …

Many database applications, such as OnLine Analytical Processing (OLAP), web-based
information extraction or scientific computation, need to select a subset of fields based on …

Stream processing has become a key means for gaining rapid insights from webserver-
captured data. Challenges include how to scale to numerous, concurrently running …

(57) ABSTRACT A system and method support a reference store in a distrib uted computing
environment such as a distributed data grid. The system associates a ticket with the …