Advances in Database Engineered Applications 2023

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 10846

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IUT de Bayonne, 2 Allée du Parc de Montaury, Campus Montaury/Anglet, Université de Pau et des Pays de l'Adour (UPPA), Office 200, 64600 Anglet, France
Interests: multimedia information retrieval; XML and RSS similarity; access control models; digital ecosystems
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School of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus
Interests: data management; data mining; data science; scientometrics
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Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: software agents; data mining; case-based reasoning; learning technologies; software engineering; social networks
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Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, 30172 Venice, Italy
Interests: spatio-temporal data warehouses; methods for privacy preserving disclosure of spatio-temporal data

Special Issue Information

Dear Colleagues,

The 27th International Database Engineering & Applications Symposium (IDEAS-II-2023) will be held in Heraklion, Crete, Greece on May 5–7 2023. This conference is an international forum for data engineering researchers, practitioners, developers, and application users to explore revolutionary ideas and results, and to exchange techniques, tools, and experiences. For more information, please visit the following link: https://conferences.sigappfr.org/ideas2023/.

The authors of a number of high-quality full papers will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Computers, published by MDPI, in open access format. The selection of these papers will be based on their ratings in the conference review process, the quality of the presentation during the conference, and the expected impact on the research community. Each submission to this Special Issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases and a change in the title, abstract, and keywords. These extended submissions will undergo a peer-review process according to the journal’s rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality reviewing process.

Prof. Dr. Richard Chbeir
Prof. Dr. Yannis Manolopoulos
Prof. Dr. Mirjana Ivanović
Dr. Claudio Silvestri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Editorial

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3 pages, 155 KiB  
Editorial
Special Issue on Advances in Database Engineered Applications
by Richard Chbeir, Mirjana Ivanovic, Yannis Manolopoulos and Claudio Silvestri
Computers 2024, 13(3), 77; https://doi.org/10.3390/computers13030077 - 14 Mar 2024
Viewed by 1066
Abstract
The 27th International Database Engineering and Applications Symposium (IDEAS-2023) was held in Heraklion, Crete, Greece, on 5–7 May 2023 [...] Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)

Research

Jump to: Editorial

33 pages, 8073 KiB  
Article
The Doubly Linked Tree of Singly Linked Rings: Providing Hard Real-Time Database Operations on an FPGA
by Simon Lohmann and Dietmar Tutsch
Computers 2024, 13(1), 8; https://doi.org/10.3390/computers13010008 - 24 Dec 2023
Viewed by 1677
Abstract
We present a hardware data structure specifically designed for FPGAs that enables the execution of the hard real-time database CRUD operations using a hybrid data structure that combines trees and rings. While the number of rows and columns has to be limited for [...] Read more.
We present a hardware data structure specifically designed for FPGAs that enables the execution of the hard real-time database CRUD operations using a hybrid data structure that combines trees and rings. While the number of rows and columns has to be limited for hard real-time execution, the actual content can be of any size. Our structure restricts full navigational freedom to every but the leaf layer, thus keeping the memory overhead for the data stored in the leaves low. Although its nodes differ in function, all have exactly the same size and structure, reducing the number of cascaded decisions required in the database operations. This enables fast and efficient hardware implementation on FPGAs. In addition to the usual comparison with known data structures, we also analyze the tradeoff between the memory consumption of our approach and a simplified version that is doubly linked in all layers. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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20 pages, 3310 KiB  
Article
Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments
by Rohit Mittal, Geeta Rani, Vibhakar Pathak, Sonam Chhikara, Vijaypal Singh Dhaka, Eugenio Vocaturo and Ester Zumpano
Computers 2023, 12(12), 250; https://doi.org/10.3390/computers12120250 - 1 Dec 2023
Cited by 3 | Viewed by 1370
Abstract
The automation industry faces the challenge of avoiding interference with obstacles, estimating the next move of a robot, and optimizing its path in various environments. Although researchers have predicted the next move of a robot in linear and non-linear environments, there is a [...] Read more.
The automation industry faces the challenge of avoiding interference with obstacles, estimating the next move of a robot, and optimizing its path in various environments. Although researchers have predicted the next move of a robot in linear and non-linear environments, there is a lack of precise estimation of sectorial error probability while moving a robot on a curvy path. Additionally, existing approaches use visual sensors, incur high costs for robot design, and ineffective in achieving motion stability on various surfaces. To address these issues, the authors in this manuscript propose a low-cost and multisensory robot capable of moving on an optimized path in diverse environments with eight degrees of freedom. The authors use the extended Kalman filter and unscented Kalman filter for localization and position estimation of the robot. They also compare the sectorial path prediction error at different angles from 0° to 180° and demonstrate the mathematical modeling of various operations involved in navigating the robot. The minimum deviation of 1.125 cm between the actual and predicted path proves the effectiveness of the robot in a real-life environment. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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18 pages, 6588 KiB  
Article
Meshfree Interpolation of Multidimensional Time-Varying Scattered Data
by Vaclav Skala and Eliska Mourycova
Computers 2023, 12(12), 243; https://doi.org/10.3390/computers12120243 - 21 Nov 2023
Viewed by 1519
Abstract
Interpolating and approximating scattered scalar and vector data is fundamental in resolving numerous engineering challenges. These methodologies predominantly rely on establishing a triangulated structure within the data domain, typically constrained to the dimensions of 2D or 3D. Subsequently, an interpolation or approximation technique [...] Read more.
Interpolating and approximating scattered scalar and vector data is fundamental in resolving numerous engineering challenges. These methodologies predominantly rely on establishing a triangulated structure within the data domain, typically constrained to the dimensions of 2D or 3D. Subsequently, an interpolation or approximation technique is employed to yield a smooth and coherent outcome. This contribution introduces a meshless methodology founded upon radial basis functions (RBFs). This approach exhibits a nearly dimensionless character, facilitating the interpolation of data evolving over time. Specifically, it enables the interpolation of dispersed spatio-temporally varying data, allowing for interpolation within the space-time domain devoid of the conventional “time-frames”. Meshless methodologies tailored for scattered spatio-temporal data hold applicability across a spectrum of domains, encompassing the interpolation, approximation, and assessment of data originating from various sources, such as buoys, sensor networks, tsunami monitoring instruments, chemical and radiation detectors, vessel and submarine detection systems, weather forecasting models, as well as the compression and visualization of 3D vector fields, among others. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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18 pages, 398 KiB  
Article
Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration
by Benjamin Warnke, Stefan Fischer and Sven Groppe
Computers 2023, 12(10), 210; https://doi.org/10.3390/computers12100210 - 17 Oct 2023
Cited by 1 | Viewed by 1784
Abstract
Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPARQL is particularly [...] Read more.
Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPARQL is particularly well-suited to the IoT environment because it can handle various data structures. Due to the flexibility of data structures, however, more data have to be joined again during processing. Therefore, a good join order is crucial as it significantly impacts the number of intermediate results. However, computing the best linking order is an NP-hard problem because the total number of possible linking orders increases exponentially with the number of inputs to be combined. In addition, there are different definitions of optimal join orders. Machine learning uses stochastic methods to achieve good results even with complex problems quickly. Other DBMSs also consider reducing network traffic but neglect the network topology. Network topology is crucial in IoT as devices are not evenly distributed. Therefore, we present new techniques for collaboration between routing, application, and machine learning. Our approach, which pushes the operators as close as possible to the data source, minimizes the produced network traffic by 10%. Additionally, the model can reduce the number of intermediate results by a factor of 100 in comparison to other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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52 pages, 1408 KiB  
Article
Specification Mining over Temporal Data
by Giacomo Bergami, Samuel Appleby and Graham Morgan
Computers 2023, 12(9), 185; https://doi.org/10.3390/computers12090185 - 14 Sep 2023
Cited by 1 | Viewed by 1225
Abstract
Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic [...] Read more.
Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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10 pages, 259 KiB  
Article
Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset
by László Göcs and Zsolt Csaba Johanyák
Computers 2023, 12(8), 147; https://doi.org/10.3390/computers12080147 - 25 Jul 2023
Cited by 1 | Viewed by 1443
Abstract
Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques [...] Read more.
Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics. Full article
(This article belongs to the Special Issue Advances in Database Engineered Applications 2023)
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