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Divyakant Agrawal
Researcher at University of California, Santa Barbara
Publications - 472
Citations - 20772
Divyakant Agrawal is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Scalability & Cloud computing. The author has an hindex of 72, co-authored 458 publications receiving 19789 citations. Previous affiliations of Divyakant Agrawal include University of California & Ask.com.
Papers
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Proceedings ArticleDOI
Limiting the spread of misinformation in social networks
TL;DR: This work study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the concept of limiting campaigns to counteract the effect of misinformation.
Proceedings ArticleDOI
Big data and cloud computing: current state and future opportunities
TL;DR: This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications, and crystallizes the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.
Book ChapterDOI
Efficient computation of frequent and top-k elements in data streams
TL;DR: In this paper, the authors propose an integrated approach for finding the most popular k elements and finding frequent elements in a data stream, which is efficient and exact if the alphabet under consideration is small.
Proceedings ArticleDOI
Medians and beyond: new aggregation techniques for sensor networks
TL;DR: This paper proposes a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks, and provides strict theoretical guarantees on the approximation quality of the queries in terms of the message size.
Posted Content
Medians and Beyond: New Aggregation Techniques for Sensor Networks
TL;DR: In this article, the authors proposed a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks, such as the median, the consensus value, a histogram of the data distribution, and range queries.