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:

IIoT Cloud Platforms
Network Infrastructure & Sensors
Configuration Management
IIoT Cybersecurity
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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Optimize your Infrastructure within the Internet of Things

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.

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Using Spark and ThingSpan for Intelligence Analytics

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.

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Takeaways from Strata + Hadoop World, and Our Upcoming Webinar for Financial Services

Last week’s Strata + Hadoop World conference in San Jose, Calif., ended on a perfect note! Over the years the event has evolved from merely discussing the power of Big Data analytics to actually implementing emerging technologies to discover relationships within that data.

After gathering my notes from the conversations that I had with the attendees who visited our booth, I can sum up my Strata experience with these two takeaways:

Graph databases are the key to extracting more value from Big Data
A lack of scalability is the primary limitation of other graph technologies

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