Dimitrios Samoladas, Christos N. Karras, Aristeidis Karras, Leonidas Theodorakopoulos, S. Sioutas
In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletion, and search. In this work, we survey B+ Tree, QuadTree, kD Tree, R Tree, and others along with efficient indexing techniques for big data management in order to provide a generic overview of the field to readers. Ultimately, we provide some indexing experiments as per insert operations and response times.
{"title":"Tree Data Structures and Efficient Indexing Techniques for Big Data Management: A Comprehensive Study","authors":"Dimitrios Samoladas, Christos N. Karras, Aristeidis Karras, Leonidas Theodorakopoulos, S. Sioutas","doi":"10.1145/3575879.3575977","DOIUrl":"https://doi.org/10.1145/3575879.3575977","url":null,"abstract":"In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletion, and search. In this work, we survey B+ Tree, QuadTree, kD Tree, R Tree, and others along with efficient indexing techniques for big data management in order to provide a generic overview of the field to readers. Ultimately, we provide some indexing experiments as per insert operations and response times.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127492631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Internet of Vehicles (IoV) has received a great deal of attention in recent years from many researchers. Recently, vehicular edge computing has been a new paradigm to support computation-intensive and latency-sensitive services in IoV. Moreover, with the cellular-vehicle to everything technology, many tasks and applications can be efficiently offloaded to another node for processing. In this paper a novel cluster-based virtual edge computation offloading scheme is proposed, which has as its main objective to efficiently find the most suitable multi-hop neighbor to act as a virtual edge computing (VEC) server for task offloading. The proposed scheme is initially based on the formation and maintenance of multi-hop clusters with high stability, whereas its efficiency is further enhanced by the local/distributed computations taking place where it's possible.
{"title":"A Cluster-based Virtual Edge Computation Offloading Scheme for MEC-enabled Vehicular Networks","authors":"Leontios Sotiriadis, B. Mamalis, G. Pantziou","doi":"10.1145/3575879.3575989","DOIUrl":"https://doi.org/10.1145/3575879.3575989","url":null,"abstract":"Internet of Vehicles (IoV) has received a great deal of attention in recent years from many researchers. Recently, vehicular edge computing has been a new paradigm to support computation-intensive and latency-sensitive services in IoV. Moreover, with the cellular-vehicle to everything technology, many tasks and applications can be efficiently offloaded to another node for processing. In this paper a novel cluster-based virtual edge computation offloading scheme is proposed, which has as its main objective to efficiently find the most suitable multi-hop neighbor to act as a virtual edge computing (VEC) server for task offloading. The proposed scheme is initially based on the formation and maintenance of multi-hop clusters with high stability, whereas its efficiency is further enhanced by the local/distributed computations taking place where it's possible.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132234588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Kalogeras, Vassilios D. Tsakanikas, Ioannis Ballas, Vassilios Aggelopoulos, Vassilios Tampakas
The proliferation of data generation devices, including IoT and edge computing has led to the big data paradigm, which has considerably placed pressure on well-established relational databases during the last decade. Researchers have proposed several alternative database models in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. More specifically, the two frameworks are compared on their capacity to execute community detection algorithms.
{"title":"Community Detection at scale: A comparison study among Apache Spark and Neo4j","authors":"Georgios Kalogeras, Vassilios D. Tsakanikas, Ioannis Ballas, Vassilios Aggelopoulos, Vassilios Tampakas","doi":"10.1145/3575879.3575961","DOIUrl":"https://doi.org/10.1145/3575879.3575961","url":null,"abstract":"The proliferation of data generation devices, including IoT and edge computing has led to the big data paradigm, which has considerably placed pressure on well-established relational databases during the last decade. Researchers have proposed several alternative database models in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. More specifically, the two frameworks are compared on their capacity to execute community detection algorithms.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130920910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Symeonidis, C. Kiourt, N. Kazakis, Evangelos Nerantzis, Tsirliganis Nestor
The livestock meat and its nutrition quality is considered to be an important factor in our daily eating habits giving particular emphasis to health issues. The quality and the nutrition value of a raw-beef-steak, is highly connected with the fat percentage of it. Consequently, the determination of the fat percentage of a raw-beef-steak is crucial for meat producers and consumers as well. In this work, we present a fat mass estimation approach based on a state-of-the-art deep learning pipeline by utilizing a single colored image presenting raw-beef-steak. In order to produce more accurate outcomes, our pipeline combines two U-Nets, one for the background removal and one for the fat extraction. By following popular computational approaches we estimate the fat amount based on the pixels presenting it. To enhance the outcomes of this work, we introduce a new data-set annotated based on the needs of the experiment. The main goal of this work is to provide accurate nutritional information to end-users through novel technologies by exploiting a single image through a mobile application.
{"title":"Fat calculation from raw-beef-steak images through machine learning approaches: an end-to-end pipeline","authors":"Georgios Symeonidis, C. Kiourt, N. Kazakis, Evangelos Nerantzis, Tsirliganis Nestor","doi":"10.1145/3575879.3575975","DOIUrl":"https://doi.org/10.1145/3575879.3575975","url":null,"abstract":"The livestock meat and its nutrition quality is considered to be an important factor in our daily eating habits giving particular emphasis to health issues. The quality and the nutrition value of a raw-beef-steak, is highly connected with the fat percentage of it. Consequently, the determination of the fat percentage of a raw-beef-steak is crucial for meat producers and consumers as well. In this work, we present a fat mass estimation approach based on a state-of-the-art deep learning pipeline by utilizing a single colored image presenting raw-beef-steak. In order to produce more accurate outcomes, our pipeline combines two U-Nets, one for the background removal and one for the fat extraction. By following popular computational approaches we estimate the fat amount based on the pixels presenting it. To enhance the outcomes of this work, we introduce a new data-set annotated based on the needs of the experiment. The main goal of this work is to provide accurate nutritional information to end-users through novel technologies by exploiting a single image through a mobile application.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Kazanidis, G. Terzopoulos, A. Tsinakos, Despoina Georgiou, D. Karampatzakis
Innovative Cultural Experience (ICE) is an Augmented Reality (AR) system for promoting cultural heritage. ICE combines cutting-edge technologies such as an interactive transparent screen, AR, motion sensors and multimedia material in order to provide a unique personal or mass-touring experience, utilizing information based on material and intangible cultural heritage, through narrative scenarios. Part of the ICE system is an interactive transparent box in which an exhibit can be placed. When a user/visitor approaches the exhibit, multimedia information is displayed on the transparent screen of the box, creating an interactive AR experience for the user. Users can interact with the content which can be text, images, videos, 360 images, 360 videos, 3D models or even play games based on the exhibit that is in front of them. By combining the real exhibit with digital information displayed on top, an interactive AR experience is created. Additionally, users can provide feedback by recording and uploading text, images, and videos to the ICE system. ICE is cognitively neutral (domain independent) technology, which makes it useful for a variety of thematic items (from museum exhibits to folk customs, local recipes, etc.) and it can be used also in education, commercial and in the tourist sectors. This paper presents the architecture of the ICE system, and the technologies used for building it. Initial internal evaluation results show that the system is easy to use, and users tend to stay longer in front of the exhibit, interacting with it, thus collecting more information about it.
{"title":"Innovative Cultural Experience (ICE), an Augmented Reality system for promoting cultural heritage","authors":"I. Kazanidis, G. Terzopoulos, A. Tsinakos, Despoina Georgiou, D. Karampatzakis","doi":"10.1145/3575879.3576001","DOIUrl":"https://doi.org/10.1145/3575879.3576001","url":null,"abstract":"Innovative Cultural Experience (ICE) is an Augmented Reality (AR) system for promoting cultural heritage. ICE combines cutting-edge technologies such as an interactive transparent screen, AR, motion sensors and multimedia material in order to provide a unique personal or mass-touring experience, utilizing information based on material and intangible cultural heritage, through narrative scenarios. Part of the ICE system is an interactive transparent box in which an exhibit can be placed. When a user/visitor approaches the exhibit, multimedia information is displayed on the transparent screen of the box, creating an interactive AR experience for the user. Users can interact with the content which can be text, images, videos, 360 images, 360 videos, 3D models or even play games based on the exhibit that is in front of them. By combining the real exhibit with digital information displayed on top, an interactive AR experience is created. Additionally, users can provide feedback by recording and uploading text, images, and videos to the ICE system. ICE is cognitively neutral (domain independent) technology, which makes it useful for a variety of thematic items (from museum exhibits to folk customs, local recipes, etc.) and it can be used also in education, commercial and in the tourist sectors. This paper presents the architecture of the ICE system, and the technologies used for building it. Initial internal evaluation results show that the system is easy to use, and users tend to stay longer in front of the exhibit, interacting with it, thus collecting more information about it.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122576243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Learning word families, that is sets of words with the same root, is important for children of very young ages as it helps them to grow their reading and writing skills in Greek. This is why teaching such word families is one of the main topics of the Modern Greek courses in primary and secondary education of Greece. This paper presents Wordinary, an interactive desktop application that supports teachers in their efforts of teaching Greek word families to elementary school students. The application presented in this paper is the first of its kind that supports the Greek language. Wordinary is meant to be used by teachers to support the design of learning activities related to teaching Greek word families for any word found in the official schoolbooks of the six elementary school grades in Greece. The application was developed following a user-centered design approach and the Python programming language. Two usability evaluation studies were conducted, one user testing study involving end users, and one heuristic evaluation involving HCI experts. The studies found that Wordinary is a usable and useful application. Evaluation results also identified issues for improvement, which led to a redesigned version of Wordinary.
{"title":"Wordinary: A Software Tool for Teaching Greek Word Families to Elementary School Students","authors":"Nikolaos Tzamos, Dimitra Ioannou, C. Katsanos","doi":"10.1145/3575879.3575992","DOIUrl":"https://doi.org/10.1145/3575879.3575992","url":null,"abstract":"Learning word families, that is sets of words with the same root, is important for children of very young ages as it helps them to grow their reading and writing skills in Greek. This is why teaching such word families is one of the main topics of the Modern Greek courses in primary and secondary education of Greece. This paper presents Wordinary, an interactive desktop application that supports teachers in their efforts of teaching Greek word families to elementary school students. The application presented in this paper is the first of its kind that supports the Greek language. Wordinary is meant to be used by teachers to support the design of learning activities related to teaching Greek word families for any word found in the official schoolbooks of the six elementary school grades in Greece. The application was developed following a user-centered design approach and the Python programming language. Two usability evaluation studies were conducted, one user testing study involving end users, and one heuristic evaluation involving HCI experts. The studies found that Wordinary is a usable and useful application. Evaluation results also identified issues for improvement, which led to a redesigned version of Wordinary.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christos Liakos, Aimilios Christos Panagopoulos, P. Karkazis
In the context of the Internet of Things and Industry 4.0 innovative technologies have been emerged namely Digital Twins, Business Process Management frameworks, Big Data analysis, and they are now available and ready to be used in order to create new management models. These models implement the interconnection of the structural elements of the production chain (machines, sensors, humans, etc.), by gathering and processing useful information, targeting on automated decisions, problems solving in real-time, and the flexible adjustment of the production process. In the current work, we present and evaluate a management architecture of a business process management flow to identify the alterations on digitally replicated industrial machinery and the impact they have on a production line.
{"title":"Smart Business Processing in Industry 4.0","authors":"Christos Liakos, Aimilios Christos Panagopoulos, P. Karkazis","doi":"10.1145/3575879.3575963","DOIUrl":"https://doi.org/10.1145/3575879.3575963","url":null,"abstract":"In the context of the Internet of Things and Industry 4.0 innovative technologies have been emerged namely Digital Twins, Business Process Management frameworks, Big Data analysis, and they are now available and ready to be used in order to create new management models. These models implement the interconnection of the structural elements of the production chain (machines, sensors, humans, etc.), by gathering and processing useful information, targeting on automated decisions, problems solving in real-time, and the flexible adjustment of the production process. In the current work, we present and evaluate a management architecture of a business process management flow to identify the alterations on digitally replicated industrial machinery and the impact they have on a production line.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121996296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eleni Tsalera, A. Papadakis, M. Samarakou, I. Voyiatzis
Audio datasets support the training and validation of Machine Learning algorithms in audio classification problems. Such datasets include different, arbitrarily chosen audio classes. We initially investigate a unifying approach, based on the mapping of audio classes according to the Audioset ontology. Using the ESC-10 audio dataset, a tree-like representation of its classes is created. In addition, we employ an audio similarity calculation tool based on the values of extracted features (spectrum centroid, the spectrum flux and the spectral roll-off). This way the audio classes are connected both semantically and in feature-based manner. Employing the same dataset, ESC-10, we perform sound classification using CNN-based algorithms, after transforming the sound excerpts into images (based on their Mel spectrograms). The YAMNet and VGGish networks are used for audio classification and the accuracy reaches 90%. We extend the classification algorithm with segmentation logic, so that it can be applied into more complex sound excerpts, where multiple sound types are included in a sequential and/or overlapping manner. Quantitative metrics are defined on the behavior of the combined segmentation and segmentation functionality, including two key parameters for the merging operation, the minimum duration of the identified sounds and the intervals. The qualitative metrics are related to the number of sound identification events for a concatenated sound excerpt of the dataset and per each sound class. This way the segmentation logic can operate in a fine- and coarse-grained manner while the dataset and the individual sound classes are characterized in terms of clearness and distinguishability.
{"title":"CNN-based Segmentation and Classification of Sound Streams under realistic conditions","authors":"Eleni Tsalera, A. Papadakis, M. Samarakou, I. Voyiatzis","doi":"10.1145/3575879.3576020","DOIUrl":"https://doi.org/10.1145/3575879.3576020","url":null,"abstract":"Audio datasets support the training and validation of Machine Learning algorithms in audio classification problems. Such datasets include different, arbitrarily chosen audio classes. We initially investigate a unifying approach, based on the mapping of audio classes according to the Audioset ontology. Using the ESC-10 audio dataset, a tree-like representation of its classes is created. In addition, we employ an audio similarity calculation tool based on the values of extracted features (spectrum centroid, the spectrum flux and the spectral roll-off). This way the audio classes are connected both semantically and in feature-based manner. Employing the same dataset, ESC-10, we perform sound classification using CNN-based algorithms, after transforming the sound excerpts into images (based on their Mel spectrograms). The YAMNet and VGGish networks are used for audio classification and the accuracy reaches 90%. We extend the classification algorithm with segmentation logic, so that it can be applied into more complex sound excerpts, where multiple sound types are included in a sequential and/or overlapping manner. Quantitative metrics are defined on the behavior of the combined segmentation and segmentation functionality, including two key parameters for the merging operation, the minimum duration of the identified sounds and the intervals. The qualitative metrics are related to the number of sound identification events for a concatenated sound excerpt of the dataset and per each sound class. This way the segmentation logic can operate in a fine- and coarse-grained manner while the dataset and the individual sound classes are characterized in terms of clearness and distinguishability.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114075415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Students attending Higher Education Institutions (HEIs) of Vocational Educational and Training (VET) are faced with a variety of complex decisions and procedures. To provide students with more sustained and personalized advising, many HEIs/VETs use academic advising systems and tools as a way to minimize costs and streamline their advising services. Furthermore, it is quite common for educational programs to include and combine educational content from different educational providers, while they are managed and executed on different platforms. Therefore, the ability to develop conceptual models for personalized learning based on educational content produced by heterogeneous educational service providers is a pressing need to address. A similar issue is confronted when deploying applications across diverse cloud computing platforms. A solution that is provided in these situations is the development of specialized languages for defining the topology and the orchestration of applications such as TOSCA, CAMP, Open-CSA, etc. In this paper, we propose to use similar conceptual models for modelling heterogeneous educational offerings toward personalized learning, which are presented along with the overall architecture of a system, named cc-coach, able to support these concepts. Further, this paper is a proposal for the standardization efforts needed for creating a multi-vendor educational ecosystem with diverse stakeholders, able to support personalized learning at various levels.
{"title":"Using TOSCA language to model personalized educational content: Introducing eduTOSCA","authors":"P. Fitsilis, Omiros Iatrellis, Paraskevi Tsoutsa","doi":"10.1145/3575879.3576017","DOIUrl":"https://doi.org/10.1145/3575879.3576017","url":null,"abstract":"Students attending Higher Education Institutions (HEIs) of Vocational Educational and Training (VET) are faced with a variety of complex decisions and procedures. To provide students with more sustained and personalized advising, many HEIs/VETs use academic advising systems and tools as a way to minimize costs and streamline their advising services. Furthermore, it is quite common for educational programs to include and combine educational content from different educational providers, while they are managed and executed on different platforms. Therefore, the ability to develop conceptual models for personalized learning based on educational content produced by heterogeneous educational service providers is a pressing need to address. A similar issue is confronted when deploying applications across diverse cloud computing platforms. A solution that is provided in these situations is the development of specialized languages for defining the topology and the orchestration of applications such as TOSCA, CAMP, Open-CSA, etc. In this paper, we propose to use similar conceptual models for modelling heterogeneous educational offerings toward personalized learning, which are presented along with the overall architecture of a system, named cc-coach, able to support these concepts. Further, this paper is a proposal for the standardization efforts needed for creating a multi-vendor educational ecosystem with diverse stakeholders, able to support personalized learning at various levels.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed.
{"title":"An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture","authors":"Vasiliki Giannou, B. Mamalis","doi":"10.1145/3575879.3576013","DOIUrl":"https://doi.org/10.1145/3575879.3576013","url":null,"abstract":"Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116623179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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