Distributed Online Learning and Stream Processing for a Smarter Planet

Abstract

Smarter Planet applications for transportation, healthcare, energy and utilities have been enabled by the confluence of technological advancements in sensing, communication, and compute systems, and analysis algorithms. These applications have have several novel characteristics in terms of their distributed nature, their analysis, security and privacy needs, their performance scaling needs, and their dynamics -that have not been sufficiently addressed by prior centrallystore-process-and-analyze systems and algorithms.In this chapter we describe the emerging field of Stream Processing systems and its impact on the realization of these applications -and discuss how this is enabling fog computing in real-world implementations. We introduce the paradigm, its core constructs and capabilities, and examples of academic, commercial and open-source systems available for use. We also highlight examples of large-scale distributed applications built on these systems, and in use in current Smarter Planet applications. We then discuss how these systems enable several new directions

Knowledge Integration in Smart Factories

“…Training models may be created and trained at different computing points, e.g., edge nodes [16]. Turaga and van der Schaar (2017) summarize different forms of data streaming analytics features, such as Streaming and In-Motion Analysis for on the fly analytics, Distributed Data Analysis, High-Performance and Scalable Analysis, Multi-Modal Analysis, Loss-Tolerant Analysis or Adaptive and Time-varying Analysis [40]. Stream analytics require local processing units, which is why the concepts of fog or edge computing are prevalent for this type of processing.…”