Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Algorithmic Data Management
share announcement

Algorithmic Data Management

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: closed (16 May 2021) | Viewed by 12201

Special Issue Editors

Prof. Dr. Spyros Sioutas

E-Mail Website
Guest Editor
Department of Computer Engineering and Informatics, University of Patras, 26504 Rio Achaia, Greece
Interests: multidimensional data structures; decentralized systems for big data management; indexing; query processing and query optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Kanavos

E-Mail Website
Guest Editor
Department of Computer Engineering and Informatics, University of Patras, 26504 Rio Achaia, Greece
Interests: data management for smart city governance; artificial intelligence for smart cities; big data in the context of urban sustainability; machine learning for advanced digital manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Data management corresponds to a critical problem in the era of cloud computing. Thousands of petabytes of data are stored up in the cloud and operations such as the synchronization of current versions of files occur billions of times a day. The bandwidth costs are enormous, thus justifying the need for highly efficient synchronization algorithms.

Numerous aspects of modern data management systems concern theoretical, design, and implementation issues. This Special Issue is particularly interested in novel algorithms in the context of decentralized systems for big data management, distributed data structures and p2p overlays, query processing and query optimization in NoSQL databases, novel cloud infrastructures, and experimental work that evaluates contemporary cloud-storage approaches and pertinent applications. Demonstration manuscripts that discuss successful system developments, as well as experience/use-case articles and high-quality survey papers, are also welcomed. Contributions may span a wide range of algorithms for modeling, practices for building, and techniques for evaluating operations and services in a variety of decentralized systems, including, but not limited to, cloud computing platforms, edge computing platforms, fog computing platforms, datacenters, cloud-storage options, cloud data management, non-traditional key-value stores on the cloud, and HPC architectures.

Submissions should focus on aspects of decentralized systems for big data management and their algorithms, including (but not limited to) the following areas:

  • Big data and the cloud
  • Machine learning techniques on edge, fog, and the cloud
  • Analysis of algorithms and distributed data structures
  • Caching and load balancing
  • Resource management and scheduling
  • Data center and infrastructure management
  • Privacy, security, and anonymization
  • Cloud-based applications
  • Virtualization and containers
  • Performance models
  • Novel programming models
  • Storage management
  • Fog and edge computing
  • Economic models and pricing
  • Energy and power management
  • Network management and techniques

Dr. Spyros Sioutas
Dr. Andreas Kanavos
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. Algorithms 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 1600 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 (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1572 KiB  
Article
Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
by Christina Koutroumpina, Spyros Sioutas, Stelios Koutroubinas and Kostas Tsichlas
Algorithms 2021, 14(11), 311; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110311 - 25 Oct 2021
Cited by 3 | Viewed by 1638
Abstract
The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing [...] Read more.
The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms. Full article
(This article belongs to the Special Issue Algorithmic Data Management)
►▼ Show Figures

Figure 1

23 pages, 886 KiB  
Article
Towards Interactive Analytics over RDF Graphs
by Maria-Evangelia Papadaki, Nicolas Spyratos and Yannis Tzitzikas
Algorithms 2021, 14(2), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/a14020034 - 25 Jan 2021
Cited by 12 | Viewed by 2506
Abstract
The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the [...] Read more.
The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data based on a high-level functional query language, called HIFUN. According to that language, each analytical query is considered to be a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this paper, we investigate how HIFUN can be used for easing the formulation of analytic queries over RDF data. We detail the applicability of HIFUN over RDF, as well as the transformations of data that may be required, we introduce the translation rules of HIFUN queries to SPARQL and we describe a first implementation of the proposed model. Full article
(This article belongs to the Special Issue Algorithmic Data Management)
►▼ Show Figures

Figure 1

23 pages, 620 KiB  
Article
Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark
by Elias Dritsas, Andreas Kanavos, Maria Trigka, Gerasimos Vonitsanos, Spyros Sioutas and Athanasios Tsakalidis
Algorithms 2020, 13(8), 182; https://0-doi-org.brum.beds.ac.uk/10.3390/a13080182 - 28 Jul 2020
Cited by 4 | Viewed by 3644
Abstract
Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed [...] Read more.
Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection. Full article
(This article belongs to the Special Issue Algorithmic Data Management)
►▼ Show Figures

Figure 1

15 pages, 1180 KiB  
Article
Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
by Christos Makris, Georgios Pispirigos and Michael Angelos Simos
Algorithms 2020, 13(7), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/a13070160 - 01 Jul 2020
Cited by 4 | Viewed by 3471
Abstract
Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial [...] Read more.
Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance. Full article
(This article belongs to the Special Issue Algorithmic Data Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop