RanKloud: Scalable multimedia data processing in server clusters

Kasim Candan; Jong Kim; Parth Nagarkar; Mithila Nagendra; Renwei Yu

doi:10.1109/MMUL.2010.70

RanKloud: Scalable multimedia data processing in server clusters

Kasim Candan, Jong Kim, Parth Nagarkar, Mithila Nagendra, Renwei Yu

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

RanKloud is an efficient, scalable, utility-aware, parallel-processing system for analysis of large media data sets.

Original language	English (US)
Article number	5629341
Pages (from-to)	64-77
Number of pages	14
Journal	IEEE Multimedia
Volume	18
Issue number	1
DOIs	https://doi.org/10.1109/MMUL.2010.70
State	Published - Jan 2011

Keywords

Large-scale multimedia processing
MapReduce
cluster architectures
data and work partitioning
nearest neighbor
ranked query processing
sampling
top-k

ASJC Scopus subject areas

Software
Signal Processing
Media Technology
Hardware and Architecture
Computer Science Applications

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10.1109/MMUL.2010.70

Cite this

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title = "RanKloud: Scalable multimedia data processing in server clusters",

abstract = "RanKloud is an efficient, scalable, utility-aware, parallel-processing system for analysis of large media data sets.",

keywords = "Large-scale multimedia processing, MapReduce, cluster architectures, data and work partitioning, nearest neighbor, ranked query processing, sampling, top-k",

author = "Kasim Candan and Jong Kim and Parth Nagarkar and Mithila Nagendra and Renwei Yu",

note = "Funding Information: This work is partially funded by a Hewlett-Packard Labs Innovation Research Program Grant: Data-Quality Aware Middleware for Scalable Data Analysis.",

year = "2011",

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AU - Kim, Jong

AU - Nagarkar, Parth

AU - Nagendra, Mithila

AU - Yu, Renwei

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KW - ranked query processing

KW - sampling

KW - top-k

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RanKloud: Scalable multimedia data processing in server clusters

Abstract

Keywords

ASJC Scopus subject areas

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