物联网数据分析:智能设备的洞察力

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"Analytics for the Internet of Things (IoT) - Andrew Minteer" 本文将深入探讨物联网(IoT)中的数据分析,这是一个将智能设备与高级分析相结合的重要领域。物联网数据分析不仅涉及收集、处理和分析来自各种IoT设备的海量数据,还涉及到如何克服与之相关的挑战。 首先,"Defining IoT Analytics and Challenges"部分会阐述物联网分析的定义,包括它是如何在物联网生态系统中帮助提取价值的。这一部分可能会涵盖如何通过实时监控和预测性分析来优化运营,以及物联网数据分析面临的挑战,如数据安全、隐私问题和数据质量保证。 接下来,"IoT Devices and Networking Protocols"将详细介绍物联网设备的各种类型和它们使用的通信协议。这可能包括蓝牙、Wi-Fi、Zigbee、LoRaWAN等,以及如何选择合适的协议来确保高效、可靠的数据传输。 "IoT Analytics for the Cloud"章节会讨论如何利用云基础设施进行物联网数据分析。AWS(亚马逊网络服务)可能是此部分的重点,介绍如何创建一个云端分析环境,利用云平台如Amazon Elasticsearch Service、Kinesis Data Streams和Redshift进行大数据处理和存储。 "Creating an AWS Cloud Analytics Environment"将提供构建和配置AWS云环境的步骤,可能包括设置数据流、存储解决方案和分析工具的详细指南。这将帮助读者理解如何利用AWS服务来支持物联网数据分析的需求。 "Collecting All That Data - Strategies and Techniques"部分将探讨收集物联网数据的不同策略和方法。这可能包括边缘计算,其中部分分析在设备本地完成,以减少云端的负担,以及如何有效地管理设备到云端的数据传输。 此外,书中还可能涉及数据预处理、清洗和集成,以及利用机器学习和人工智能技术对物联网数据进行深度分析,以实现自动化决策和智能预测。最后,可能会讨论最佳实践和案例研究,以展示物联网数据分析在实际应用中的效果,如智慧城市、工业4.0和智能家居等场景。 这本书《Analytics for the Internet of Things》旨在为读者提供全面的物联网数据分析知识,帮助他们理解和掌握如何利用数据分析技术驱动智能设备的性能优化和业务创新。
2017-08-17 上传
Intelligent Analytics for your Intelligent devices 针对智能设备的数据智能分析 Book Description Break through the hype and learn how to extract actionable intelligence from the flood of IoT data About This Book Make better business decisions and acquire greater control of your IoT infrastructure Learn techniques to solve unique problems associated with IoT and examine and analyze data from your IoT devices Uncover the business potential generated by data from IoT devices and bring down business costs Who This Book Is For This book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful What You Will Learn Overcome the challenges IoT data brings to analytics Understand the variety of transmission protocols for IoT along with their strengths and weaknesses Learn how data flows from the IoT device to the final data set Develop techniques to wring value from IoT data Apply geospatial analytics to IoT data Use machine learning as a predictive method on IoT data Implement best strategies to get the most from IoT analytics Master the economics of IoT analytics in order to optimize business value In Detail We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling. Style and approach This book follows a step-by-step, practical approach to combine the power of analytics and IoT and help you get results quickly Contents Chapter 1. Questions Chapter 2. Defining Iot Analytics And Challenges Chapter 3. Iot Devices And Networking Protocols Chapter 4. Iot Analytics For The Cloud Chapter 5. Creating An Aws Cloud Analytics Environment Chapter 6. Collecting All That Data – Strategies And Techniques Chapter 7. Getting To Know Your Data – Exploring Iot Data Chapter 8. Decorating Your Data – Adding External Datasets To Innovate Chapter 9. Communicating With Others – Visualization And Dashboarding Chapter 10. Applying Geospatial Analytics To Iot Data Chapter 11. Data Science For Iot Analytics Chapter 12. Strategies To Organize Data For Analytics Chapter 13. The Economics Of Iot Analytics Chapter 14. Bringing It All Together