Prasad Chakka

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The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop [3] is a popular open-source map-reduce implementation which is being used as an alternative to store and process extremely large data sets on commodity hardware. However,(More)
The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop [1] is a popular open-source map-reduce implementation which is being used in companies like Yahoo, Facebook etc. to store and process extremely large data sets on commodity(More)
We introduce a simple data model and API tailored for serving the social graph, and TAO, an implementation of this model. TAO is a geographically distributed data store that provides efficient and timely access to the social graph for Facebook’s demanding workload using a fixed set of queries. It is deployed at Facebook, replacing memcache for many data(More)
Over 800 million people around the world share their social interactions with friends on Facebook, providing a rich body of information referred to as the social graph. In this talk, I describe how we model and serve this graph. Our model uses typed nodes (fbobjects) and edges (associations) to express the relationships and actions that happen on Facebook.(More)
XML is now widely used and management of XML data has become important. To this end, there has been work on the native management of XML data in a database to utilize the different capabilities of such a system like transaction management and indexing structures. At the heart of such a native XML database is the query evaluator, which provides access(More)
Efficient management of large multidimensional datasets has attracted much attention in the database research community. Such large multidimensional datasets are common and efficient algorithms are needed for analyzing these data sets for a variety of applications. In this thesis, we focus our study on two very common classes of analysis: similarity and(More)
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