Automating Process of Big Data Analytics Using Service Composition
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".
Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 3320
Special Issue Editors
Interests: deep learning applications in semantic web services, brain signal, natural language processing, and web data together with big data infrastructure and analytics
Interests: web service clustering; discovery; selection and composition; big data analytics; data mining
Special Issue Information
Dear Colleagues,
Big data analytics (BDA) extract valuable knowledge from large sets of data and provide support for decision making by discovering patterns. Thus, companies that utilize advanced BDA techniques will grow faster than their competitors. The big data analytics process contains several steps, such as data collection, preprocessing and modeling. The fact that these steps must be carried out manually in the BDA process hinders the ability to make quick decisions in real-time data applications. Therefore, it is necessary to automate the BDA process. The BDA process can be modeled as a workflow, and the Automatic Service Composition (ASC) technique can be used to realize some level of automation. ASC consists of four stages: planning, discovery, selection, and execution. It can be used to orchestrate existing services to complete larger tasks, resulting in a new composite and value-added web service. The automatic BDA process can be improved significantly by utilizing machine learning (ML) and deep learning (DL) approaches. In this Special Issue, topics including, but not limited to, BDA, the automation of BDA via ASC, and the application of ML/DL regarding BDA and its automation can be covered.
Prof. Dr. Incheon Paik
Prof. Dr. B. T. G .S. Kumara
Guest Editors
Manuscript Submission Information
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Keywords
- big data
- big data analytics
- big data as a service
- service computing
- service composition
- machine learning
- deep learning
