Social influence determination on big data streams in an online social network

2017 ◽  
Vol 76 (21) ◽  
pp. 22133-22167
Author(s):  
Kumaran P. ◽  
Chitrakala S.
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Influence ◽  
Data Streams ◽  
Online Social Network ◽  
Big Data Streams

Linear Scheduling of Big Data Streams on Multiprocessor Sets in the Cloud

2019 ◽  
Author(s):  
Nicoleta Tantalaki ◽  
Stavros Souravlas ◽  
Manos Roumeliotis ◽  
Stefanos Katsavounis
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Linear Scheduling ◽  
Big Data Streams

Boosting heapsort performance of processing Big Data streams

2016 ◽  
Author(s):  
Usamah Algemili ◽  
Adi Alhudhaif
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Big Data Streams

A spatial-adaptive sampling procedure for online monitoring of big data streams

2018 ◽  
Vol 50 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Andi Wang ◽  
Xiaochen Xian ◽  
Fugee Tsung ◽  
Kaibo Liu
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Adaptive Sampling ◽  
Online Monitoring ◽  
Sampling Procedure ◽  
Big Data Streams

scholarly journals Methods of Situation Analysis and Graphical Visualisation of Big Data Streams

2018 ◽  
Author(s):  
А.В. Пролетарский ◽  
◽  
Д.В. Березкин ◽  
Ю.Е. Гапанюк ◽  
И.А. Козлов ◽  
...  
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Situation Analysis ◽  
Big Data Streams

Shifting Perspectives

2015 ◽  
Vol 6 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Geoffrey Hill ◽  
Pratim Datta ◽  
William Acar
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Knowledge Flows ◽  
Process Models ◽  
Data Sources ◽  
Management Process ◽  
Actionable Knowledge ◽  
Sources Of Uncertainty ◽  
Level Of Knowledge ◽  
Big Data Streams

This paper proposes that, in the context of generating actionable knowledge, uncertainties pertaining to big data streams should be recognized, categorized and accounted for at the appropriate level of knowledge management process models. Arguing that sensemaking from big data sources is a complex series of processes extending beyond just the application of sophisticated analytics, this paper proposes a big data reengineering (BDR) framework to guide requisite categorization, contextualization and remediation processes. The authors discuss the characteristics that uncertainty presents to organizations using big data streams as potential knowledge sources – surfacing relationships between the underlying knowledge flows and uncertainty and presenting typologies that categorize the effects of several common sources of uncertainty. These typologies also serve to provide guidance to transformation agent(s) regarding appropriate actions ultimately aimed at the generation of actionable knowledge.

Sign in / Sign up

Export Citation Format

Share Document