R
Raghavendra Rao Chillarige
Researcher at University UCINF
Publications - 10
Citations - 402
Raghavendra Rao Chillarige is an academic researcher from University UCINF. The author has contributed to research in topics: Big data & Cloud computing. The author has an hindex of 5, co-authored 10 publications receiving 341 citations.
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The Anatomy of Big Data Computing
Raghavendra Kune,Pramod Kumar Konugurthi,Arun Agarwal,Raghavendra Rao Chillarige,Rajkumar Buyya +4 more
TL;DR: The evolution of big data computing, differences between traditional data warehousing and big data, taxonomy ofbig data computing and underpinning technologies, integrated platform of bigdata and clouds known as big data clouds, layered architecture and components of bigData cloud, and finally open‐technical challenges and future directions are discussed.
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The anatomy of big data computing
Raghavendra Kune,Pramod Kumar Konugurthi,Arun Agarwal,Raghavendra Rao Chillarige,Rajkumar Buyya +4 more
TL;DR: In this paper, the authors discuss the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of Big Data computing and underpinning technologies, integrated platform of Big data and clouds known as big data clouds, layered architecture and components of big Data cloud, and finally open-technical challenges and future directions.
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Generating cancellable fingerprint templates based on Delaunay triangle feature set construction
TL;DR: In this study, the authors propose a novel fingerprint template protection scheme that is developed using Delaunay triangulation net constructed from the fingerprint minutiae using two methods namely FS_INCIR and FS_AVGLO to construct a feature set from the Delauny triangles.
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XHAMI – extended HDFS and MapReduce interface for Big Data image processing applications in cloud computing environments
Raghavendra Kune,Pramod Kumar Konugurthi,Arun Agarwal,Raghavendra Rao Chillarige,Rajkumar Buyya +4 more
TL;DR: Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process and can be utilized by many applications where there is a requirement of overlapped data.
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New replica selection technique for binding replica sites in Data Grids
TL;DR: A replica selection strategy that adapts its criteria dynamically so as to best approximate application providers' and clients' requirements is presented and a new selection technique (EST) is introduced that shows improved performance over the more common algorithms.