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Software Engineering and Data Science
Davide Tosi (editor)
2023
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This reprint focuses on data-driven software solutions and their impact on research and development at the academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future. Data-driven software solutions are different from “traditional” software development projects, as the focus of the main development core is on managing the data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life cycles, algorithms, methods, processes, and tools are required. This reprint is centered on the recent trends and advancements in the field of engineering data-intensive software solutions to address the challenges in developing, testing, and maintaining such data-driven systems, with a focus on the application of data-driven solutions to real-life problems and techniques and algorithms addressing the different challenges of data-driven software engineering.
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Keywords
- Bayesian regression
- big data
- changepoint detection
- COVID-19
- Data Analytics
- delay-tolerant network (DTN)
- dependency graphs
- Economics, finance, business & management
- Energy consumption
- European football championship
- feature selection
- Google mobility impact
- graph filters
- Industry & industrial studies
- information system development
- Information technology industries
- link prediction
- machine-learning
- Media, information & communication industries
- multi-attribute decision making
- n/a
- Ontology
- operations
- Public transport
- SARS-CoV-2
- schools’ impact
- software development process
- Software engineering
- Software Library Recommendation
- team structure
- text classification
Links
DOI: 10.3390/books978-3-0365-6441-8Editions