In the present era, the use of the Internet has drastically increased in the sharing of digital information. In this case, the digital information is stored using cloud technology or nosql databases. However, there is...
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In the present era, the use of the Internet has drastically increased in the sharing of digital information. In this case, the digital information is stored using cloud technology or nosql databases. However, there is a significant challenge in protecting and managing the cloud and nosql-based data and extracting required information from these sources while maintaining the actual information. The network traffic has also increased significantly, which requires more memory and sufficient systems to manage and monitor the influx of Big Data. Traditional relational databases face issues in managing and securing the cloud-based dynamic data generated from various sources. nosql databases have recently been used to store and manage dynamic data effectively. However, there are security and privacy issues with the nosql databases, which remain challenging to provide. Consequently, in the present study, we propose a novel algorithm that enhances the security of the nosql databases and predicts its success rate. Initially, we implemented the Fernet data masking algorithm to secure the nosql database. Then, the secured data is classified and predicted using an innovative proposed method called the Ensemble Bagging Classifier-Logistic Regression (EBC-LR) to validate the accuracy of the secured nosql database. The experimental outcomes depict that our proposed algorithm achieves 85 percent accuracy, better than traditional methods in enhancing the security of nosql databases. Our proposed algorithm can effectively predict secure standard databases with the highest success rate.
The rapid development of information technology has necessitated the management of large volumes of data in modern society, leading to the emergence of nosql databases (e.g., MongoDB). To meet the huge demand for effi...
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ISBN:
(纸本)9798400707056
The rapid development of information technology has necessitated the management of large volumes of data in modern society, leading to the emergence of nosql databases (e.g., MongoDB). To meet the huge demand for efficient data management and query, optimizing the performance of these databases has become crucial. Currently, some reinforcement learning-based methods have been used to improve the efficiency of databases by tuning customizable database configurations. However, these methods have limitations: they ignore operating system configurations, incur high training costs with more knobs, and adapt poorly to new environments with varying workloads and hardware. To address these issues, we propose a novel and effective approach named CTUNER for the online performance tuning of nosql databases. CTUNER skips cold start by Bayesian optimization-based learning, and improves the exploitation strategy of the Twin Delayed Deep Deterministic Policy Gradient (TD3) model with causal inference. Practical implementation and experimental evaluations on three prominent nosql databases show that CTUNER can find a better configuration at the same time cost than state-of-the-art approaches, with up to a 27.4% improvement in throughput and up to 13.2% reduction in 95%-tail latency. Moreover, we introduce meta-learning to enhance the adaptability of CTUNER and confirm that it is able to reliably improve performance under new environments and workloads.
With the continuous development of science and technology, we have fully entered the information age, people's entertainment life is becoming more and more abundant, and the Internet has also provided people with ...
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With the continuous development of science and technology, we have fully entered the information age, people's entertainment life is becoming more and more abundant, and the Internet has also provided people with a lot of convenience. The advent of the Internet age means that more and more various kinds of data are appearing, and the situation is becoming more and more abundant. The use of traditional relational databases can no longer store these data, nor can it be queried. With the continuous development of voice technology, database technology based on nosql has become a research hotspot. The availability of nosql databases is very high, the scalability is also very high, and the efficiency is very high when processing data. Based on the development of 5G networks and the development of big data technology, this research proposes a brand-new business architecture. This architecture can use the network to store data on the basis of massive data. At the same time, we also described the business scenario, providing a new idea for more intelligent education services. We use this model for teaching in schools, and we can transmit some spoken language resources to the school through the Internet for students to use for learning. Nowadays, the application range of AI technology and intelligent technology has become more and more extensive, and these technologies have also been applied in the education field. We can apply this brand-new technology in teaching to promote the development of teaching and improve students' enthusiasm and learning effect.
Many big data applications such as smart transportation, healthcare, and e-commerce need to store and query large collections of small XML documents, which has become a fundamental problem. However, existing solutions...
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Many big data applications such as smart transportation, healthcare, and e-commerce need to store and query large collections of small XML documents, which has become a fundamental problem. However, existing solutions are inadequate to deliver satisfactory query performance in such circumstances. In this paper, we propose a framework named XML2HBase to address this problem using HBase, a widely deployed nosql database. Within this framework, we design a novel encoding scheme called Pathed-Dewey Order and a two-layer mapping method to store XML documents in HBase tables. XML queries, which are represented as XPath expressions, are evaluated through their translation into queries over HBase tables. Based on an in-depth analysis of the characteristics of the proposed approach, we design and integrate four optimization strategies to reduce storage space and query response time. Extensive experiments on two well-known XML benchmarks demonstrate the superior performance of XML2HBase over three state-of-the-art methods. (C) 2021 Elsevier Inc. All rights reserved.
Since its emergence, nosql databases have firmly established themselves as an indispensable software component of modern cloud-native applications. However, it also becomes increasingly challenging to perform critical...
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ISBN:
(纸本)9781665406017
Since its emergence, nosql databases have firmly established themselves as an indispensable software component of modern cloud-native applications. However, it also becomes increasingly challenging to perform critical management tasks such as troubleshooting unexpected performance problems. This is due to the ever-increasing diversity and specialization of nosql databases that make it difficult to observe the internal activities. To address these challenges, we have designed and built a technique for introspecting nosql databases. Our technique traces system call sequences of key operations under controlled workloads and filters scaling patterns from constant components. Novel algorithms are developed to uncover repeating patterns of system calls from massive amounts of traces and filter out background noise with high efficiency. The evaluation shows that our technique can greatly enhance the visibility into the nosql databases enabling us to diagnose performance problems or gain insights into internal activities.
In the realm of the Internet of Things (IoT), the rapid evolution and deployment of diverse applications necessitate robust and scalable database solutions. This paper explores the integration of Model-Driven Architec...
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The amount of data on the Web and the number of users accessing those data drastically grows every year. Web-based services have to be able to receive and process those requirements within the shortest possible time. ...
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ISBN:
(纸本)9781510883888
The amount of data on the Web and the number of users accessing those data drastically grows every year. Web-based services have to be able to receive and process those requirements within the shortest possible time. Additionally, they have to manage sudden and unpredictable peaks of user requirements for accessing the data. To tackle these problems, nosql databases have been designed to provide scalability and good performance. However, it is important to know in advance the number of resources (i.e. computers, network capacity) to efficiently deploy a database application. In this work, we propose a simulator for a nosql database named MongoDB. Our simulator aims to detect saturation levels of the resources to ensure an efficient execution of MongoDB upon different users demands and upon failures of resources.
The trajectory data often contains a large amount of information. So effective storage management is needed to analyze and apply the trajectory data. This paper proposes a distributed storage strategy for the characte...
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ISBN:
(纸本)9781538691540
The trajectory data often contains a large amount of information. So effective storage management is needed to analyze and apply the trajectory data. This paper proposes a distributed storage strategy for the characteristics of trajectory data and the requirement of data balance among distributed nodes. A trajectory data is converted into a rectangle by the MBR to ensure data integrity;and the center of the MBR is calculated for K-Means clustering;then, this paper divides the clustering results by grid and merges the grids with high overlap rate. Finally, a data partitioning strategy is used to store the divided data in each nodes so that the data volume of each node is balance. Experiments show that the proposed distributed storage strategy can effectively take into account the integrity of trajectory data, spatial proximity, data structure flexibility to achieve data balance among each nodes based on distributed nosql database.
In order to better guarantee the security of network privacy information,a method of network privacy information security management based on nosql database is ***,the network privacy information security status is de...
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ISBN:
(纸本)9781728150505
In order to better guarantee the security of network privacy information,a method of network privacy information security management based on nosql database is ***,the network privacy information security status is detected and evaluated based on the principle of neural *** to the evaluation results,the network privacy information encryption processing method is optimized,and the intensity of information encryption is strengthened to achieve the management of network privacy information ***,the experiment proves that the nosql database-based network privacy information security management method has higher security and effectiveness than the traditional method.
nosql database system is widely used in Big Data environment for its high performance and scalability. Due to the width of usage of nosql database, it is inevitable to store sensitive data in it. Utilizing homomorphic...
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ISBN:
(纸本)9781538679753
nosql database system is widely used in Big Data environment for its high performance and scalability. Due to the width of usage of nosql database, it is inevitable to store sensitive data in it. Utilizing homomorphic encryption, sensitive data can be encrypted and calculated in the form of ciphertext to preserve privacy. Although calculating on homomorphically encrypted data is possible, searching on these data is not simple for the calculating results are encrypted and the search engine doesn't know what data should be sent back to the requester. A homomorphic searching scheme is proposed to handle this problem. In this scheme, the searching function is divided into two parts named homomorphic comparing and homomorphic decryption. The homomorphic comparing is executed by the encrypted data holder, and the homomorphic decryption is executed by the key holder. With this scheme, encrypted data holder can only get encrypted data and calculate the encrypted differences of these data, and the key holder can only get the encrypted differences and decrypt them. A sample implementation of the scheme with MongoDB is given. Analysis and experimental results show that the proposed scheme is secure and practical.
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