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Analytics for the Internet of Things（IoT）
最新章節：
Summary
Thisbooktargetsdevelopers,IoTprofessionals,andthoseinthefieldofdatasciencewhoaretryingtosolvebusinessproblemsthroughIoTdevicesandwouldliketoanalyzeIoTdata.IoTenthusiasts,managers,andentrepreneurswhowouldliketomakethemostofIoTwillfindthisequallyuseful.ApriorknowledgeofIoTwouldbehelpfulbutisnotnecessary.Somepriorprogrammingexperiencewouldbeuseful
最新章節
- Summary
- A sample project
- Building revenue from IoT analytics
- IoT data science
- IoT exploratory analytics
- The IoT data flow
品牌：中圖公司
上架時間：2021-07-02 18:17:51
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Summary 更新時間：2021-07-02 19:00:35
- A sample project
- Building revenue from IoT analytics
- IoT data science
- IoT exploratory analytics
- The IoT data flow
- Review
- Bringing It All Together
- Summary
- An example of making a value decision
- The value formula
- Situation
- The economics of predictive maintenance example
- Thinking about revenue opportunities
- Expected usage considerations
- Cloud services costs
- Cost considerations for IoT analytics
- Support
- Scale
- Intellectual property considerations
- Open source economics
- Monitoring cloud billing closely
- Cloud costs can escalate quickly
- The option to quit
- Variable versus fixed costs
- The economics of cloud computing and open source
- The Economics of IoT Analytics
- Summary
- The retention strategy example
- Reduce the number of records
- Reducing the number of fields
- Reducing accessibility
- Retention strategies for IoT data
- Goals
- The data retention strategy
- Developing a progression process
- Data refineries
- When data lakes turn into data swamps
- Managing data lakes
- Linking together datasets
- Building analytic datasets
- Analytical datasets
- Linked Analytical Datasets
- Strategies to Organize Data for Analytics
- Summary
- Setting up TensorFlow on AWS
- A Nickel Tour of deep learning
- Use cases for deep learning with IoT data
- Deep learning
- Using R to forecast time series IoT data
- Forecasting using ARIMA
- Anomaly detection using R
- Ensemble
- The Gradient Boosting Machines R example
- GBM key concepts
- Gradient Boosting Machines (GBM) using R
- Random forest R examples
- Random forest key concepts
- Random forest models using R
- Area Under the Curve (AUC)
- ROC curves
- Comparing different models to find the best fit using R
- Trade-off and complexity
- Variance
- Bias
- Understanding the bias–variance tradeoff
- Precision recall and specificity
- Test set
- Cross-validation
- Validation methods
- Time series handling
- Centering and scaling
- Dealing with missing values
- Feature engineering with IoT data
- Generalization
- Optimization
- Evaluation
- Representation
- What is machine learning?
- Machine learning (ML)
- Data Science for IoT Analytics
- Summary
- Solving the pollution reporting problem
- Geospatial analysis in the big data world
- PostGIS spatial functions
- ogr2ogr
- QGIS
- ArcGIS
- Geospatial analysis software
- Processing geospatial data
- R-tree
- Spatial indexing
- Storing geospatial data in HDFS
- Spatial extensions for relational databases
- File formats
- Storing geospatial data
- Raster-based methods
- Vector summary
- Simplify
- Dilation and erosion
- Buffer
- Contains
- The bounding box
- Vector-based methods
- The Earth is not a ball
- Coordinate Reference Systems
- Welcome to Null Island
- The basics of geospatial analysis
- Why do you need geospatial analytics for IoT?
- Applying Geospatial Analytics to IoT Data
- Summary
- Organizing alerts using a Tableau dashboard
- Alert principles
- Creating and visualizing alerts
- Assembling views into a dashboard
- Creating individual views
- Aligning visuals to the thought process
- Hierarchy of Questions example
- The dashboard walk-through
- Creating a dashboard with Tableau
- Make charts easy to interpret
- Be consistent across visuals
- The impact of using a single color to communicate importance
- Use color to highlight important data
- Using layout positioning to convey importance
- Designing visual analysis for IoT data
- Aligning views with question flows
- Pulling together the data
- Developing question trees
- The Hierarchy of Questions method overview
- The Hierarchy of Questions method
- Common mistakes when designing visuals
- Communicating with Others - Visualization and Dashboarding
- Summary
- Federal Reserve Economic Data (FRED)
- Organization for Economic Cooperation and Development (OECD)
- External datasets - economic
- CIA World Factbook
- The U.S. Census Bureau
- External datasets - demographic
- USGS national transportation datasets
- Google Maps API
- Planet.osm
- Geographical features
- Weather
- National Elevation Dataset (NED)
- SRTM elevation
- Elevation
- External datasets - geography
- Adding external datasets
- Financial
- Field services
- Production data
- Customer information
- Which ones and why?
- Adding internal datasets
- Decorating Your Data - Adding External Datasets to Innovate
- Summary
- Retail
- Healthcare
- Manufacturing
- Solving industry-specific analysis problems
- Summing it all up
- Using R for statistical analysis
- Installing R and RStudio
- R (the pirate's language...if he was a statistician)
- Look for attributes that might have predictive value
- Bring in geography
- Get to know categories in the data
- Applying time series analysis
- What is meant by time series?
- Basic time series analysis
- Representativeness
- Assessing Information Lag
- Data validity
- Data completeness
- Look at your data - au naturel
- Techniques to understand data quality
- The Tableau overview
- Exploring and visualizing data
- Getting to Know Your Data - Exploring IoT Data
- Summary
- Handling change
- Lambda architectures
- To stream or not to stream
- Using Spark for IoT data processing
- Thinking about a single machine versus a cluster of machines
- Spark and big data analytics
- What is Apache Spark?
- Apache Spark for data processing
- Amazon S3
- Amazon DynamoDB
- HBase
- Yet Another Resource Negotiator (YARN)
- Hadoop MapReduce
- Serialization/Deserialization (SerDe)
- Hive
- Avro
- Parquet
- Hadoop Distributed File System
- Node types
- What is a Node?
- Hadoop cluster architectures
- Hadoop
- Applying big data technology to storage
- Microsoft Azure IoT Hub
- The AWS IoT platform
- AWS Athena
- AWS Lambda
- Amazon Kinesis
- Designing data processing for analytics
- Collecting All That Data - Strategies and Techniques
- Summary
- How to terminate and clean up the environment
- The VPC Creation walk-through
- Your VPC architecture
- What is a bastion host?
- What is a NAT gateway?
- Creating a VPC for IoT Analytics
- Creating an S3 bucket to store data
- Creating a key pair for the NAT and bastion instances
- The AWS Virtual Private Cloud (VPC) setup walk-through
- The AWS CloudFormation overview
- Creating an AWS Cloud Analytics Environment
- Summary
- Entities
- Data shapes
- Thing shapes
- Events
- Services
- Properties
- Things
- Thing templates
- ThingWorx concepts
- ThingWorx Edge
- ThingWorx Connection Services
- ThingWorx Core
- The ThingWorx overview
- The R server option
- HDInsight
- Azure Analysis Services
- Azure Data Lake Store
- Microsoft Azure overview
- Amazon Redshift
- Amazon Relational Database Service (RDS)
- AWS machine learning
- Amazon Elastic Map Reduce (EMR)
- Amazon Simple Queue Service (SQS)
- AWS key services for IoT analytics
- Simple Storage Service (S3)
- Elastic Compute (EC2)
- Identity and Access Management (IAM)
- Virtual Private Cloud (VPC)
- AWS key core services
- Security groups
- Subnet
- Availability Zones
- Regions
- AWS key concepts
- The AWS overview
- Securing customer data
- Access restrictions
- Public versus private subnets
- Public/private keys
- Cloud security and analytics
- Use Application Programming Interfaces (API)
- Leverage managed services
- Assuming that change is constant
- When to use distributed and when to use one server
- Avoid containing analytics to one server
- Distributed computing
- Decoupling with message queues
- Encapsulate analytics
- Decouple key components
- Designing for scale
- Design with the endgame in mind
- Elastic analytics concepts
- What is cloud infrastructure?
- Building elastic analytics
- IoT Analytics for the Cloud
- Summary
- Analyzing data to infer protocol and device characteristics
- Common use cases
- Data Distribution Service (DDS)
- Common use cases
- Message reliability
- Disadvantages to CoAP
- Advantages to CoAP
- Constrained Application Protocol (CoAP)
- Disadvantages to HTTP
- Advantages to HTTP
- HTTP and IoT
- Representational State Transfer (REST) principles
- Hyper-Text Transport Protocol (HTTP)
- Common use cases
- Tips for analytics
- Last Will and Testament (LWT)
- QoS 2
- QoS 1
- QoS 0
- QoS levels
- Disadvantages to MQTT
- Advantages to MQTT
- Topics
- Message Queue Telemetry Transport (MQTT)
- IoT networking data messaging protocols
- Common use cases
- Cellular (4G/LTE)
- Common use cases
- Wi-Fi
- Connectivity protocols (when power is not a problem)
- Sigfox
- Common use cases
- NFC
- Common use cases
- Disadvantages of ZigBee
- Advantages of ZigBee
- ZigBee
- 6LoWPAN
- Bluetooth Low Energy (also called Bluetooth Smart)
- Connectivity protocols (when the available power is limited)
- IoT networking connectivity protocols
- Networking basics
- Sensor types
- Wearables
- Home automation or monitoring
- Oil and gas
- Retail
- Transportation and logistics
- Manufacturing
- Healthcare
- The wild world of IoT devices
- IoT devices
- IoT Devices and Networking Protocols
- Summary
- Business value concerns
- Analytics challenges
- Data quality
- Problems with space
- Problems with time
- The data volume
- IoT analytics challenges
- The concept of constrained
- Defining the Internet of Things
- Defining analytics
- Defining IoT analytics
- The situation
- Defining IoT Analytics and Challenges
- Questions
- Piracy
- Errata
- Downloading the color images of this book
- Downloading the example code
- Customer support
- Readers feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- why subscribe
- www.PacktPub.com
- About the Reviewer
- About the Author
- Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- why subscribe
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Readers feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- Defining IoT Analytics and Challenges
- The situation
- Defining IoT analytics
- Defining analytics
- Defining the Internet of Things
- The concept of constrained
- IoT analytics challenges
- The data volume
- Problems with time
- Problems with space
- Data quality
- Analytics challenges
- Business value concerns
- Summary
- IoT Devices and Networking Protocols
- IoT devices
- The wild world of IoT devices
- Healthcare
- Manufacturing
- Transportation and logistics
- Retail
- Oil and gas
- Home automation or monitoring
- Wearables
- Sensor types
- Networking basics
- IoT networking connectivity protocols
- Connectivity protocols (when the available power is limited)
- Bluetooth Low Energy (also called Bluetooth Smart)
- 6LoWPAN
- ZigBee
- Advantages of ZigBee
- Disadvantages of ZigBee
- Common use cases
- NFC
- Common use cases
- Sigfox
- Connectivity protocols (when power is not a problem)
- Wi-Fi
- Common use cases
- Cellular (4G/LTE)
- Common use cases
- IoT networking data messaging protocols
- Message Queue Telemetry Transport (MQTT)
- Topics
- Advantages to MQTT
- Disadvantages to MQTT
- QoS levels
- QoS 0
- QoS 1
- QoS 2
- Last Will and Testament (LWT)
- Tips for analytics
- Common use cases
- Hyper-Text Transport Protocol (HTTP)
- Representational State Transfer (REST) principles
- HTTP and IoT
- Advantages to HTTP
- Disadvantages to HTTP
- Constrained Application Protocol (CoAP)
- Advantages to CoAP
- Disadvantages to CoAP
- Message reliability
- Common use cases
- Data Distribution Service (DDS)
- Common use cases
- Analyzing data to infer protocol and device characteristics
- Summary
- IoT Analytics for the Cloud
- Building elastic analytics
- What is cloud infrastructure?
- Elastic analytics concepts
- Design with the endgame in mind
- Designing for scale
- Decouple key components
- Encapsulate analytics
- Decoupling with message queues
- Distributed computing
- Avoid containing analytics to one server
- When to use distributed and when to use one server
- Assuming that change is constant
- Leverage managed services
- Use Application Programming Interfaces (API)
- Cloud security and analytics
- Public/private keys
- Public versus private subnets
- Access restrictions
- Securing customer data
- The AWS overview
- AWS key concepts
- Regions
- Availability Zones
- Subnet
- Security groups
- AWS key core services
- Virtual Private Cloud (VPC)
- Identity and Access Management (IAM)
- Elastic Compute (EC2)
- Simple Storage Service (S3)
- AWS key services for IoT analytics
- Amazon Simple Queue Service (SQS)
- Amazon Elastic Map Reduce (EMR)
- AWS machine learning
- Amazon Relational Database Service (RDS)
- Amazon Redshift
- Microsoft Azure overview
- Azure Data Lake Store
- Azure Analysis Services
- HDInsight
- The R server option
- The ThingWorx overview
- ThingWorx Core
- ThingWorx Connection Services
- ThingWorx Edge
- ThingWorx concepts
- Thing templates
- Things
- Properties
- Services
- Events
- Thing shapes
- Data shapes
- Entities
- Summary
- Creating an AWS Cloud Analytics Environment
- The AWS CloudFormation overview
- The AWS Virtual Private Cloud (VPC) setup walk-through
- Creating a key pair for the NAT and bastion instances
- Creating an S3 bucket to store data
- Creating a VPC for IoT Analytics
- What is a NAT gateway?
- What is a bastion host?
- Your VPC architecture
- The VPC Creation walk-through
- How to terminate and clean up the environment
- Summary
- Collecting All That Data - Strategies and Techniques
- Designing data processing for analytics
- Amazon Kinesis
- AWS Lambda
- AWS Athena
- The AWS IoT platform
- Microsoft Azure IoT Hub
- Applying big data technology to storage
- Hadoop
- Hadoop cluster architectures
- What is a Node?
- Node types
- Hadoop Distributed File System
- Parquet
- Avro
- Hive
- Serialization/Deserialization (SerDe)
- Hadoop MapReduce
- Yet Another Resource Negotiator (YARN)
- HBase
- Amazon DynamoDB
- Amazon S3
- Apache Spark for data processing
- What is Apache Spark?
- Spark and big data analytics
- Thinking about a single machine versus a cluster of machines
- Using Spark for IoT data processing
- To stream or not to stream
- Lambda architectures
- Handling change
- Summary
- Getting to Know Your Data - Exploring IoT Data
- Exploring and visualizing data
- The Tableau overview
- Techniques to understand data quality
- Look at your data - au naturel
- Data completeness
- Data validity
- Assessing Information Lag
- Representativeness
- Basic time series analysis
- What is meant by time series?
- Applying time series analysis
- Get to know categories in the data
- Bring in geography
- Look for attributes that might have predictive value
- R (the pirate's language...if he was a statistician)
- Installing R and RStudio
- Using R for statistical analysis
- Summing it all up
- Solving industry-specific analysis problems
- Manufacturing
- Healthcare
- Retail
- Summary
- Decorating Your Data - Adding External Datasets to Innovate
- Adding internal datasets
- Which ones and why?
- Customer information
- Production data
- Field services
- Financial
- Adding external datasets
- External datasets - geography
- Elevation
- SRTM elevation
- National Elevation Dataset (NED)
- Weather
- Geographical features
- Planet.osm
- Google Maps API
- USGS national transportation datasets
- External datasets - demographic
- The U.S. Census Bureau
- CIA World Factbook
- External datasets - economic
- Organization for Economic Cooperation and Development (OECD)
- Federal Reserve Economic Data (FRED)
- Summary
- Communicating with Others - Visualization and Dashboarding
- Common mistakes when designing visuals
- The Hierarchy of Questions method
- The Hierarchy of Questions method overview
- Developing question trees
- Pulling together the data
- Aligning views with question flows
- Designing visual analysis for IoT data
- Using layout positioning to convey importance
- Use color to highlight important data
- The impact of using a single color to communicate importance
- Be consistent across visuals
- Make charts easy to interpret
- Creating a dashboard with Tableau
- The dashboard walk-through
- Hierarchy of Questions example
- Aligning visuals to the thought process
- Creating individual views
- Assembling views into a dashboard
- Creating and visualizing alerts
- Alert principles
- Organizing alerts using a Tableau dashboard
- Summary
- Applying Geospatial Analytics to IoT Data
- Why do you need geospatial analytics for IoT?
- The basics of geospatial analysis
- Welcome to Null Island
- Coordinate Reference Systems
- The Earth is not a ball
- Vector-based methods
- The bounding box
- Contains
- Buffer
- Dilation and erosion
- Simplify
- Vector summary
- Raster-based methods
- Storing geospatial data
- File formats
- Spatial extensions for relational databases
- Storing geospatial data in HDFS
- Spatial indexing
- R-tree
- Processing geospatial data
- Geospatial analysis software
- ArcGIS
- QGIS
- ogr2ogr
- PostGIS spatial functions
- Geospatial analysis in the big data world
- Solving the pollution reporting problem
- Summary
- Data Science for IoT Analytics
- Machine learning (ML)
- What is machine learning?
- Representation
- Evaluation
- Optimization
- Generalization
- Feature engineering with IoT data
- Dealing with missing values
- Centering and scaling
- Time series handling
- Validation methods
- Cross-validation
- Test set
- Precision recall and specificity
- Understanding the bias–variance tradeoff
- Bias
- Variance
- Trade-off and complexity
- Comparing different models to find the best fit using R
- ROC curves
- Area Under the Curve (AUC)
- Random forest models using R
- Random forest key concepts
- Random forest R examples
- Gradient Boosting Machines (GBM) using R
- GBM key concepts
- The Gradient Boosting Machines R example
- Ensemble
- Anomaly detection using R
- Forecasting using ARIMA
- Using R to forecast time series IoT data
- Deep learning
- Use cases for deep learning with IoT data
- A Nickel Tour of deep learning
- Setting up TensorFlow on AWS
- Summary
- Strategies to Organize Data for Analytics
- Linked Analytical Datasets
- Analytical datasets
- Building analytic datasets
- Linking together datasets
- Managing data lakes
- When data lakes turn into data swamps
- Data refineries
- Developing a progression process
- The data retention strategy
- Goals
- Retention strategies for IoT data
- Reducing accessibility
- Reducing the number of fields
- Reduce the number of records
- The retention strategy example
- Summary
- The Economics of IoT Analytics
- The economics of cloud computing and open source
- Variable versus fixed costs
- The option to quit
- Cloud costs can escalate quickly
- Monitoring cloud billing closely
- Open source economics
- Intellectual property considerations
- Scale
- Support
- Cost considerations for IoT analytics
- Cloud services costs
- Expected usage considerations
- Thinking about revenue opportunities
- The economics of predictive maintenance example
- Situation
- The value formula
- An example of making a value decision
- Summary
- Bringing It All Together
- Review
- The IoT data flow
- IoT exploratory analytics
- IoT data science
- Building revenue from IoT analytics
- A sample project
- Summary 更新時間：2021-07-02 19:00:35
