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.
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:
IIoT Cloud Platforms
Network Infrastructure & Sensors
Big Data Learning
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.
2016 is the year that we’ve finally entered the era of the Internet of Things (IoT). Since the beginning of this year, I’ve seen and heard more and more customers and industry leaders discuss technologies that can store, process, and analyze large amounts of real-time streaming data from sensors and IoT devices.
Organizations within the Industrial IoT especially are seeking new IoT technologies to solve their technical challenges and add significant business value. Industries, such as manufacturing, logistics, telecommunications, and oil and gas, have been successfully building IoT applications for configuration management, predictive maintenance, supply chain optimization, and many other critical use cases.
Human Intelligence (HUMINT) consists of a huge graph of connected snippets of information about criminals and terrorists, plus analyst reports and a wealth of background information. In this example, we will deal with data that is primarily about telephone metadata, which includes Call Detail Records and the people involved in the calls.
We will look for suspicions patterns of calls, and, if we find any, we will try to determine whether any of the people involved has been seen sighted near a potential target, such as an important government facility.
It’s no secret that the Oil and Gas industry is cautious and calculated when adopting new technology. There are more than a few reasons for this, but the most fundamental in my opinion are the absolute requirement for safe operation combined with the sheer amount of inertia, in terms of investment, in existing technologies. It’s the analog of turning an oil tanker – it’s going to take a while.
That said, when there is uptake of a particular set of technologies, the consumption and demand comes at astounding pace and scales. Without a question, Oil and Gas is one of the largest industries, if not the largest industry globally. According to Wikipedia, six out of ten companies with the highest revenue in the world are Oil and Gas companies. Profit percentage, on the other hand, especially in the current low-price oil environment, is another matter. The pressure from that aspect of the business is actively driving increased appetite for new technologies to reduce costs.