Big data machine learning and graph analytics: Current state and future challenges

Author

Huang, He Helen ; Hang Liu

Author_Institution

Dept. of Electr. & Comput. Eng., George Washington Univ., Washington, DC, USA

fYear

2014

fDate

27-30 Oct. 2014

Firstpage

16

Lastpage

17

Abstract

Big data machine learning and graph analytics have been widely used in industry, academia and government. Continuous advance in this area is critical to business success, scientific discovery, as well as cybersecurity. In this paper, we present some current projects and propose that next-generation computing systems for big data machine learning and graph analytics need innovative designs in both hardware and software that provide a good match between big data algorithms and the underlying computing and storage resources.

Keywords

Big Data; graph theory; learning (artificial intelligence); Big Data algorithms; Big Data machine learning; business success; computing resources; cybersecurity; graph analytics; hardware innovative designs; next-generation computing systems; scientific discovery; software innovative designs; storage resources; Big data; Computer architecture; Conferences; Graphics processing units; Hardware; Machine learning algorithms; Nonvolatile memory; Big Data; Graphics Processing Unit; Hardware and Software Co-Design; Lambda Architecture; Non-Volatile Memory; Solid-State Drive;

fLanguage

English

Publisher

ieee

Conference_Titel

Big Data (Big Data), 2014 IEEE International Conference on

Conference_Location

Washington, DC

Type

conf

DOI

10.1109/BigData.2014.7004471

Filename

7004471