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Algorithms
Special Issues
Mining Humanistic Data 2019
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Mining Humanistic Data 2019

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 27468

Special Issue Editors

Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Makris

E-Mail Website
Guest Editor
Department of Computer Engineering and Informatics, University of Patras, 26504 Rio Achaia, Greece
Interests: data structures; information retrieval; data mining; bioinformatics; string algorithmic; computational geometry; multimedia databases; internet technologies
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
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

Special Issue Information

Dear Colleagues,

The abundance of available data that is retrieved from or is related to the areas of Humanities and the human condition challenges the research community in processing and analyzing it. The aim of MHDW and, by extension, of this Special Issue is thus two-fold: to extract knowledge that will help one understand human behavior, creativity, ways of thinking, reasoning, learning, decision making, socializing, and even biological processes, and to exploit this extracted knowledge by incorporating it into intelligent systems that will support humans in their everyday activities.

The nature of humanistic data can be multimodal, semantically heterogeneous, dynamic, time- and space-dependent, and highly complicated. Translating humanistic information, e.g., behavior, state of mind, artistic creation, linguistic utterance, learning, and genomic information into numerical or categorical low-level data is a significant challenge on its own. New techniques, appropriate to deal with these types of data, need to be proposed and existing ones adapted.

In the same manner, an important aspect of the humanities is centered on managing, processing, and computationally analyzing biological and biomedical data. Hence, one of the aims of this Special Issue is to also attract researchers that are interested in designing, developing, and applying efficient data and text mining techniques for discovering the underlying knowledge existing in biomedical data, such as sequences, gene expressions, and pathways.

This Special Issue aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing data matching, fusion, mining and knowledge discovery, and management techniques (like decision rules, decision trees, association rules, ontologies and alignments, clustering, filtering, learning, classifier systems, neural networks, support vector machines, preprocessing, postprocessing, feature selection, and visualization techniques) to data derived from all areas of the humanities, e.g., linguistic, historical, behavioral, psychological, artistic, musical, educational, social, etc., as well as from domains related to the human condition such as bioinformatics.

Dr. Phivos Mylonas
Dr. Christos Makris
Dr. Andreas Kanavos
Dr. Spyros Sioutas
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.

Keywords

  • humanistic data
  • behavior
  • state of mind
  • artistic creation
  • linguistic utterance
  • learning
  • genomic
  • data matching
  • data fusion
  • data mining
  • knowledge discovery
  • data management techniques

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Published Papers (5 papers)

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24 pages, 552 KiB  
Article
Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
by Athanasios Alexopoulos, Georgios Drakopoulos, Andreas Kanavos, Phivos Mylonas and Gerasimos Vonitsanos
Algorithms 2020, 13(3), 71; https://doi.org/10.3390/a13030071 - 24 Mar 2020
Cited by 15 | Viewed by 4860
Abstract
At the dawn of the 10V or big data data era, there are a considerable number of sources such as smart phones, IoT devices, social media, smart city sensors, as well as the health care system, all of which constitute but a small [...] Read more.
At the dawn of the 10V or big data data era, there are a considerable number of sources such as smart phones, IoT devices, social media, smart city sensors, as well as the health care system, all of which constitute but a small portion of the data lakes feeding the entire big data ecosystem. This 10V data growth poses two primary challenges, namely storing and processing. Concerning the latter, new frameworks have been developed including distributed platforms such as the Hadoop ecosystem. Classification is a major machine learning task typically executed on distributed platforms and as a consequence many algorithmic techniques have been developed tailored for these platforms. This article extensively relies in two ways on classifiers implemented in MLlib, the main machine learning library for the Hadoop ecosystem. First, a vast number of classifiers is applied to two datasets, namely Higgs and PAMAP. Second, a two-step classification is ab ovo performed to the same datasets. Specifically, the singular value decomposition of the data matrix determines first a set of transformed attributes which in turn drive the classifiers of MLlib. The twofold purpose of the proposed architecture is to reduce complexity while maintaining a similar if not better level of the metrics of accuracy, recall, and F 1 . The intuition behind this approach stems from the engineering principle of breaking down complex problems to simpler and more manageable tasks. The experiments based on the same Spark cluster indicate that the proposed architecture outperforms the individual classifiers with respect to both complexity and the abovementioned metrics. Full article
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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16 pages, 4067 KiB  
Article
A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends
by Georgios Alexandridis, Yorghos Voutos, Phivos Mylonas and George Caridakis
Algorithms 2020, 13(3), 59; https://doi.org/10.3390/a13030059 - 4 Mar 2020
Cited by 6 | Viewed by 6152
Abstract
Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of [...] Read more.
Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods. Full article
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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19 pages, 679 KiB  
Article
Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases
by Elias Dritsas, Andreas Kanavos, Maria Trigka, Spyros Sioutas and Athanasios Tsakalidis
Algorithms 2019, 12(12), 266; https://doi.org/10.3390/a12120266 - 11 Dec 2019
Cited by 7 | Viewed by 3746
Abstract
The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as [...] Read more.
The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1 . To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work. Full article
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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17 pages, 1210 KiB  
Article
Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
by Georgios Paraskevopoulos, Evaggelos Spyrou, Dimitrios Sgouropoulos, Theodoros Giannakopoulos and Phivos Mylonas
Algorithms 2019, 12(5), 108; https://doi.org/10.3390/a12050108 - 20 May 2019
Cited by 4 | Viewed by 6812
Abstract
In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and [...] Read more.
In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works. Full article
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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11 pages, 988 KiB  
Article
Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
by Ioannis E. Livieris
Algorithms 2019, 12(4), 85; https://doi.org/10.3390/a12040085 - 22 Apr 2019
Cited by 14 | Viewed by 4877
Abstract
During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently [...] Read more.
During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning. Full article
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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