The 3Vs - Volume, Velocity and Variety - of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades to compress signals are often undefined in data that is characterized by heterogeneity, high dimensionality, and lack of known structure. In this article, we describe and demonstrate an approach to sample large datasets such as social media data. We evaluate the effect of sampling on a common predictive analytic: link prediction. Our results indicate that greatly sampling a dataset can still yield meaningful link prediction results.
|Title of host publication||Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - 26 Feb 2016|
|Event||49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States|
Duration: 8 Nov 2015 → 11 Nov 2015
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015|
|Period||8/11/15 → 11/11/15|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications