Data mining is the process of data analysis and information extraction from large amounts of datasets with machine learning, statistical approaches. and many others. Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more. Also, Data mining is the process of turning raw data into useful information by using software to look for meaningful patterns[347 - Data Mining – Text: electronic. – https://bigdataschool.ru (https://bigdataschool.ru/) URL: https://www.teradata.ru/Glossary/What-is-Data-Mining (https://www.teradata.ru/Glossary/What-is-Data-Mining) (date of request: 17.02.2022)],[348 - Data mining – Text: electronic. – www.sas.com (http://www.sas.com/) (date of request: 07.07.2022) https://www.sas.com/en_us/insights/analytics/data-mining.html (https://www.sas.com/en_us/insights/analytics/data-mining.html)],[349 - Data mining – Text: electronic. – https://www.trendminer.com (https://www.trendminer.com/) URL: https://www.trendminer.com/iiot-glossary/ (https://www.trendminer.com/iiot-glossary/) (date of request: 25.02.2023)].
Data modeling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows. Data models provide a blueprint for designing a new database or reengineering a legacy application. Overall, data modeling helps an organization use its data effectively to meet business needs for information[350 - Data modeling – Text: electronic. – www.techtarget.com (http://www.techtarget.com/) (date of request: 07.07.2022) https://www.techtarget.com/searchdatamanagement/definition/data-modeling (https://www.techtarget.com/searchdatamanagement/definition/data-modeling)]
Data portability allows individuals to obtain and reuse their personal data for their own purposes across different services. It allows them to move, copy or transfer personal data easily from one IT environment to another in a safe and secure way, without affecting its usability[351 - Data portability – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/trusted-third-party-2/ (https://digitalhealtheurope.eu/glossary/trusted-third-party-2/) (date of request: 10.11.2022)].
Data Privacy – the assurance that a persons or organizations personal and private information is not inappropriately disclosed. Ensuring Data Privacy requires Access Management, eSecurity, and other data protection efforts[352 - Data Privacy – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-privacy/ (https://digitalhealtheurope.eu/glossary/data-privacy/) (date of request: 10.11.2022)].
Data Processing within the field of information technology, typically means the processing of information by machines. Data processing is defined by procedures designed to make a data collection easier to use, ensure its accuracy, enhance its utility, optimize its format, protect confidentiality, etc. For archival purposes, the process and results of data processing must be systematically and comprehensively captured so that the process applied to the data is transparent to users[353 - Data Processing – Text: electronic. – www.umich.edu (http://www.umich.edu/) URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D) (date of request: 07.07.2022)].
Data Processor (or Processor) – the natural or legal person, or any other body, which processes personal data on behalf of the controller[354 - Data Processor (or Processor) – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-processor-or-processor/ (https://digitalhealtheurope.eu/glossary/data-processor-or-processor/) (date of request: 10.11.2022)].
Data Protection Authority monitors and supervises, through investigative and corrective powers, the application of the data protection law. It provides expert advice on data protection issues and handle complaints that may have breached the law[355 - Data Protection Authority – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-protection-authority/ (https://digitalhealtheurope.eu/glossary/data-protection-authority/) (date of request: 10.11.2022)].
Data protection is the process of protecting data and involves the relationship between the collection and dissemination of data and technology, the public perception and expectation of privacy and the political and legal underpinnings surrounding that data. It aims to strike a balance between individual privacy rights while still allowing data to be used for business purposes[356 - Data protection – Text: electronic. – www.techopedia.com (http://www.techopedia.com/) (date of request: 07.07.2022) URL: https://www.techopedia.com/definition/29406/data-protection (https://www.techopedia.com/definition/29406/data-protection)].
Data Protection Officer ensures that the organisation processes the personal data of its staff, customers, providers or any other individuals (also referred to as data subjects) in compliance with the applicable data protection rules[357 - Data Protection Officer – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-protection-officer/ (https://digitalhealtheurope.eu/glossary/data-protection-officer/) (date of request: 10.11.2022)].
Data Requestor – person or institution that is looking for data and provides the necessary infrastructure, e.g. a publicly available Semantic Container initialized with a semantic description of the data request and intended purpose of the collected data[358 - Data Requestor – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-requestor/ (https://digitalhealtheurope.eu/glossary/data-requestor/) (date of request: 10.11.2022)].
Data Science is a broad grouping of mathematics, statistics, probability, computing, data visualization to extract knowledge from a heterogeneous set of data (images, sound, text, genomic data, social network links, physical measurements, etc.). The methods and tools derived from artificial intelligence are part of this family. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value. Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Also, Data Science this is an academic/professional field that comprises several components for data analysis and interpretation through mathematics, statistics and information technology. Thus, a data scientist not only collects and analyzes inputs, but also interprets and relates the facts to the context in which they are inserted[359 - Data science – Text: electronic. – www.datarobot.com (http://www.datarobot.com/) (date of request: 07.07.2022) URL: https://www.datarobot.com/wiki/data-science/ (https://www.datarobot.com/wiki/data-science/)],[360 - Data science – Text: electronic. – www.igi-global.com (http://www.igi-global.com/) (date of request: 07.07.2022) URL: https://www.igi-global.com/dictionary/integrating-big-data-technology-into-organizational-decision-support-systems/40290 (https://www.igi-global.com/dictionary/integrating-big-data-technology-into-organizational-decision-support-systems/40290)],[361 - Data Science – Text: electronic. – https://packiot.com (https://packiot.com/) URL: https://packiot.com/glossary-of-digital-transformation-in-manufacturing-40-terms-you-must-know/ (https://packiot.com/glossary-of-digital-transformation-in-manufacturing-40-terms-you-must-know/) (date of request: 25.02.2023)].
Data set is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. Data set a collection of data records. In the SAS statistical software, a «SAS data set» is the internal representation of data. Also, Data set is a set of data that has undergone preliminary preparation (processing) in accordance with the requirements of the legislation of the Russian Federation on information, information technology and information protection and is necessary for the development of software based on artificial intelligence (National strategy for the development of artificial intelligence for the period up to 2030)[362 - Data set – Text: electronic. – https://en.wikipedia.org (https://en.wikipedia.org/) URL: https://en.wikipedia.org/wiki/Data_set (https://en.wikipedia.org/wiki/Data_set) (date of request: 07.07.2022)],[363 - Dataset – Text: electronic. – www.umich.edu (http://www.umich.edu/) URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D) (date of request: 07.07.2022)].
Data Sharing – the disclosure of data from one or more organizations to a third party organisation or organizations, or the sharing of data between different parts of an organisation[364 - Data Sharing – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-sharing/ (https://digitalhealtheurope.eu/glossary/data-sharing/) (date of request: 10.11.2022)].
Data Sharing Agreement – common set of rules to be adopted by the various organizations involved in a data sharing operation[365 - Data Sharing Agreement – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-sharing-agreement/ (https://digitalhealtheurope.eu/glossary/data-sharing-agreement/) (date of request: 10.11.2022)].
Data sharing governance – concept changing «ownership’ of data-to-data control and data sharing governance[366 - Data sharing governance – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-sharing-governance/ (https://digitalhealtheurope.eu/glossary/data-sharing-governance/) (date of request: 10.11.2022)].
Data silos are repositories of fixed data that remain under the control of one group or department and that are isolated from the rest of the organization[367 - Data silos – Text: electronic. – https://www.trendminer.com (https://www.trendminer.com/) URL: https://www.trendminer.com/iiot-glossary/ (https://www.trendminer.com/iiot-glossary/) (date of request: 25.02.2023)].
Data source is the primary location where the data that is being used comes from[368 - Data source – Text: electronic. – https://www.trendminer.com (https://www.trendminer.com/) URL: https://www.trendminer.com/iiot-glossary/ (https://www.trendminer.com/iiot-glossary/) (date of request: 25.02.2023)].
Data Stakeholders – those who use, affect, or are affected by data. Data Stakeholders may be upstream producers, gatherers, or acquirers of information; downstream consumers of information, those who manage, transform, or store data, or those who set policies, standards, architectures, or other requirements or constraints[369 - Data Stakeholders – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-stakeholders (https://digitalhealtheurope.eu/glossary/data-stakeholders) (date of request: 10.11.2022)].
Data Steward is a person with data-related responsibilities as set by a Data Governance or Data Stewardship program. Often, Data Stewards fall into multiple types. Data Quality Stewards, Data Definition Stewards, Data Usage Stewards, etc.[370 - Data Steward – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-steward/ (https://digitalhealtheurope.eu/glossary/data-steward/) (date of request: 10.11.2022)].
Data Subject is the person whose personal data are collected, held or processed. identified or identifiable natural person, who is the subject of personal data[371 - Data Subject – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/data-subject/ (https://digitalhealtheurope.eu/glossary/data-subject/) (date of request: 10.11.2022)].
Data transfer rate (DTR) is the amount of digital data that is moved from one place to another in a given time. The data transfer rate can be viewed as the speed of travel of a given amount of data from one place to another. In general, the greater the bandwidth of a given path, the higher the data transfer rate[372 - Data transfer rate (DTR) – Text: electronic. – www.techtarget.com (http://www.techtarget.com/) (date of request: 07.07.2022) URL: https://www.techtarget.com/searchunifiedcommunications/definition/data-transfer-rate (https://www.techtarget.com/searchunifiedcommunications/definition/data-transfer-rate)].
Data variability describes how far apart data points lie from each other and from the center of a distribution. Along with measures of central tendency, measures of variability give you descriptive statistics that summarize your data[373 - Data variability – Text: electronic. – www.investopedia.com (http://www.investopedia.com/) (date of request: 07.07.2022) URL: https://www.investopedia.com/terms/v/variability.asp (https://www.investopedia.com/terms/v/variability.asp)].
Data veracity is the degree of accuracy or truthfulness of a data set. In the context of big data, it’s not just the quality of the data that is important, but how trustworthy the source, the type, and processing of the data are[374 - Data veracity – Text: electronic. – https://datafloq.com (https://datafloq.com/) (date of request: 07.07.2022) URL: https://datafloq.com/read/data-veracity-new-key-big-data/ (https://datafloq.com/read/data-veracity-new-key-big-data/)].
Database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Together, the data and the DBMS, along with the applications that are associated with them, are referred to as a database system, often shortened to just database. Data within the most common types of databases in operation today is typically modeled in rows and columns in a series of tables to make processing and data querying efficient. The data can then be easily accessed, managed, modified, updated, controlled, and organized. Most databases use structured query language (SQL) for writing and querying data[375 - Database – Text: electronic. – www.oracle.com (http://www.oracle.com/) (date of request: 07.07.2022) URL: https://www.oracle.com/database/what-is-database/ (https://www.oracle.com/database/what-is-database/)].
Database management system (DBMS) is a software package designed to define, manipulate, retrieve and manage data in a database. A DBMS generally manipulates the data itself, the data format, field names, record structure and file structure. It also defines rules to validate and manipulate this data. Database management systems are set up on specific data handling concepts, as the practice of administrating a database evolves. The earliest databases only handled individual single pieces of specially formatted data. Today’s more evolved systems can handle different kinds of less formatted data and tie them together in more elaborate ways[376 - Database management system (DBMS) – Text: electronic. – www.techopedia.com (http://www.techopedia.com/) (date of request: 07.07.2022) URL: https://www.techopedia.com/definition/24361/database-management-systems-dbms (https://www.techopedia.com/definition/24361/database-management-systems-dbms)].
Databus is a data-centric sharing system where applications exchange information in a virtual, global data space[377 - Databus – Text: electronic. – https://www.freewave.com (https://www.freewave.com/) URL: https://www.freewave.com/freewaves-industrial-internet-of-things-iiot-glossary/ (https://www.freewave.com/freewaves-industrial-internet-of-things-iiot-glossary/) (date of request: 25.02.2023)].
Data-driven decisions are decisions made based on data/information, not experience, hunches, or intuition[378 - Data-driven decisions – Text: electronic. – https://www.trendminer.com (https://www.trendminer.com/) URL: https://www.trendminer.com/iiot-glossary/ (https://www.trendminer.com/iiot-glossary/) (date of request: 25.02.2023)].
Dataflow Processing Unit (DPU) is a programmable specialized electronic circuit with hardware accelerated data processing for data-oriented computing.
DDI instance an XML document, marked up according to the DDI DTD. In other words, a codebook or catalog record marked up in DDI-compliant XML[379 - DDI instance – Text: electronic. – www.umich.edu (http://www.umich.edu/) (date of request: 07.07.2022) URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D)].
Debugging is the process of finding and resolving bugs (defects or problems that prevent correct operation) within computer programs, software, or systems. Debugging tactics can involve interactive debugging, control flow analysis, unit testing, integration testing, log file analysis, monitoring at the application or system level, memory dumps, and profiling. Many programming languages and software development tools also offer programs to aid in debugging, known as debuggers[380 - Debugging – Text: electronic. – https://en.wikipedia.org (https://en.wikipedia.org/) (date of request: 07.07.2022) URL: https://en.wikipedia.org/wiki/Debugging (https://en.wikipedia.org/wiki/Debugging)].
Decentralized applications (dApps) are digital applications or programs that exist and run on a blockchain or peer-to-peer (P2P) network of computers instead of a single computer. DApps (also called «dapps») are outside the purview and control of a single authority. DApps – which are often built on the Ethereum platform – can be developed for a variety of purposes including gaming, finance, and social media[381 - Decentralized applications (dApps) – Text: electronic. – www.investopedia.com (http://www.investopedia.com/) (date of request: 07.07.2022) URL: https://www.investopedia.com/terms/d/decentralized-applications-dapps.asp (https://www.investopedia.com/terms/d/decentralized-applications-dapps.asp)].
Decentralized control is a process in which a significant number of control actions related to a given object are generated by the object itself on the basis of self-government.
Decentralized finance (DeFi) is an emerging financial technology based on secure distributed ledgers similar to those used by cryptocurrencies. The system removes the control banks and institutions have on money, financial products, and financial services[382 - Decentralized finance (DeFi) – Text: electronic. – www.investopedia.com (http://www.investopedia.com/) (date of request: 07.07.2022) URL: https://www.investopedia.com/decentralized-finance-defi-5113835 (https://www.investopedia.com/decentralized-finance-defi-5113835)].
Decision intelligence (DI) is a practical discipline used to improve the decision making process by clearly understanding and programmatically developing how decisions are made and how the outcomes are evaluated, managed and improved through feedback. Also, Decision intelligence is a discipline offers a framework to assist data and analytics practitioners develop, model, align, implement, track, and modify decision models and processes related to business results and performance.
Decision Rights – the system of determining who makes a decision, and when, and how, and under what circumstances. Formalizing Decision Rights is a key function of Data Governance[383 - Decision Rights – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/decision-rights/ (https://digitalhealtheurope.eu/glossary/decision-rights/) (date of request: 10.11.2022)].
Decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance – i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both. Also, Decision Support Systems is a collection of integrated technologies, software and hardware, that constitute the main support of the organization`s decision making process[384 - Decision Support Systems – Text: electronic. – www.igi-global.com (http://www.igi-global.com/) (date of request: 07.07.2022) URL: https://www.igi-global.com/dictionary/decision-support-systems/7020 (https://www.igi-global.com/dictionary/decision-support-systems/7020)].
Decision tree is a tree-and-branch model used to represent decisions and their possible consequences, similar to a flowchart.
Decompression is a feature that is used to restore data to uncompressed form after compression[385 - Decompression – Text: electronic. – www.umich.edu (http://www.umich.edu/) (date of request: 07.07.2022) URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D)].
Deep Learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Also, Deep Learning (DL) is a subfield of machine learning concerned with algorithms that are inspired by the human brain that works in a hierarchical way. Deep Learning models, which are mostly based on the (artificial) neural networks, have been applied to different fields, such as speech recognition, computer vision, and natural language processing[386 - Deep learning – Text: electronic. – https://en.wikipedia.org (https://en.wikipedia.org/) (date of request: 07.07.2022) URL: https://en.wikipedia.org/wiki/Deep_learning (https://en.wikipedia.org/wiki/Deep_learning)].
Deep neural network – a multilayer network containing several (many) hidden layers of neurons between the input and output layers, which allows modeling complex nonlinear relationships. GNNs are now increasingly used to solve such artificial intelligence problems as speech recognition, natural language processing, computer vision, etc., including in robotics[387 - Deep neural network – Text: electronic. – https://machinelearningmastery.ru (https://machinelearningmastery.ru/) URL: https://www.machinelearningmastery.ru/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/ (https://www.machinelearningmastery.ru/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/) (date of request: 08.02.2022)].
Deep Technology (DEEP TECH) refers to a startup whose business idea is based on a scientific or otherwise extensive (deep) understanding of technology. The term has been adopted to set certain companies apart from other startups which are also technology driven. A deep tech company may, for instance, base the core of its operations on particularly complex mathematics in the creation of software algorithms. Deep technology companies typically comprise artificial intelligence companies, which try to replicate human thinking, build navigation systems for flying cars and so on[388 - Deep Technology (DEEP TECH) – Text: electronic. – www.sofokus.com (http://www.sofokus.com/) (date of request: 07.07.2022) URL: https://www.sofokus.com/glossary-of-digital-business/#D (https://www.sofokus.com/glossary-of-digital-business/#D)].
DeepMind is an artificial intelligence company founded in 2010 and later acquired by Google in 2014. DeepMind developed AlphaGo program that beat a human professional Go player for the first time.
Default access controls – the access controls that apply where a registered individual has not set controls on the registered healthcare provider organizations or nominated representatives who may access the individual’s My Health Record. This means that any registered healthcare provider organisation involved in your healthcare can access your record[389 - Default access controls – Text: electronic. – www.digitalhealth.gov.au/ (http://www.digitalhealth.gov.au/) URL: www.digitalhealth.gov.au/support/glossary (http://www.digitalhealth.gov.au/support/glossary) (date of request: 10.11.2022)].
Degree of maturity is the degree of clarity (clarity) of the definition, management, measurement, control and implementation of a specific technological process.
De-identification – process of rendering data pseudonymized or anonymized. general term for any process of removing the association between a set of identifying data and the data subject[390 - De-identification – Text: electronic. – https://digitalhealtheurope.eu (https://digitalhealtheurope.eu/) URL: https://digitalhealtheurope.eu/glossary/de-identification/ (https://digitalhealtheurope.eu/glossary/de-identification/) (date of request: 10.11.2022)].
Denial of Service (DoS) prevents unauthorized access to resources. It also prevents time-critical operation delays[391 - Denial of Service (DoS) – Text: electronic. – https://www.freewave.com (https://www.freewave.com/) URL: https://www.freewave.com/freewaves-industrial-internet-of-things-iiot-glossary/ (https://www.freewave.com/freewaves-industrial-internet-of-things-iiot-glossary/) (date of request: 25.02.2023)].
Depersonalization of personal data is actions as a result of which it is impossible to determine, without the use of additional information, the belonging of personal data to a specific User or other personal data subject[392 - Depersonalization of personal – Text: electronic. – https://link.springer.com (https://link.springer.com/) (date of request: 07.07.2022) URL: https://link.springer.com/chapter/10.1007/978-3-030-39225-3_83 (https://link.springer.com/chapter/10.1007/978-3-030-39225-3_83)].
Depthwise separable convolutional neural network (sepCNN) – a convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions. Also known as Xception. A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n ✕ n ✕ 1), and then second, a pointwise convolution, with length and width of 1 (1 ✕ 1 ✕ n). To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions.
Design Center – an organizational unit (the entire organization or its subdivision) that performs a full range or part of the work on creating products up to the stage of its mass production, and also has the necessary personnel, equipment and technologies for this.
Design thinking is an iterative process in which we seek to understand the user, challenge assumptions, and redefine problems in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding. At the same time, it provides a solution-based approach to solving problems. It is a way of thinking and working as well as a collection of hands-on methods[393 - Design thinking – Text: electronic. – https://cdn.aigroup.com.au (https://cdn.aigroup.com.au/) URL: https://cdn.aigroup.com.au/Policy/2018/Industry4-0/Glossary.pdf (https://cdn.aigroup.com.au/Policy/2018/Industry4-0/Glossary.pdf) (date of request: 25.02.2023)].
