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Gone all-in on edge


The survey indicates respondents look forward to a f there is cloud, there must be edge. Othercomputing environment that blends traditional closedwise use of the industrial Internet of things is loop control with IT-world technologies like virtualization limited to cases where latency and bandwidth and data-hungry applications making use of analytics. constraints don’t come into play. “The traditional DCS — or PLC with SCADA and histoEdge computing can be described as a mesh network rian — is becoming obsolete fast…. Instead, plant devices of micro-data centers that process or store critical data will be provisioned and maintained centrally. We engineer locally while also working in concert with a central data it in the cloud, register the edge device, bind them tocenter or cloud-storage repository. A sophisticated gether and deploy,” said Tim Sowell, global market of single-use and multidirector, digital portfolio strategy, purpose devices for industrial edge Aveva, a Stratus partner. computing has developed rapidly. It’s going to happen Historically speaking, Maynard, Mass.-based Stratus Technologies’ Centralized skills sharing because the economic products often were compared to large It’s the ability to provision and enterprise servers found in data cenmaintain devices centrally that ters, but its latest solution, ztC Edge, should allow companies to move value is so compelling it is better described as a virtualized, forward with edge computing, deautomated platform for industrial edge spite a perceived dearth of personcan’t be resisted. environments. nel with the requisite advanced skill “Our customers today are in an intersets. esting space,” said Jason Andersen, VP While 55% of survey respondents of business line management, Stratus Technologies. “They believe control, process or automation engineering are trying to build towards the future while selectively expertise are core to edge computing success, near 30% maintaining their core operational applications. This means to 40% believe skills related to systems architectures, that they need a different kind of platform that can collect computer networking, cloud computing, data-base data analytics as well as manage current or legacy automa- security, data engineering, data science and application tion technology. ztC is effectively the bridge that translates development are also needed. their existing technologies into the world of cloud service Whether purposed to upgrade SCADA deployments providers like Amazon or Microsoft.” or in the application of statistics-based analytics, edge computing may be the most significant plant-floor advancement in industrial computing since the introduction Quantification as justification of SCADA 30 years ago. In fact, 43% of respondents to Stratus recently released results of a survey of engineers the Stratus survey see edge computing as “a great leap and others involved in edge connectivity, process control forward for process and production environments.” and operational computing environments. The result will be disruption of the industrial computThe edge computing trend report for North America ing space, said Dave Laurello, Stratus CEO, “by generalfound 46% of survey respondents agree that lack of purpose computers in the virtual mode, virtual PCs. education on if, when and how to use edge technology and applications is the foremost barrier to edge-computing It’s going to happen because the economic value is so deployment. At the same time, 53% of survey respondents compelling that it can’t be resisted.” say their companies are actively evaluating or planning These computational resources at the edge can filter edge computing implementations. Meanwhile, roughly or process data so that only what’s needed is transmitone-third still need a plan. ted to enterprise systems in the cloud. IIoT

Industrial Internet of Things


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How information ascends to the cloud for greater utilization Modern instrumentation delivers data, but too often it serves no purpose By Ryan Williams


mart instruments have been available since the mid-1980s when first 4-20mA HART devices entered the market, quickly followed by fieldbusbased devices. These digital communication technologies made it possible for instruments to provide more than just a process signal. Using digital interfaces, these devices were now able to send device status, diagnostics and other information. Endress+Hauser estimates that of the 40 million of its process instruments installed worldwide, 90% are digital, smart devices. These smart instruments provide an incredible amount of information at “the edge” that is of immense benefit to a wide range of host systems and IIoT applications, such as maintenance management, asset management, inventory control, and so forth. But one major problem facing industrial plants is: How do we manage all this data? If a single smart instrument, such as a Coriolis meter, delivers a few dozen items of status and diagnostic information, and a plant has several thousand instruments (Figure 1), the host systems must deal with huge amounts of data derived in real time. Because of the immense amount of data, and problems in managing it, Endress+Hauser estimates that 97% of the data is not being used. Instead,




IIoT For Engineers

automation systems use the flow, pressure, temperature, level and other data needed to control the process, and ignore or discard status, diagnostic and other data. Major instrument manufacturers are well aware of the problem, and several are now delivering solutions

Networks can be unduly

burdened with data transmissions, historians can

become bloated...

to acquire data from the Edge and making it available to specialized IIoT software — all without affecting or involving the automation system. Let’s take a quick look at the concepts behind these solutions.

Handling massive data As noted above, a smart instrument generates a great deal of status, diagnostic and other information. An Endress+Hauser Proline flowmeter, for example, can detect entrained air, vibration (which could be caused by pump cavitation), coating, corrosion and inhomogeneous or unsuitable media. In fact, the flowmeter can detect 125 different problems. When

process conditions warrant a notification (Figure 2), the flowmeter generates an event message. While the automation system is mostly interested in flow values and alarms, IIoT software wants to know about the warnings shown in Figure 2, as well as diagnostics and other data. Many smart instruments can provide diagnostics to indicate problems with electronics or subcomponents. For example, Proline Coriolis flowmeters can monitor oscillation damping and frequency, temperature, signal asymmetry, exciter current, carrier pipe temperature, frequency fluctuation and other parameters. While every instrument manufacturer’s diagnostics differ, each typically monitors internal parameters, observes changes and diagnoses problems. Any further analysis must be done by IIoT maintenance software, which means status and diagnostic data needs to be transmitted to this software. In many cases, this is accomplished by the automation system, which periodically asks each instrument for the data, then stores it in an online database, such as a process historian. Maintenance management software accesses what it needs from the historian and performs its analysis. This type of solution presents problems. Networks can be unduly burdened with data transmissions, historians can become bloated, and there can be lags between data

From the edge to the cloud FIGURE 1: A process plant may have thousands of smart instruments, all providing status and diagnostic data needed by IIoT software. All graphics courtesy: Endress + Hauser

tion and its recognition by the IIoT software. Data is collected only periodically because the automation system can’t deal with the massive amount of status and diagnostic data from hundreds or thousands of instruments. The data is stored in a database, which must be accessed from the maintenance software, adding even more delays. A better solution is to provide all the data available at the edge to IIoT software via the cloud, thus bypassing the automation system completely.

Connecting at the edge The 30+ million digital instruments currently installed worldwide communicate with their automation systems via different interfaces, including Profibus, 4-20mA HART, WirelessHART, EtherNet/IP, and several others. However, many eventually connect to an Ethernet-based network, where the data can be acquired by a specialized “edge device.” The edge device is programmed to extract instrument data from the network and transmit it to IIoT software in the cloud. An edge device can also be installed on a smaller system, such as a pumping station, that may or may not be connected to a plant’s Ethernet network, or to instruments that are connected to an older, non-Ethernet system. In that case, each instrument is wired to a nearby “edge gateway” device which collects data from devices and transmits it to the cloud.

Figure 2: Typical errors generated by a flowmeter Error code



Alarm type


Process limit

Check process conditions



Partially filled pipe

Check for gas in process Adjust detection limits



Tubes not oscillating

Check input configuration Check external device or process conditions


Medium inhomogeneous

Check process condition Increase system pressure



Medium unsuitable

Check process conditions Check sensor



Oscillation damping too high

Check process conditions


Once the instruments are connected to an Ethernet-based network that is ready for IIoT connection, the appropriate edge device is selected. Endress+Hauser has multiple approaches to select the right edge device for the right quantity of instruments transmitting information to the cloud. At a site where there are hundreds of instruments, the edge device has high-speed data acquisition to push the information to the cloud. Conversely, instrument-based edge


devices are offered that run at basic speed, transmitting small amounts of information to the cloud. All data transmission is one way from the device to the cloud. Cybersecurity is deployed within data transmission, edge devices, and cloud services connectivity.

Living in a cloud Major instrument manufacturers provide software that uses data from the edge to diagnose problems, IIoT For Engineers


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schedule maintenance activities, analyze processes, predict problems and so on. For example, cloud software consists of several components: Instrument diagnostics: Software built into modern instruments monitors device status and process conditions, and provides data needed for further analysis. Endress+Hauser embeds “Heartbeat Technology” into its instruments to provide status and diagnostic information, and to perform vital functions such as condition monitoring and in-situ verification. During verification, the current conditions of various parameters are compared with their reference values, thereby determining the device status. The technology produces a “pass” or a “fail” statement based on the tests, which is performed by

traceable and redundant internal references. The individual tests and results are automatically recorded and used to print a verification report. Cloud connection: Software and hardware are needed to extract data from the plant’s Ethernet network or from individual devices and transmit it to the cloud-based software. At Endress+Hauser, this is accomplished with Netilion Connect, which consists of edge devices which acquire the data, a cloud platform that hosts the IIoT software and an application programmable interface (API). The API provides a way to connect cloud-to-cloud or cloud-to-apps in a simplified way. It enables customers to use IIoT in a simple and efficient way without the complexity of ITbased computer science.


Direct Field to Cloud Connection with the PFC Series Controllers • • • •

IIoT-ready with Sparkplug, native MQTT and TLS encryption Built-in VPN and Firewall for increased network security Run Docker Containers in parallel with PLC logic Interface with existing controls via onboard fieldbus gateways

Final words Modern instruments provide a wealth of information about their health and the process they’re monitoring, but few plants use all that data. Today, major instrument manufacturers are providing hardware and software solutions that bring all the data available at the edge to IIoT software for analysis and corrective actions. IIoT

Ryan Williams is the national product manager for services and solutions at Endress+Hauser.



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The ecosystem is based on an open source technology platform that is common in our industry and offers functions that include supporting analytics and providing maintenance documentation.

Microsoft Azure IBM BLUEMIX


IEEE 802.16s – A standard built SPECIFICALLY for Mission Critical Internet of Things (MC-IoT) communications. Summary: Most oil & gas companies do not have access to enough Radio Frequency (RF) spectrum to deploy standard technologies, such as LTE or IEEE 802.16, the two most common wireless technologies for their mission critical data communications networks. Standards such as LTE, were designed for the consumer industry, not mission critical industries meaning oil & gas companies are forced to install proprietary communications networks, putting them at risk if the manufacturer goes out of business or discontinues their product line.

Challenge: Most oil & gas companies do not have access to broadband spectrum that can support standard technologies such as LTE and IEEE 802.16 (WiMAX) leaving only proprietary solutions to deploy private communications networks for their mission critical communications networks.

Solution: A grass roots effort was formed to revise IEEE 802.16 to ďŹ t into smaller channel sizes – 100 kHz to 1.25 MHz providing a standard communications technology that can be used in spectrum bands oil & gas companies have access to.

Result: Critical industry Companies have successfully deployed communications networks for their mission critical data using the standard eliminating this risk.

In the fall of 2017, a narrower channel standard technology was ratiďŹ ed and published by the IEEE. IEEE 802.16s was a grass roots effort launched because mission critical entities were looking for a standard technology that could be used in the narrow channel bands they have access to. Public broadband wireless technologies are evolving towards higher speeds and smaller cell sizes and are focused on consumer applications. The public Internet of Things (IoT) services such as NB-LTE are being deployed with a focus on consumer market applications and are not well suited for mission critical IoT applications. The IEEE 802.16s standard is designed for the mission critical private broadband wireless market. It provides multimegabit throughput using relatively narrow channel sizes and long distances to minimize spectrum acquisition and network infrastructure cost.   sWWWONDASCOM


HMIs enable operations and maintenance collaboration Mobile interfaces facilitate convergence of operations and maintenance. By Rich Carpenter


n a luxury cruise liner or large naval vessel, the bridge is the place from where the officers and crew can command all operations. In a similar way, traditional manufacturing facilities have often relied on one or more main control rooms or consoles staffed by operators who monitor the status of all systems and issue commands as needed. Most production plants also have maintenance personnel available who can respond to problems in the field, take appropriate actions, and report results back to control room operations personnel. Coordinating actions between these two groups often presents problems and can introduce delays, but a solution is at hand, driven by need for plant performance improvements in environments energized by digitalization improvements. A more modern, effective and efficient approach is for operations and maintenance personnel to function in multiple roles with some overlapping responsibilities, sometimes acting as operators and other times as maintenance workers. Newer mobile human machine interface (HMI) technologies are enabling this transformation and providing innovative ways for operations and maintenance personnel to collaborate. This shift in operating and collaboration methods is facilitated by




IIoT For Engineers

mobile HMIs which have become convenient and functional enough to get workers the information they need when they are out where the action is — on the plant floor. The changing role of HMIs made possible by handheld devices is improving the operating efficiency for these companies by enabling and empowering collaboration between operations and maintenance personnel.


to resolve the problem. The operators remain at their station and may communicate with maintenance via radio to coordinate their actions with what remote personnel are seeing on their local screens. This is a tried and true approach but cumbersome as it introduces delays and opportunities for errors. Plants are thus searching for a better tactic, one where these two areas converge.

The other convergence

Business information technologies are

converging with plant floor operational


technologies... HMIs anchored in place

Conventional control room operators typically work via fixed HMIs, which are usually PC-based systems (Figure 1). There are often other local HMI options in the field, ranging from pushbuttons to smaller displays, but these are typically also fixed in place, with very localized and limited functionality. When operators detect trouble, the next step is often for them to contact maintenance mechanics and electricians who are dispatched to the field

Within the field of industrial automation, there has been significant discussion and coverage of the ways that business information technologies (IT) are converging with plant floor operational technologies (OT). The IT/OT convergence is enabled as IT-based hardware, software, networking, and protocols become integrated and more compatible with OT-hardware such as PLCs and HMIs, which have historically been much more specialized and dedicated products. This convergence is a story not just of merging technology, but also of the personnel in each of those areas gaining greater visibility, along with improved coordination. The result is more efficient operations and other improvements. But there is another lesser known convergence occurring at manufacturing plants, and it is located out in the OT production area. This convergence involves the roles of operators and maintenance personnel, and it is

made possible by the aforementioned IT/OT convergence. One result of the IT/OT convergence has been the proliferation of highly capable mobile HMI options. End users are already thoroughly experienced with the use of mobile devices in their everyday lives, and many prefer the same experience in industrial settings, as long as the device durability and performance are acceptable. Some facilities may use mobile phones for certain HMI functions, even enabling employees to bring their own devices (BYOD). Others provide site-specific commercial or industrially hardened tablets. The devices can be configured so operators can initiate control, although oftentimes BYOD devices are used for read-only access. Regardless of the device format, mobile HMIs release operators from the control room and provide extended information for maintenance personnel at field locations. In some cases, a single person working in a converged operations/maintenance role can quickly identify a problem, physically go to it, troubleshoot it, and resolve it.

Rising tide of capabilities Perhaps some form of fixed HMI will always be needed, but many types of operations can benefit from adding mobile HMIs into the mix. Mobile HMIs provide crucial operational information to operators on the production line. When issues occur, mobile HMIs are changing the two-step operator and maintenance interaction process into a more collaborative and streamlined effort. Operators can carry HMIs right out to the production line, identify problems faster, and in some cases resolve the issue immediately, for example by adjusting a setpoint and observing

FIGURE 1: Mobile HMIs may be hand-held, or on a cart as shown here, but can serve to widen the area where control and information tasks can be performed, from the central control room to the plant floor. All graphics courtesy: Emerson

its effect. If maintenance personnel are needed, they can be called to the spot, often by receiving a message via a mobile HMI. When they arrive, they can collaborate with the operators to troubleshoot the problem. Using fixed and mobile HMIs, maintenance personnel can take on some of the roles formerly restricted to operators, such as reacting to alarms, often by working in concert with operators. Mobile HMIs are also improved by other underlying technologies resulting from the IT/OT convergence. One prominent example is that the latest OT controllers include not only traditional programmable logic controller (PLC) control, but also seamlessly merge PC-like computing abilities. This enables system designers to include advanced processing out at the PLC, such as real-time analytics, without disrupting the basic automation functions. The latest PLCs can even serve up graphics directly to mo-

bile devices. This type of enhanced information helps operations and/or maintenance personnel run the plant at peak efficiency.

Operators walk the line Food and beverage production facilities, like many types of manufacturing sites, operate a distributed mix of equipment, so these plants naturally fit well with mobile HMI use. These locations typically include areas such as: n Raw material receiving and bulk storage n Processing and blending systems n Bottle conveyors n Fillers n Carton machines n Robotic palletizers Each of these unique areas uses equipment and systems that are IIoT For Engineers


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largely standalone, other than upto correlate the HMI-supplied inforstream/downstream handshakes. The mation with actual conditions they automation products and programcan see firsthand. Operators can deMobile HMIs are ming for each system is likely to have termine if production stoppages are been provided by different vendors, due to a simple blockage of bottles changing the way each a specialist in their area. they can correct, or if the problem industrial users work, Even so, the many disparate appears to require maintenance systems may report back to an attention. Maintenance personnel in many ways and for overall HMI application in a cencan use the information delivered tral control room for monitoring, by their mobile HMIs to work more various reasons. alarming, setpoint/recipe settings, efficiently. and control commands. When there In either case, personnel become are problems, such as backups on more effective because the time bebottle conveyors, or insufficient cartons arriving at the tween recognizing and resolving problems is minimized robotic palletizers, it can be difficult to determine the when mobile HMIs are combined with a firsthand perroot cause from the control room. It is often helpful for spective in the field. an operator or maintenance person to “walk the line” so he or she can inspect the condition directly and Changing how work is done discover what is wrong. Mobile HMIs are changing the way industrial users The best of both worlds is possible when operators work, in many ways and for various reasons. There is a and maintenance personnel are each outfitted with generational shift because many younger workers are mobile HMIs they can take to the field, enabling them comfortable and even prefer working with mobile devices. These workers feel empowered when they know more information, and mobile HMIs can be the best way to bring real-time knowledge into their hands, even out on the factory floor. The technology helps close the experience and skills gap between the control room and the field. Future mobile developments such as augmented reality will improve the collaboration further as field workers can act as remote experts and share their view with others. Industrial IT/OT convergence has arrived at just the right time to effectively combine established industrial methodologies with the latest IT technologies, resulting in fullfeatured mobile HMIs able to break out of the traditional control room. This has led to a secondary convergence of operations and maintenance personnel at operating plants resulting in the best use of all available expertise. These workers now have the tools at hand to provide additional information, enabling them to be more efficient and improving operations productivity. IIoT

Rich Carpenter is the general manager for product management for Emerson’s Machine Automation Solutions business unit and has responsibility for its portfolio of control system, operator interface, industrial PC, and Industrial IOT software and hardware products for industrial automation.




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Interoperability and how to sustain it Vocabulary and concepts for the age of analytics organizations — to exchange and use information without knowledge of the characteristics or inner workings ne critical aspect of any of the collaborating systems — or digital transformation is organizations. interoperability. InteroperFurther, we observe, by convention, ability of components, devices and “levels” of interoperability, wherein systems is necessary because, without each level increases interoperabilinteroperability, organizations will ity in a network or community. The continue spending precious resources suggestion is that greater interoperon costly, ineffective and brittle data ability leads to greater autonomy. For searching, preparation and aggreour purposes, the salient levels are gation functions. The real value of standards-based, semantic-based and analysis and automation will remain sustained interoperability. lacking. Standards-based interoperability In other words, the ability to locate, includes dedicated reference models understand, access and trust data is covering many business areas and a key enabler of digital transformarelated application activities. From the tion. In this context, interoperability design phase to production and comis the ability of systems — including mercialization, standards are developed to enable organizations to exchange information based on common models. Semantic interoperability is the ability of computer systems to exchange data with unambiguous, machine understandable meaning. Semantic interoperability is required to enable machine computable logic, inferencing, knowledge discovery, and data federation among information systems. In other words, despite standards for data formats FIGURE 1. A proposed model for sustained interoperabiland structures, informaity to maintain network harmony. All graphics courtesy: tion may not always be Cambridge Semantics Sam Chance





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understood by recipients. Explicit knowledge can be encoded, but tacit knowledge requires human interactions. Semantic interoperability adds semantic annotations and knowledge enrichment to address these issues. Ontologies represent the contemporary approach to implement knowledge enrichment and reach semantic interoperability.

Network harmonization Sustained interoperability maintains network harmonization. As an area of research this involves theory related to complex adaptive systems (CAS). Organizations — and networks — must adapt to survive. Change is constant. Models and semantics change, which can break the harmony of the network, introducing a new dimension to interoperability. Thus, when one network member adapts to a new requirement, it creates a ripple that propagates through the network, and the network begins experiencing interoperability problems. One model for sustaining interoperability includes a monitoring system that detects actions that break the network harmony. Upon discovery of the event, an intelligence integration layer interprets the change and devises a strategy to adapt to the change. Then a decision-support system assesses the strategy and decides on a course of action, using the communications layer to notify the network of the action to restore

network harmony. Thus, the network evolves to restore harmony. We believe that semantic interoperability is the key enabler for digital transformation. But, how do we achieve semantic interoperability? It has long been realized that interoperability could benefit by having content understandable and available in a machine processable form, and it is widely agreed that ontologies will play a key role in providing much enabling infrastructure to support this goal.

FIGURE 2. Ontology is a semantic interoperability enabler.

Ontology is the key In the broadest sense, ontology is the study of the nature of existence, beings and their relations. In information science, ontology provides a means to create unambiguous knowledge. “An” ontology is a formal specification of the concepts, types, properties and interrelationships of entities within a domain of the real world. Ontologies provide humans and machines an accurately understandable context or meaning. Ontologies ensure a common understanding of information. In practice ontologies describe and link disparate and complex data. Important architectural considerations of ontologies include the following. n Ontologies enable reuse of foundational concepts in (upper) ontologies that are domain independent and can be used across domains. n Modularity of ontologies allows separation and recombination of different parts of an ontology depending on specific needs, instead of creating a single common ontology. n Extensibility of ontologies allows further growth of the ontology for the purpose of specific applications. n Maintainability of ontologies facilitates the process of identifying and correcting defects, accommodates new requirements and copes with changes in an ontology.

FIGURE 3. Ontology as an explicit specification of a conceptualization.

n Ontologies enable separation of design and implementation concerns, so they are flexible to changes in specific implementation technologies. Notably, informal ontologies may lead to ambiguities. Systems based on informal ontologies are more errorprone than systems based on formal ontologies. Formal ontologies allow automated reasoning and consistency checking. Formal ontologies span from taxonomies of concepts related by subsumption relationships to complete representations of concepts related by complex relationships. Formal ontologies include axioms to constrain their intended concept interpretations. We require a language to create standard and shareable ontologies. When one models a portion of the real world, i.e., some domain of interest, a conceptualization exists in one’s mind. This is based on the concepts existing in the domain and

their salient relationships. An ontology language provides a mechanism to represent the concepts. The entire domain specification is expressed in the language. Thus, an ontology is an explicit specification of a conceptualization of some domain. So how do we arrive at a standard ontology language? In the 1990s there was a recognition that languages such as HTML and XML were insufficient for knowledge representation. HTML is oriented to rendering information in a human friendly presentation. XML provides a platform-independent data exchange model. In 1999, the European Union sponsored development of the Ontology Inference Layer (OIL). Note, sometimes “Information” is used in place of “Inference.” OIL was based on strong formal foundations of Description Logics, namely SHIQ. OIL was IIoT For Engineers


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FIGURE 4. Path to a standard ontology language.

compatible with a very lightweight model called Resource Description Framework Schema (RDFS), which was already standardized in 1998. In 2000, the Defense Advanced Research Projects Agency (DARPA) initiated the DARPA Agent Markup Language (DAML) project. DAML was to serve as the foundation for the next generation of the Web which would increasingly utilize “smart” agents and programs. One goal was to reduce the heavy reliance on human interpretation of data. DAML extended XML, RDF and RDFS to support machine understandability. DAML included “some” strong formal foundations of Description Logics but focused more on pragmatic application. Circa 2001, groups from the US and the EU collaborated to merge DAML and OIL, the result of which was known as DAML+OIL. DAML+OIL provided formal semantics that support machine and human understandability. This new language also provides axiomatization, or inference rules to expand reasoning services. In 2004, the World Wide Web Consortium (W3C) derived the Web Ontology Language (OWL) from DAML+OIL and published it as a “standard” knowledge representation language for authoring ontologies.




IIoT For Engineers

The initial OWL specification featured three “species” of OWL: OWL Lite, OWL DL, and OWL Full. In 2009, the W3C released OWL 2 which articulated different versions of OWL tailored to different reasoning requirements and application areas. This entire evolution can be aptly characterized as “making the data intelligent instead of the software.” Since the data is “common” to all software processes — and the areas within digital transformation — we can more effectively realize interoperable and autonomous systems.

Standardizing the enabler Subsequent to the initial release of OWL, the W3C articulated a set of standards and methods under the “Semantic Web” label. In this construct, the primary standards that adopters and vendors implement to create machine understandable, rich contextualized knowledge include Resource Description Framework (RDF), RDF Schema (RDFS), Web Ontology Language (OWL), and SPARQL Protocol And RDF Query Language (SPARQL). RDF provides the means to create, store and exchange semantic data. RDF is a Directed Acyclic Graph (DAG) which, for our purposes, means that

concepts are neither defined in terms of themselves nor in terms of other concepts that indirectly refer to them. In addition, Semantic Web standards enable machine reasoning services that infer new facts from existing facts; that is, semantic technologies make implicit data explicit. Semantic Web standards allow human and machine data consumers to know unambiguously what data mean. Semantic Web standards create machine understandable context in a standard and repeatable methodology. The idea is for data producers to publish machine understandable content that software and human consumers can discover and consume in a reliable and repeatable manner. As the ecosystem grows, the need for standardized publishing, finding and invoking semantic data and services applies. Ontologies grow, evolve and adapt over time as adoption increases. The superior ontologies naturally become more popular and gain traction. Because ontology is based on existence of beings and their relationships, terminology in information systems tends toward alignment. The need for “top down” or “highly coordinated” planned and implemented data architectures is diminished because the model is decentralized, distributed and based on formal ontology that is designed to achieve semantic interoperability in a federated manner. A subject beyond the scope of this paper, ontologybased approaches assume that “one never has all the facts.” Previous approaches did not make this assumption, which resulted in inflexible designs wherein requirements had to be known in the design phase. IIoT Sam Chance is a principal consultant with Cambridge Semantics, Boston.


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