We consider distributed applications that continuously stream data across the network, where data needs to be aggregated and processed to produce a 'useful' stream of up...
Vibhore Kumar, Brian F. Cooper, Zhongtang Cai, Gre...
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed...
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar...
Abstract. We are witnessing a dramatic increase in the use of datacentric distributed systems such as global grid infrastructures, sensor networks, network monitoring, and various ...
Requirements from new types of applications call for new database system solutions. Computational science applications performing distributed computations on Grid networks with req...
Traditional methods for data mining typically make the assumption that data is centralized and static. This assumption is no longer tenable. Such methods waste computational and I/...
Adriano Veloso, Matthew Eric Otey, Srinivasan Part...