NSF Grant at ASU : RanKloud: Data Partitioning and Resource Allocation Strategies for Scalable Multimedia and Social Media Analysis
Investigator(s):
K. Selcuk Candan candan@asu.edu (Principal Investigator)
Hari Sundaram (Co-Principal Investigator)
Maria Luisa Sapino (Co-Principal Investigator)

ABSTRACT

Today, multimedia data are produced and consumed in massive quantities in a broad range of applications with significant economic and societal benefit, including e-commerce, surveillance, education, web services, and social media. Hence, there is an urgent need for systems to provide highly scalable processing and efficient analysis of large media data collections. The RanKloud prototype system, developed in this research project, focuses on the needs and requirements of applications that deal with large quantities of multimedia data in a cloud-based scalable environment. 

Most multimedia applications share a few core operations, including integration/fusion, classification, clustering, graph analysis, near-neighbor search, and similarity search. When performed naively, however, these core operations are often very costly, because the number of objects and object features that need to be considered can be prohibitive. Avoiding this cost requires that redundant work is avoided. This research focuses on the next generation cloud-based massive media processing and analysis systems where the fundamental principles that govern their design include an awareness of the utilities of data and features to a particular analysis task. Incorporating data and feature utilities for performing a particular utility task is expected to significantly reduce the overall cost of the analysis task. The RanKloud project research plan includes: (1) data model and query language to specify multimedia data processing workflows; (2) adaptable, rank-aware parallel multimedia data processing primitives; (3) run-time data sampling strategies to support adaptation to data and resource; and (4) waste- and unbalance-avoidance strategies for utility-aware data partitioning, resource allocation, and for incremental batched processing.

RanKloud bridges an important gap in our understanding of cloud-based computing in general, and efficient processing of multimedia data in particular. The results are expected to enable new tools and systems supporting scalability in a large class of problems in content-aware multimedia and social media analysis with impact in web intelligence, business intelligence, and scientific and sensor applications all of which need to handle imprecise multimedia data for more effective decision making. This project provides research experience opportunities for graduate and undergraduate students and includes research results and challenges in courses, including Capstone projects. Arizona State University (ASU) recruits top-quality undergraduates through a nationally recognized residential Honors College and the Minority Access to Research Careers program. The national and international dissemination of the project results includes premier conference and journal publications, as well as open source software licenses at the RanKloud Web site (http://aria.asu.edu/rankloud).