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The Emphasis is on Out-of-the-Box Internet of Things Data Management

By   /  January 18, 2017  /  No Comments

Enterprises face growing Data Management challenges as the industrial Internet of Things (IoT) becomes an increasing part of their daily activities.

The difficulties for companies in sectors like manufacturing and energy start with a slew of Internet of Things devices, producing and sending information in realtime, whose sensor data must often be combined with other data. Such difficulties extend to not having a standard software solution to address each business’ unique use case and to the fact that a company’s talented engineers aren’t necessarily experts at software development and Data Analytics. The result is that they’re not wringing all the potential they can from the IoT.

“IoT is so broad, and it’s such a fragmented market” says Andreas Eberhart, Managing Director at Semantic Technologies vendor fluidOps. “If you go to IoT conferences you’ll see so many use cases but they are all different.”

fluidOps’ Information Workbench, an integration platform for creating Smart Data with Semantic context and links, can help industries sort through many of the Internet of Things complexities with which they’re struggling. As Eberhart explains it, fluidOps’ Information Workbench functionality includes the ability to leverage Linked Data to deal with the IoT issues around data variety and veracity.

“Big Data stacks and Data Lakes are great IoT assets but certainly they are not the be-all and end-all of IoT,” he says. “Using Linked Data makes it possible to address all the aspects of the IoT data challenge.”

Highly-fragmented, high-variety types of data is where Linked Data shines, he notes, and such is the case with IoT information that ranges from machine Master Data to associated service contracts to where sensors are relative to each other. The inner workings of the Information Workbench engine, which has as its underlying data model a directed labeled graph, serves the purpose of gluing all that data together like a mashup, he says, so to the user it appears as one system. Graphs also allow keeping all the graph edges which come from one source within context, so that it’s possible to attach data provenance Metadata to that context and address the veracity aspect.

Next-Gen IoT Data Management

Applications of fluidOps’ Information Workbench include the latest version of IoT Manager, which extends the platform by providing Data Management technologies for the integration, analysis, visualization, and plausibility of Master, streaming and Big Data that are part and parcel of IoT projects. It also leverages fluidOps’ data center management heritage and translates that to the Internet of Things experience, making it possible to use standardized features to support individual IoT use cases.

IoT Manager’s upcoming 8.0 release, due in January, covers the ability to interlink all varieties of IoT data in the Cloud. Stream and Big Data systems and their sensor readings are attached to a core Linked Data graph of smart Master Data that captures IoT devices via sensor IDs, as well as core ontology concepts such as locations, customers, contracts, service levels, technicians, machine blueprints, and more. Smart and live streaming data are viewable via the same user interface and dashboards for a sensor’s page. “We don’t replace the Hadoops of this world, but just let them be connected to the Master Data world for use in their analysis,” he says.

So users can view integrated Internet of Things data and visualize key properties, including concepts and relationships. “To a user it’s one system and that’s great thing,” he says. Managing and controlling devices, customers, locations and contracts; complying with policies and enforcing service level agreements; as well as automating corrective actions and more are possible using IoT Manager.

The vendor has taken the opportunity with the new release to apply some of the similarities that exist between managing data center hardware and software and managing IoT devices. The concept, in principle, is the same: In the data center, APIs collect data from, say, VMware and integrate that into workloads that orchestrate processes. “IoT is doing the same thing, but just instead of using VMware maybe you’re collecting data from a car that you can tell to do things. We can leverage our expertise there,” Eberhart says.

Think of it this way:

“If I put my data center hat on, I’d say that what a company needs to deal with its IoT requirements about what it has, where it is, and what is its status is a CMDB (configuration management database),” Eberhart says. “That’s classical IT stuff.”

Providing enterprises standardized features to gain such insights into their individual IoT use cases prepares them to exploit their data synergies by using individually defined workflows within their IoT stacks, he says.

The goal, Eberhart says, is to emphasize agility, with a good portion of the capabilities needed for IoT Data Management available out of the box. That way, companies can make strong use of their engineering talent without having to dive into software development and Data Analytics implementations to realize IoT advantages.

Internet of Things Success

The company points as examples of its IoT success to work it’s done with industrial enterprises including Siemens, which captured gas turbine blueprints and equipment monitoring processes in an IoT app; the Norwegian oil giant StatOil, which uses standard energy ontologies for accessing data stored in hundreds of relational database tables; and, a German provider of energy management systems that leverages its technology to analyze data from thousands of remote sensors in order to optimize the connected appliances’ energy consumption. “They ask questions of the sensor data like, ‘Are lights still on outside even though the sun is already shining,’” Eberhart says.

Recently FluidOps also received recognition from Experton Group AG as a Rising Star in the Industrial Big Data Analytics category in its independent study, “Industry 4.0/IoT Vendor Benchmark 2017.” The category evaluates analytic solutions and databases for their ability to process data from industrial manufacturing machines and plants as well as analyze huge amounts of complex, unstructured data.  In a press release, Experton said that the vendor’s Semantic IT approach “has helped it secure a very good evaluation among the newcomers,” while Eberhart pointed to Information Workbench and its Smart Data platform, along with the IoT Manager App, as covering the bulk of all use cases in this field.

“Thanks to our high-performance, flexible technologies, our clients can quickly make customizations at any time,” he is quoted as saying in the release.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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