We are pleased to announce that Journal of Big Data is now included in the Emerging Sources Citation Index (ESCI). All articles published after January 1, 2018 will be included in the database, and will therefore be searchable in Web of Science.
Journal News
Call for papers: Applications and development in Linked Open Data (LOD) cloud
This special issue proposal intends to capture the state-of-the-art applications and technologies such as the semantic web, machine learning, deep learning, natural language processing, internet of things, knowledge graph, process mining, and artificial intelligence, etc. which encompasses the wider spectrum of the Linked Open Data (LOD).
Call for papers: Advanced bio-inspired deep learning algorithms for multi-modal perceptual big data analysis in car-driver assistance systems
Next generation cars collect a wide variety of data coming from different systems that need to be processed and merged in order to provide adequate car driving assistance. The thematic series will look at approaches to car-driver assistance systems that make use of sophisticated bio-inspired algorithms and modern deep learning approaches.
Articles
-
-
Bayesian negative binomial logit hurdle and zero-inflated model for characterizing smoking intensity
-
A reconstruction error-based framework for label noise detection
-
An analysis of the graph processing landscape
-
Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)
Aims and scope
The Journal of Big Data publishes open-access original research on data science and data analytics. Deep learning algorithms and all applications of big data are welcomed. Survey papers and case studies are also considered.
The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.
The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.
Follow
Annual Journal Metrics
-
Speed
40.7 days to first decision for reviewed manuscripts only
40.3 days to first decision for all manuscripts
108 days from submission to acceptance
18 days from acceptance to publicationCitation Impact
2.501 - Source Normalized Impact per Paper (SNIP)
0.925 - SCImago Journal Rank (SJR)
6.1 - CiteScore
Usage
647,436 downloads
549 Altmetric mentions
- ISSN: 2196-1115 (electronic)