Shivakumar Venkataraman

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Traditional disk arrays have a centralized architecture, with a single controller through which all requests flow. Such a controller is a single point of failure, and its performance limits the maximum size that the array can grow to. We describe here TickerTAIP, a parallel architecture for disk arrays that distributed the controller functions across(More)
Next generation decision support applications, besides being capable of processing huge amounts of data, require the ability to integrate and reason over data from multiple, heterogeneous data sources. Often, these data sources differ in a variety of aspects such as their data models, the query languages they support, and their network protocols. Also,(More)
Web-based enterprises process events generated by millions of users interacting with their websites. Rich statistical data distilled from combining such interactions in near real-time generates enormous business value. In this paper, we describe the architecture of Photon, a geographically distributed system for joining multiple continuously flowing streams(More)
DataJoiner (DJ) is a heterogeneous database system that provides a single database image of multiple databases. It provides transparent access to tables at remote databases through user defined aliases (nicknames) that can be accessed as if they were local tables. DJ is also a fully functional relational database system. A couple of salient features of the(More)
In this paper we describe the design and implementation of ParSets, a means of exploiting parallelism in the SHORE OODBMS. We used ParSets to parallelize the graph traversal portion of the OO7 OODBMS benchmark , and present speedup and scaleup results from parallel SHORE running these traversals on a cluster of commodity workstations connected by a standard(More)
Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy a complex and challenging set of user and systems requirements , including near real-time data ingestion and querya-bility, as well as high availability, reliability, fault tolerance,(More)
The recent dramatic improvements in the performance of commodity hardware has made clusters of workstations or PCs an attractive and economical platform upon which to build scalable database servers. These clusters have large aggregate memory capacities; however, since this global memory is distributed, good algorithms are necessary for memory management,(More)