Scope
Big data has emerged as a new paradigm to deal with the processing issues of large volumes of data. Big data and cloud computing have a reciprocal relationship. One provides the data as services to meet users' needs, and the other provides the services to interact with data, allowing their processing and management. This "servicelization" across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. The concept of “big service” was introduced for the first time in 2015. It consists of the encapsulation, abstraction, and the processing of big data, allowing, then, to hide their complexity.
However, this promising approach still lacks a real understanding and management facilities and tools. Also, the few existing researches have dealt with big services as a class of the traditional Web and data services, thus inheriting their issues. Hence, the need to reconsider service computing challenges in the era of big data arises. Such a big data-centric service model needs new methods, techniques and solutions that take advantage of various fields including data integration, data security and provenance, data streaming, machine learning, distributed and parallel processing, graph processing, etc. In this context, frameworks, and solutions for designing, composing, executing, and managing big services become a major and urgent need.
The purpose of this workshop is to provide an understanding of the emerging big service model from the lifecycle management phases' point of view, and to summarize the researchers insights on big data-centric services. The workshop seeks innovative research ideas and results focusing on big services’ data-driven lifecycle management.
Workshop topics
By organizing this workshop, we aim to collect recent and significant solutions in the area of "Big Data-centric Services". We look for original and high-quality submissions related to (but not limited to) the following topics:
Quality models for big services.
Data quality in big services.
Big service management and adaptation.
Maintenance and evolution of big data-centric services.
Change management in large-scale big services.
Big services in smart environments.
Middlewares and big data frameworks for big services.
Security, privacy, and trust models for big data-centric services.
Cloud computing for big services.
Mining, analytics, and machine learning for big services.
Microservices architectures for big services.
Stream computing for big services.
Data stream systems and sensor clouds for big services.
Parallel computing for big services.
Large-scale recommender systems for big services.
Data models, semantics, and query languages for big services.
Engineering of big data-centric services
Design of real-word big services.
Big Data Analytics for big Services.
Big service applications: smart cities, urban services, healthcare, etc.