Smarter Storage for Big Data Analytics: Architecture and Systems

Speaker:  Yu Hua – Wuhan, China
Topic(s):  Networks and Communications

Abstract

In the era of big data, the explosive growth in data volume and complexity requires highly efficient searchable data analytics. Existing cloud storage systems have largely failed to offer an adequate capability for real-time analytics for big data. In order to support cost-efficient and real-time services in data analytics, we propose a smarter and searchable data analytics methodology. The idea is to explore and exploit the semantic correlation within and among datasets via correlation-aware hashing and manageable flat-structured addressing to significantly reduce the processing latency, while incurring acceptably small loss of data-search accuracy. The near-real-time property enables rapid identification of correlated files and the significant narrowing of the scope of data to be processed. The proposed scheme supports several types of data analytics, which can be implemented in existing searchable storage systems. 

About this Lecture

Number of Slides:  50
Duration:  60 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.