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ThingSpan Performance Blog Series – Part I

Graph Analytics with Billions of Daily Financial Transaction Events   Introduction In this first blog of the series we will explain how ThingSpan can deliver solutions to meet the demanding needs of today’s complex analytics systems by combining big and fast data...

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In the beginning

In the beginning there was data. Then Codd (and Date) created relational database systems, and then there was structured query language (SQL). SQL was good for queries by values of data, and queries where you knew what you were looking for. You could answer the known questions. Data was neatly organized into rows (records) and columns (fields) of tables. You could even query across tables using “joins” if you knew what to join.

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How Smart Are Your Connected Devices? Using Spark and ThingSpan to Provide IIoT Predictive Analytics for Smart Homes.

The Industrial Internet of Things covers a very wide range of devices and systems that interact with one another or dedicated services over the Internet. Although such systems have been deployed by specialist companies, such as building control system suppliers, there has been a recent upsurge in interest in developing unified protocols and standards for IIoT infrastructure. IIoT covers a wide range of disciplines, but they can be grouped as follows:

Infrastructure:
IIoT Cloud Platforms
Network Infrastructure & Sensors
Configuration Management
IIoT Cybersecurity
Techniques:
Big Data Learning
Machine Analytics
Application Sectors:
Manufacturing & Supply Chain
Extraction & Heavy Industry
Utilities and Smart Grid/City/Home
Transportation & Fleet.

The infrastructure and techniques share a lot in common with the consumer/retail IoT domain, so in this first look at applying Spark and ThingSpan in IIoT applications we will look at a simple Smart Home application as the techniques employed are applicable to both domains.

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