Implementing Smart Processes using IoT, Machine Learning, and Business Rules

Implementing Smart Processes using IoT, Machine Learning, and Business Rules

This unique book proposes a systematic methodology spanning the full lifecycle of such so-called smart process applications (SPAs) and illustrates it with two realistic case studies. SPAs are fundamentally different from traditional transaction-driven IT applications, from functional and architectural requirements points of......
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Implementing Smart Processes using IoT, Machine Learning, and Business Rules
Implementing Smart Processes using IoT, Machine Learning, and Business Rules
649,-

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<p><span>This unique book proposes a systematic methodology spanning the full lifecycle of such so-called smart process applications (SPAs) and illustrates it with two realistic case studies.</span></p> <p><span>SPAs are fundamentally different from traditional transaction-driven IT applications, from <em>functional </em>and <em>architectural requirements </em>points of view. They silently monitor independently running processes and only spring into action when they recognize some situations, to <em>suggest or execute actions</em> that will help the process under observation to achieve or prevent certain outcomes. With SPAs, the IoT-enabled context awareness, cloud-enabled distribution, and AI-enabled decision making—a combination of machine learning and business rules—are a matter of domain necessity and not mere technical wizardry. <span> </span>At the core of the proposed development methodology is a <em>requirements framework</em> that helps translate general domain requirements—staying healthy, preventing accidents, etc.—into precise functional and architectural requirements. </span></p> <p><strong><span>Topics and features:</span></strong></p> <p><!-- [if !supportLists]--><span><span>·<span>       </span></span></span><!--[endif]--><span>An integrated development methodology that spans the entire lifecycle of SPAs from domain requirements to architecture, detailed design, coding, testing, and maintenance</span></p> <p><!-- [if !supportLists]--><span><span>·<span>       </span></span></span><!--[endif]--><span>A pragmatic approach to hybrid AI, advocating the use of machine learning or business rules depending on the task at hand, the state of knowledge, and the availability of historical data</span></p> <p><!-- [if !supportLists]--><span><span>·<span>       </span></span></span><!--[endif]--><span>A combination of state-of-the-art developments with lessons learned from dozens of consulting engagements in industry</span></p> <p><!-- [if !supportLists]--><span><span>·<span>       </span></span></span><!--[endif]--><span>Two running case studies help explore the design space for the component technologies, taken separately, and in an integrated fashion</span></p> <p><!-- [if !supportLists]--><span><span>·<span>       </span></span></span><!--[endif]--><span>A companion <em>evolving </em>GitHub repository that hosts various development deliverables for the case studies</span></p> <p><span>Practicing software engineers who work with the domain and architectural dimensions of SPAs will find food for thought and ready-to-use generic, and technology-specific, design patterns. Academic researchers interested in either method engineering or in developing domain-specific SPAs will find great value in this book.</span></p> <p><strong><span>Hafedh Mili</span></strong><span> is full professor of Computer Science at the Université du Québec à Montréal, Canada. <strong>Ghizlane El Boussaidi</strong> is a full professor affiliated with the Dept. of Software and IT engineering at École de Technologie Supérieure, Canada.</span></p> <p><span> </span></p>

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