Formal verification of the extension of iStar to support Big data projects

Identifying all the right requirements is indispensable for the success of anysystem. These requirements need to be engineered with precision in the earlyphases. Principally, late corrections costs are estimated to be more than 200times as much as corrections during requirements engineering (RE). EspeciallyBig data area, it becomes more and more crucial due to its importance andcharacteristics. In fact, and after literature analyzing, we note that currentsRE methods do not support the elicitation of Big data projects requirements. Inthis study, we propose the BiStar novel method as extension of iStar to under-take some Big data characteristics such as (volume, variety ...etc). As a firststep, we identify some missing concepts that currents requirements engineeringmethods do not support. Next, BiStar, an extension of iStar is developed totake into account Big data specifics characteristics while dealing with require-ments. In order to ensure the integrity property of BiStar, formal proofs weremade, we perform a bigraph based description on iStar and BiStar. Finally, anapplication is conducted on iStar and BiStar for the same illustrative scenario.The BiStar shows important results to be more suitable for eliciting Big dataprojects requirements

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Requirements Engineering in the Context of Big Data Software Applications

Big Data applications, like traditional applications, serve end-user needs except that underlying the software system is Big Data which the system operates upon to improve or provide different end-user experience with the application. In comparison to traditional software development where the development processes are usually well-established, the development of Big Data applications is - to our knowledge - not explored to any significant degree. With Big Data, characterised by the well-known V attributes, questions arise as to how to elicit, specify, analyse, and document system requirements. While requirements engineering (RE) has long been recognised as critical for downstream development of computer systems, the field is currently passive about how to deal with characteristics of data in the RE process in the development of Big Data software applications. This problem is compounded by the fact that the RE field had no domain model (until recently) for Big Data systems depicting the various artefacts, activities, and relationships amongst them that, in turn, can be used to support RE specifications, product design, project decisions, and maintenance. In this thesis research, we investigated empirically a number of issues in RE involving Big Data applications, leading to the following research contributions: (i) knowledge concerning (a) the state of RE research involving Big Data applications, and (b) RE practices on real-world Big Data applications projects; (ii) a set of RE challenges in creating Big Data applications; (iii) a meta-model depicting the various RE artefacts and their inter-relationships in the context of Big Data software development projects; (iv) a goal-oriented approach (composed of a systematic process, requirements logging templates, checklists, and a requirements language) for modelling quality requirements for Big Data applications; and (v) a prototype tool that implements the proposed Big Data goal-oriented requirements language. These results lay a foundation in RE research involving Big Data applications development with anticipated impact in real-world projects and in RE research