Paper Info
Title
FAST: A High-Performance Architecture for Heterogeneous Big Data Forensics.
Abstract
We are presenting a highly-efficient, novel architecture (which we call FAST, or Forensic Analysis of Sensitive Traces) for high-performance big data forensics for heterogeneous systems (CPU and GPU-based). Our model uses a highly-compact storage format of the widely known Aho-Corasick algorithm [1], as well as a partial pruning mechanism to ensure the lowest possible memory footprint, while maximizing throughput performance. We are comparing our performance with classic methods used in data forensics and observe significant memory footprint improvements, as well as massive throughput improvements throughout all stages of big data processing.
Year
DOI
Venue
2017
10.1007/978-3-319-67180-2_60
INTERNATIONAL JOINT CONFERENCE SOCO'17- CISIS'17-ICEUTE'17 PROCEEDINGS
Keywords
Field
DocType
Data forensics,Big data,High performance computing,Efficient storage,Aho-corasick,GPU processing
Big data processing,World Wide Web,Architecture,Computer architecture,Supercomputer,Computer science,Throughput,Memory footprint,Aho–Corasick string matching algorithm,Big data
Conference
Volume
ISSN
Citations 
649
2194-5357
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Ciprian Pungila1114.51
Viorel Negru231147.71