Classifications, Concerns, and Concepts: Reference Architectures and the Industrial Internet of Things

The expected disruptive developments collectively referred to as the Internet of Things (IoT) have drawn significant attention in many industries, disciplines, and organizations. While the concrete benefits and requirements are still not sufficiently clear, the general agreement on its relevance and impact is undeniable. As a result, a large number of initiatives and consortia from industry and research have been formed to all set the de facto standards and best practices. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications, and the concerns they target.

en flag
nl flag
et flag
fi flag
fr flag
de flag
pt flag
ru flag
es flag

Editor’s Note: According to the authors, this article on the structuring of reference architectures for the Industrial Internet of Things (IIoT) contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications, and the concerns they target. The article supplies alignment of shared concepts, identifies gaps, and gives a structured mapping of concerns at each part of respective reference architectures. The article also links relevant industry standards and technologies to the architectures, allowing for a more effective search for specifications and guidelines and supporting direct technology adoption. Understanding the classifications, concerns, and concepts presented in this article may be beneficial for legal, business, and information technology professionals in the eDiscovery ecosystem as they consider the challenges and opportunities for data discovery and legal discovery in the age of the IIoT.

An extract from the article by Sebastian Bader, Maria Maleshkova, and Steffen Lohmann

Structuring Reference Architectures for the Industrial Internet of Things*

Abstract

The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.

Introduction

The expected disruptive developments collectively referred to as the Internet of Things (IoT) have drawn significant attention in many industries, disciplines and organizations. While the concrete benefits and requirements are still not sufficiently clear, the general agreement on its relevance and impact is undeniable. As a result, a large number of initiatives and consortia from industry and research have been formed to all set the de facto standards and best practices.

Especially the manufacturing industry is actively involved in numerous activities related to this topic. Organizing this area and enabling effective discussions and design decisions are the targets of several standardization efforts. Many of them provide reference frameworks and architecture models. Reference frameworks in this context provide the necessary structure to transform the combined experiences and best practices, the opportunities of available technologies and the expected implications into understandable guidance for the involved stakeholders.

As a common understanding has not yet been reached, the current situation is characterized by the variety of proposed models and frameworks, created by groups of experts from different countries and domains. Whereas the goal of each approach is to overcome the current confusion, the huge amount of published models is again becoming a source of heterogeneity and misunderstanding. Newcomers and non-experts are overwhelmed by the amount of published recommendations and suggestions, contradicting terminology, inconsistent structuring and proposed best practices. The uncountable efforts intended to structure the domain have by now created another dimension of complexity. The thereby created barriers aggravate the adaption of crucial developments and decelerate further progress. Moreover, the rising difficulty to find and classify relevant information undermines the further propagation of the core principles.

Therefore, a consistent alignment of the different frameworks and a structured organization of the main concepts are a pressing need, in order to create a sufficiently complete picture of the current state of the specification processes. Following the assumption that a single model can not cover all requirements, we annotated and interlinked the frameworks and models of the most influential initiatives, which cope with the digitization of the manufacturing domain. An openly available knowledge graph with self-defined and both human and machine-readable concepts, serves as the representation of the derived facts. Based on this grounded mapping of discovered relations, variances and commonalities we illustrate the different scopes and strengths.

This paper contributes to the mentioned challenges by:

  • Providing a methodology to structure, align and compare the various reference frameworks;
  • Presenting a collection of relevant concerns, their hierarchical structure and relationships;
  • Providing configurable visual views of the characteristics and relations between the concerns and the reference frameworks (http://i40.semantic-interoperability.org/sto-visualization/);
  • Offering an analysis of the thereby gained insights, for example, frequently covered areas or inconsistencies, which need further attention from the community.

The remainder of this paper is structured as follows: Section 2 gives an overview of other approaches to structure the observed frameworks and of surveys of the IIoT domain. The used methodology and data model is introduced in Section 3, followed by a description how the IIoT reference frameworks have been selected Section 4 and an introduction of the most relevant ones in Section 6, followed by an outline of the findings and outline research gaps in Section 7 and conclude with our lessons learned and future activities (Section 8).

Read the complete article at Structuring Reference Architectures for the Industrial Internet of Things

* Bader, S.R.; Maleshkova, M.; Lohmann, S. Structuring Reference Architectures for the Industrial Internet of Things. Future Internet 201911, 151.


Structuring Reference Architectures for the Industrial Internet of Things (PDF) Mouseover to Scroll

Structuring-Reference-Architectures-for-the-Industrial-Internet-of-Things

Original Source: Future Internet 201911(7), 151; https://doi.org/10.3390/fi11070151 (Republished Under the Creative Commons Attribution License)


Additional Reading

Source: ComplexDiscovery

Have a Request?

If you have information or offering requests that you would like to ask us about, please let us know and we will make our response to you a priority.

ComplexDiscovery is an online publication that highlights data and legal discovery insight and intelligence ranging from original research to aggregated news for use by business, information technology, and legal professionals. The highly targeted publication seeks to increase the collective understanding of readers regarding data and legal discovery information and issues and to provide an objective resource for considering trends, technologies, and services related to electronically stored information.

ComplexDiscovery OÜ is a technology marketing firm providing strategic planning and tactical execution expertise in support of data and legal discovery organizations. Registered as a private limited company in the European Union country of Estonia, one of the most digitally advanced countries in the world, ComplexDiscovery OÜ operates virtually worldwide to deliver marketing consulting and services.

Business as Unusual? Eighteen Observations on eDiscovery Business Confidence in the Summer of 2020

The results of the recent Summer 2020 eDiscovery Business Confidence Survey present the unfortunate and continuing impact of COVID-19 on the business of eDiscovery. However, for these pandemic-driven results to be fully understood, they should be viewed through the contextual lens of the results of all nineteen surveys that have been administered to eDiscovery professionals since the inception of the eDiscovery Business Confidence Survey in early 2016.



Check Out the Observations Now!

Interested in Contributing?

ComplexDiscovery combines original industry research with curated expert articles to create an informational resource that helps legal, business, and information technology professionals better understand the business and practice of data discovery and legal discovery.

All contributions are invested to support the development and distribution of ComplexDiscovery content. Contributors can make as many article contributions as they like, but will not be asked to register and pay until their contribution reaches $5.

Collaborative Cyber Defense: The U.S. Army and Estonia Sign Historic Agreement

“Estonia is a cyber country of excellence with a robust cyber...

Festive or Restive? The Fall 2020 eDiscovery Business Confidence Survey

Since January 2016, 2,189 individual responses to nineteen quarterly eDiscovery Business...

Blue-Sueded? Considerations for Decision Making

While an understanding of decisions from definitions and elements to cornerstones...

Socially Acceptable? EDBP Guidelines on the Targeting of Social Media Users

According to the recently published EDPB guidelines on the targeting of...

A Running List: Top 100+ eDiscovery Providers

Based on a compilation of research from analyst firms and industry...

The eDisclosure Systems Buyers Guide – 2020 Edition (Andrew Haslam)

Authored by industry expert Andrew Haslam, the eDisclosure Buyers Guide continues...

The Race to the Starting Line? Recent Secure Remote Review Announcements

Not all secure remote review offerings are equal as the apparent...

Enabling Remote eDiscovery? A Snapshot of DaaS

Desktop as a Service (DaaS) providers are becoming important contributors to...

Home or Away? New eDiscovery Collection Market Sizing and Pricing Considerations

One of the key home (onsite) or away (remote) decisions that...

Revisions and Decisions? New Considerations for eDiscovery Secure Remote Reviews

One of the key revision and decision areas that business, legal,...

A Macro Look at Past and Projected eDiscovery Market Size from 2012 to 2024

From a macro look at past estimations of eDiscovery market size...

An eDiscovery Market Size Mashup: 2019-2024 Worldwide Software and Services Overview

While the Compound Annual Growth Rate (CAGR) for worldwide eDiscovery software...

Festive or Restive? The Fall 2020 eDiscovery Business Confidence Survey

Since January 2016, 2,189 individual responses to nineteen quarterly eDiscovery Business...

Casting a Wider Net? Predictive Coding Technologies and Protocols Survey – Fall 2020 Results

The Predictive Coding Technologies and Protocols Survey is a non-scientific semi-annual...

Business as Unusual? Eighteen Observations on eDiscovery Business Confidence in the Summer of 2020

Based on the aggregate results of nineteen past eDiscovery Business Confidence...

A Growing Concern? Budgetary Constraints and the Business of eDiscovery

In the summer of 2020, 56% of respondents viewed budgetary constraints...

ayfie to Acquire Haive

According to Johannes Stiehler, CEO of ayfie Group AS, “This acquisition...

Innovative Discovery and Integro Merge

“Integro and Innovative Discovery’s services and solutions are highly complementary. Our...

Software Growth Partners Makes Majority Investment in Venio Systems

According to the press announcement, industry analysts have enthusiastically supported this...

Reveal Acquires NexLP

According to Jay Leib, Co-Founder and CEO of NexLP, "We chose...

Five Great Reads on eDiscovery for August 2020

From predictive coding and artificial intelligence to antitrust investigations and malware,...

Five Great Reads on eDiscovery for July 2020

From business confidence and operational metrics to data protection and privacy...

Five Great Reads on eDiscovery for June 2020

From collection market size updates to cloud outsourcing guidelines, the June...

Five Great Reads on eDiscovery for May 2020

From review market sizing revisions to pandemeconomic pricing, the May 2020...