SQL and NoSQL Databases in the Context of Industry 4.0

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture’s functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0.

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SQL and NoSQL Databases in the Context of Industry 4.0

10.20944/preprints202111.0019.v1 ◽

2021 ◽

Author(s):

Vitor Furlan de Oliveira ◽

Marcosiris Amorim de Oliveira Pessoa ◽

Fabrício Junqueira ◽

Paulo Eigi Miyagi

Keyword(s):

Big Data ◽

Data Storage ◽

Industry 4.0 ◽

Transformation Process ◽

Data Models ◽

Technological Transformation ◽

Nosql Databases ◽

Five Dimensions ◽

Advantages And Disadvantages ◽

Logical Data

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that Big Data & Analytics is considered a technological pillar of this process. The literature reports a series of system architecture proposals that seek to implement the so-called Smart Factory, which is primarily data-driven. Many of these proposals treat data storage solutions as mere entities that support the architecture's functionalities. However, choosing which logical data model to use can significantly affect the performance of the architecture. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, taking into account the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of Big Data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of SQL and NoSQL databases for different scenarios within I4.0.

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Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

10.4018/978-1-6684-3694-3.ch021 ◽

2022 ◽

pp. 406-428

Author(s):

Lejla Banjanović-Mehmedović ◽

Fahrudin Mehmedović

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Industry 4.0 ◽

Data Analytics ◽

Manufacturing Systems ◽

System Modeling ◽

Big Data Analytics ◽

Intelligent Manufacturing ◽

Intelligent Manufacturing Systems ◽

Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

Advances in Business Information Systems and Analytics - Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing ◽

10.4018/978-1-7998-2725-2.ch002 ◽

2020 ◽

pp. 31-52 ◽

Cited By ~ 1

Author(s):

Lejla Banjanović-Mehmedović ◽

Fahrudin Mehmedović

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Industry 4.0 ◽

Data Analytics ◽

Manufacturing Systems ◽

System Modeling ◽

Big Data Analytics ◽

Intelligent Manufacturing ◽

Intelligent Manufacturing Systems ◽

Intelligent Decision Making

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Comparison between SQL and NoSQL Databases and Their Relationship with Big Data Analytics

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2019/v4i230108 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Wajid Ali ◽

Muhammad Usman Shafique ◽

Muhammad Arslan Majeed ◽

Ali Raza

Keyword(s):

Big Data ◽

Data Storage ◽

Data Analytics ◽

Relational Databases ◽

Web Applications ◽

Distributed Storage ◽

Big Data Analytics ◽

Nosql Databases ◽

Advantages And Disadvantages ◽

The World

A key ingredient in the world of cloud computing is a database that can be used by a great number of users. Distributed storage mechanisms become the de-facto method for data storage used by companies for the new generation of web applications. In the world of data storage, NoSQL (usually interpreted as "not only SQL" by developers) database is a growing trend. It is said that NoSQL alternates with the most widely used relational databases for the data storage, but, as the name implies, it does not fully replace the SQL. In this paper we will discuss about SQL and NoSQL databases, comparison of traditional SQL with NoSQL databases for Big Data analytics, NoSQL data models, types of NoSQL data stores, characteristics and features of each data store, advantages and disadvantages of NoSQL and RDBMS.

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Big Data-driven Decision-Making Processes, Real-Time Advanced Analytics, and Cyber-Physical Production Networks in Industry 4.0-based Manufacturing Systems

Economics Management and Financial Markets ◽

10.22381/emfm16420216 ◽

2021 ◽

Vol 16 (4) ◽

pp. 84

Keyword(s):

Decision Making ◽

Big Data ◽

Real Time ◽

Industry 4.0 ◽

Manufacturing Systems ◽

Data Driven ◽

Production Networks ◽

Data Driven Decision Making ◽

Decision Making Processes ◽

Advanced Analytics

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NoSQL Databases

Advances in Data Mining and Database Management - Handbook of Research on Cloud Infrastructures for Big Data Analytics ◽

10.4018/978-1-4666-5864-6.ch008 ◽

2014 ◽

pp. 186-215 ◽

Cited By ~ 2

Author(s):

Ganesh Chandra Deka

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Processing ◽

Open Source ◽

Data Storage ◽

Big Data Processing ◽

Nosql Databases ◽

Data Intensive ◽

Huge Data ◽

Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

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Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Managing Big Data in Cloud Computing Environments ◽

10.4018/978-1-4666-9834-5.ch006 ◽

2016 ◽

pp. 133-162 ◽

Cited By ~ 7

Author(s):

Berkay Aydin ◽

Vijay Akkineni ◽

Rafal A Angryk

Keyword(s):

Data Storage ◽

Relational Databases ◽

Large Scale ◽

Database Systems ◽

Distributed Database ◽

Data Models ◽

Trajectory Data ◽

Nosql Databases ◽

Distributed Database Systems ◽

Partitioned Data

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

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Sustaining India's Gig Economy in Industry 4.0

10.4018/978-1-7998-8705-8.ch015 ◽

2022 ◽

pp. 172-189

Author(s):

Vidushi Vatsa ◽

Ruchika Gupta ◽

Priyank Srivastava

Keyword(s):

Labour Market ◽

Industry 4.0 ◽

Analytical Framework ◽

Transformation Process ◽

Gradual Improvement ◽

Indian Economy ◽

Advantages And Disadvantages ◽

Gig Economy ◽

Brick And Mortar ◽

Policy Proposals

Today's corporate landscape is undergoing a transformation process, and India is not untouched by these phases of transition as humans are replaced by computers and brick-and-mortar firms are substituted by e-commerce companies. In the midst of these shifts, issues such as labour dynamics have changed dramatically. One such consequence is the Gig Economy. With the gradual improvement in the labour market and the focus of government on localisation, it remains important to analyse the widespread influence of growing gig culture in making India a self-reliant economy. This chapter of the book therefore seeks to review the different components of the gig economy along with the advantages and disadvantages and how gig can contribute towards a localised and self-reliant Indian economy. The chapter also evaluates the regulatory framework of the gig economy in India. The chapter also proposes a conceptual model incorporating various pillars that could serve as an analytical framework for the rapidly increasing number of concepts and policy proposals.

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