Paper Info
Title
Model-Driven Analytics: Connecting Data, Domain Knowledge, and Learning.
Abstract
Gaining profound insights from collected data of todayu0027s application domains like IoT, cyber-physical systems, health care, or the financial sector is business-critical and can create the next multi-billion dollar market. However, analyzing these data and turning it into valuable insights is a huge challenge. This is often not alone due to the large volume of data but due to an incredibly high domain complexity, which makes it necessary to combine various extrapolation and prediction methods to understand the collected data. Model-driven analytics is a refinement process of raw data driven by a model reflecting deep domain understanding, connecting data, domain knowledge, and learning.
Year
Venue
Field
2017
arXiv: Software Engineering
Data science,Data domain,Domain knowledge,Software analytics,Computer science,Internet of Things,Raw data,Semantic analytics,Analytics,Liberian dollar
DocType
Volume
Citations 
Journal
abs/1704.01320
1
PageRank 
References 
Authors
0.43
7
7
Name
Order
Citations
PageRank
Thomas Hartmann 00011458.08
Assaad Moawad2315.37
François Fouquet311715.16
Gregory Nain425916.56
Jacques Klein52498112.20
Yves Le Traon63922190.39
Jean-Marc Jézéquel73050219.89