Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models.

LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

We uncover a new class of attacks that can potentially affect any cryptographic protocol. The attack is performed by an adversary that at some point has access to the physical memory of a participant, including all its previous states. In order to protect protocols from such attacks, we introduce a cryptographic primitive that we call erasable memory. Using this primitive, it is possible to implement the essential cryptographic action of forgetting a secret. We show how to use a small erasable memory in order to transform a large non-erasable memory into a large and erasable memory. In practice, this shows how to turn any type of storage device into a storage device that can selectively forget. Moreover, the transformation can be performed using the minimal assumption of the existence of any one-way function, and can be implemented using any block cipher, in which case it is quite efficient. We conclude by suggesting some concrete implementations of small amounts of erasable memory.

How to forget a secret

Our world has evolved to an optimal point of advancement. The extravagant growth has helped in the invention of technologies, industry standards, gadgets, and devices that produce enormous amount of data that all require an essential data management and manipulation system. The data acquired from the various input and output sources are indulged in providing a certain infrastructure are also susceptible to damages if not treated well which may result in loss of data. To overcome this loss, various strategies that run parallel to prevent such loss are being used, one such example is the NoSQL MongoDB. MongoDb is a cross-platform, document oriented database that provides, high performance and easy scalability ensuring effective data management with its prominent feature of auto sharding. Sharding splits the database across multiple servers, increasing the capacity and scalability as required. This feature handles distribution of data in different nodes to maximize disk space and dynamically load balance queries. Partitioning the databases appropriately is a major step that determines the efficiency of sharding. This involves choosing an index of the MongoDB, competently as a shared key for further horizontal scaling of the database. Our current research involves the study of this load balancer. This paper intends to ascertain the need for NoSQL databases in the present situation and emphasize advancement of document-oriented database — MongoDB in particular by describing with a quantitative example that SQL databases are prone to deterioration when data is over loaded and MongoDB comes with inbuilt load balancer which makes it a better solution in applications with high data load. We describe the technology of sharding — auto load balancing feature of MongoDB and hope to provide a comprehensive insight of the process.

A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retriewal operations using a web/android application to explore load balancing — Sharding in MongoDB and its advantages

Efficient cloud service discovery approach based on LDA topic modeling

The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.

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A Comparative Study of Relational and Non-Relational Database Models in a Web- Based Application

In this paper we will try to present a comparative study of non-relational databases and relational databases. We mainly focus our presentation on one implementation of the NoSQL database technology, namely MongoDB, and make a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL. We will also present the advantages of using a non-relational database compared to a relational database, integrated in a forum in the field of personal and professional development. The NoSQL database used to develop the forum is MongoDB, and was chosen from a variety of non-relational databases, thanks to some aspects that we will highlight in this article. The database integration in the framework will also be presented.

A comparative study: MongoDB vs. MySQL

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

In this article, we will describe a method to securely erase sensitive data in fully automated and hidden mode and with remote data destruction capabilities. Compared to other similar technologies, our method has two main advantages. The first one is the ability to run in a fully automated mode, in other words the system is configured once and the computer is protected without requiring any user intervention. The second advantage is the ability to run in a so-called hidden mode, in which the system looks like a different software, for the main purpose of confusing other users. Also, our system can be useful to prevent data loss in stolen laptops, by triggering remote wiping of sensitive data. This is done by overwriting the encryption key of an encrypted volume, that makes the data completely unrecoverable. Some tests and results that show data is not recoverable are also presented at the end of the paper. We will describe the structure and functionality of our system, and some of the most important technologies and algorithms that we have used.

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Data wiping system with fully automated, hidden and remote destruction capabilities

Data exchange between different devices and applications has become a necessity nowadays. Data is no longer stored locally on the device, but in the cloud. In order to communicate with the cloud and exchange data, web services are being used. To keep the communication consistent across different devices and platforms, the data needs to be formatted using a standard data format, such as JSON or XML. This paper compares both standards and provides an in depth analysis of their performance. In order to perform the analysis a web API was built in the PHP framework Laravel, which was then tested with the help of the API development environment called Postman for different number of transferred items.

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Comparative Study of Data Sending Methods for XML and JSON Models

Most applications available nowadays are using an Object Relational Mapper (ORM) to access and save data. The additional layer that is being wrapped over the database induces a performance impact in detrimental of raw SQL queries; on the other side, the advantages of using ORMs by focusing on domain level through application development represent a premise for easier development and simpler code maintenance. In this context, this paper makes a performance comparison between three of the most used ORM technologies from the .NET family: Entity Framework Core 2.2, nHibernate 5.2.3 and Dapper 1.50.5. The main objective of the paper is to make a comparative analysis of the impact that a specific ORM has on application performance when realizing database requests. In order to perform the analysis, a specific testing architecture was designed to ensure the consistency of tests. Performance evaluation for time responses and memory usage for each technology was done using the same CRUD (Create Read Update Delete) operations on the database. The results obtained proved that the decision to use one of another is dependent of the most used type of operation. A comprehensive discussion based on results analysis is done in order to support a decision for choosing a specific ORM by the software engineers in the process of software design and development.

/pdf/performance-comparison-of-crud-methods-using-net-object-2o0jqpfywg.pdf

George Pecherle

Papers

A comparative study: MongoDB vs. MySQL

Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

Data wiping system with fully automated, hidden and remote destruction capabilities

Comparative Study of Data Sending Methods for XML and JSON Models

Performance Comparison of CRUD Methods using NET Object Relational Mappers: A Case Study