The Internet of Things (IoT) introduces a new challenge for Database Management Systems (DBMS). In IoT, large numbers of sensors are used in daily lives. These sensors generate a huge amount of heterogeneous data that needs to be handled by the appropriate DBMS. The IoT has a challenge for the DBMS in evaluating how to store and manipulate a huge amount of heterogeneous data. DBMS can be categorized into two main types: The Relational DBMSs and the Non-relational DBMSs. This paper aims to provide a thorough comparative evaluation of two popular open-source DBMSs: MySQL as a Relational DBMS and MongoDB as a Non-relational DBMS. This comparison is based on evaluating the performance of inserting and retrieving a huge amount of IoT data and evaluating the performance of the two types of databases to work on resources with different specifications in cloud computing. This paper also proposes two prediction models and differentiates between them to estimate the response time in terms of the size of the database and the specifications of the cloud instance. These models help to select the appropriate DBMS to manage and store a certain size of data on an instance with particular specifications based on the estimated response time. The results indicate that MongoDB outperforms MySQL in terms of latency and the database size through increasing the amount of tested data. Moreover, MongoDB can save resources better than MySQL that needs resources with high capabilities to work with less performance.

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Performance Evaluation of IoT Data Management Using MongoDB Versus MySQL Databases in Different Cloud Environments

Performance analysis of NoSQL and relational databases with MongoDB and MySQL

Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

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Data Analytics in Industry 4.0: A Survey.

Due to the exponential growth of NoSQL databases and in addition the circumstance of perusing humongous volumes of information, maximum applications switch RDBMS to NoSQL and pick it as information stockpiling framework. But we all know that RDBMS have several advantages which make it a popular platform across several applications over the decades. Therefore we view the standard problem of converting the RDBMS to NoSQL in reverse approach and we conceptualize a problem where NoSQL is converted back to a RDBMS based system. A generic framework is proposed in this paper so that different NoSQL databases could be converted to RDBMS. This approach is illustrated here using a case study on MongoDB and Neo4j. MongoDB is a document oriented database, fully unstructured and schemaless whereas Neo4j is a graph oriented database, fully unstructured and schemaless. This proves robustness of our proposed mechanism.

A Framework to Convert NoSQL to Relational Model

Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests.

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A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Introduction of structural, semi-structural and unstructured data has tested and challenged the scalability, flexibility and processing ability of the conventional relational database management systems (RDBMS). The new eras of frameworks demand horizontal scaling of the databases. Data with various formats including unstructured data has to be stored and processed in the databases. NoSQL methodologies are answers to these issues. The conventional approaches used in relational and object oriented DBMS cannot adopt the flexible scaling required for today's systems. Likewise, Big Databases have come into existence. But systems with advanced facilities have not emerged, which can support such databases. These enormous databases could not be stored in one physical system. It is recommended to have a distributed framework for this. With a specific goal to enhance the unstructured data processing, a NoSQL framework can be utilized. A document based NoSQL database MongoDB which commonly uses JSON data is discussed to explore the merits of NoSQL databases.

Exploring the merits of nosql: A study based on mongodb

Intelligent processing of unstructured textual data in document based NoSQL databases

The majority of data kept in organizations are in unstructured arrangement including text, sound, video etc. The scope of the keywords is in recovering required documents in a web look or to brief documents for ranking purposes. Keywords are the minutest segments which is used to represent a textual document and are frequently used to point to the most pertinent data contained in texts. Extracting keywords and key phrases is mainly used for identifying content of a document. In this research, we introduce a keyword and key phrase-based extraction approach using MongoDB which is a NoSQL database. By using this new method, documents are queried and ranked with the help of the keyword or keyphrases which is decided and specified by the user in a query. This result can be considered as a measure to decide the Key Performance Indicator of any business organization.

Benymol Jose

Papers

Exploring the merits of nosql: A study based on mongodb

Performance analysis of NoSQL and relational databases with MongoDB and MySQL

Intelligent processing of unstructured textual data in document based NoSQL databases

Keyword and keyphrase based ranking of documents with NoSQL databases in business context

Analysis of Aggregate Functions in Relational Databases and NoSQL Databases