DD 2016 - Special Issue on Data to Decision

Computing and networking, data collection and analytics have been booming in the recent decades to manifest the coming big data era. Today, more and more data are being collected and analysed by organisations in order to make critical decisions. However, many challenges still need to be addressed; for example, the difficulty of tracking the versions of data sets in workflows, supporting data scientists in exploring subsets of data locally, sharing reproducible exploration and scaling up, and keeping semantic and physical consistency of views among distributed and multiple data stores. This special issue aims to present solutions to these challenges from both researchers and practitioners.
Suitable topics include, but are not limited to, the following:
Data to decision management
Data transformation and analytics pipelines
Workflow management in service and scientific computing
Entity resolution/matching and schema matching
(Big) data provenance and lineage
Data processing and tracking
Data management in data science, AI, data mining and machine learning
Data management in exploration environments and production environments
Data and data computing platforms
Service-oriented architecture for data analytics pipelines
Data analytics as services
Trustworthiness of data and analytics
Reproducible data analytics and decisions
Traceability from decisions to data
Understanding and explaining of decisions
Mining and analysis of contracts and e-contracts
Entity matching
Data analytics and pipelines
Transactions in web services
Semantic issues in analysis and verification of APIs
Data provenance and lineage
Data automata
Functional programming for data processing
Approximate computing for big data
Submission method:
http://www.inderscience.com/info/inauthors/author_...
1. Prepare your paper
2. Register as an author if you do not have an account
3. Select Journal of Big Data Intelligence
4. Select Special Issue of Data to Decision
4. Follow the instruction to finish your submission