Streaming Data Processing forIoT
| Autor: | Carolina Fortuna, Timotej Gale |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Data processing
business.industry Computer science Relational database Distributed computing 020206 networking & telecommunications 02 engineering and technology Automatic summarization Visualization Stream processing Data visualization Data stream management system 020204 information systems 0202 electrical engineering electronic engineering information engineering business Data compression |
| Zdroj: | The Internet of Things |
| DOI: | 10.1002/9781119545293.ch5 |
| Popis: | The early Internet of Things stream processing platforms were mainly designed to collect and display real‐time raw sensor measurements. Data often has to go through several phases of processing to lead to actionable automatic or human decision making. This chapter discusses five main operations performed on streaming data: compression, dimensionality reduction, summarization, learning, and visualization. It distinguishes two main types of stream data processing systems. The first type, also called a data stream management system, is based on relational database principles and introduced the concept of continuous queries. The second type does not enforce a relational view and enables the creation of custom operators. Based on batch and stream processing paradigms, two data processing architectures emerged: the Lambda architecture, which enables both batch and stream processing, and the simpler Kappa architecture, which enables stream processing. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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