Publications – AI-Lab

Santipantakis, Georgios M; Vouros, George A; Andrienko, Natalia; Martinez, Miguel Garcia; Fuchs, Georg; Andrienko, Gennady; Garcia, Jose Manuel Cordero; Doulkeridis, Christos; Vlachou, Akrivi

Specification of Semantic Trajectories Supporting Data Transformations for Analytics: The datAcron Ontology Proceeding

2017.

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Andrienko, Natalia; et al.,

Visual exploration of movement and event data with interactive time masks Journal Article

Visual Informatics, On Line , 2017.

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Vouros, George A

Learning Conventions via Social Reinforcement Learning in Complex and Open Settings Proceeding

Sao Paulo, Brazil, 2017.

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Kofinas, Panagiotis; Doltsinis, Stefanos; Dounis, Anastasios; Vouros, George A

A Reinforcement Learning Approach for MPPT Control Method of Photovoltaic Sources Renewable Energy Journal Article

Reniewable Energy, 108 , 2017.

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2016

Kofinas, Panagiotis; Vouros, George A; Dounis, Anastasios I

Energy Management in Solar Microgrid via Reinforcement Learning. Conference

SETN 2016, Springer Springer, Thessaloniki, Greece, 2016, ISBN: 978-145033734-2.

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Rovatsos, Michael; Vouros, George A; á, Vicente Juli

Multi-Agent Systems and Agreement Technologies – 13th European Conference, EUMAS 2015, and Third International Conference, AT 2015 Book

Springer, Athens, 2016, ISSN: ISBN 978-3-319-33508-7.

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Vouros:, George A; Bassiliades, Nick; Bikakis, Antonis; Vrakas, Dimitris; Vlahavas, Ioannis P

Proceedings of the 9th Hellenic Conference on Artificial Intelligence Book

Thessaloniki, Greece, 2016, ISBN: 978-1-4503-3734-2.

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2015

Vouros, George

The Emergence of Norms via Contextual Agreements in Open Societies Journal Article

CoRR, abs/1503.07017 , 2015.

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Skarlatidis, Anastasios; Paliouras, Georgios; Artikis, Alexander; Vouros, George A

Probabilistic Event Calculus for Event Recognition Journal Article

Transactions on Computational Logic, 16 , 2015.

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Santipantakis, Georgios; Kotis, Konstantinos; Vouros, George A

Accessing and Reasoning with Data from Disparate Data Sources Using Modular Ontologies and OBDA Conference

SEMANTiCS 2015, Vienna, 2015.

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Santipantakis, Georgios; Vouros, George A

Decomposing Ontologies for the Construction of Distributed Knowledge Bases: The mONT ul method Journal Article

IJAIT, 24 , 2015.

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G.Vouros,

The Emergence of Norms via Contextual Agreements in Open Societies Book Chapter

Fernando, Guttmann Christian Busquets Didac Koch (Ed.): Advances in Social Computing and Multiagent Systems, 541 , 2015.

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Santipantakis, Georgios; Kotis, Konstantinos; Vouros, George

Ontology-Based Data Integration for Event Recognition in the Maritime Domain Conference

WIMS 2015, Limassol, Cyprus, 2015.

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Santipantakis, George M; Kotis, Konstantinos; Vouros, George A

Ontology-based Data Sourcestextquoteleft Integration for Maritime Event Recognition Conference

Workshop on Modeling, Computing and Data Handling for Marine Transportation (MCDMT 2015) @ IISA 2015, IEEE IEEE, Corfu – Greece, 2015.

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~n, Carlos Iván Ches; Onaindia, Eva; Ossowski, Sascha; Vouros, George A

Special Issue on Agreement Technologies Journal Article

Information Systems Frontiers Journal, Springer, 2015.

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2014

Santipantakis, Georgios; Vouros, George A

Distributed Reasoning with Coupled Ontologies: The E-SHIQ Representation Framework Journal Article

KAIS, OnLine , 2014, ISSN: 0219-3116.

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Santipantakis, Georgios; Vouros, George A

Constructing E-SHIQ Distributed Knowledge Bases via Ontology Modularization: The mONTul method Conference

WoMO 2014, 2014.

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Vouros, George

Decentralized Semantic Coordination of Interconnected Entities via Belief Propagation Journal Article

AI Communications, On Line , 2014, ISSN: 1875-8452.

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Santipantakis, Georgios; Vouros, George A

Modularizing Ontologies for the Construction of E-SHIQ Distributed Knowledge Bases Conference

SETN 2014 (Hellenic Conference on AI), Springer Springer, Ioannina, Greece, 2014.

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2004

Kotis, K; Vouros, G

HCONE approach to Ontology Merging Conference

The Semantic Web: Research and Applications: First European Semantic Web Symposium (ESWS 2004), Springer-Verlag Lecture Notes in Computer Science Springer-Verlag Lecture Notes in Computer Science, Crete, Greece, 2004.

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Vouros, G; Panayiotopoulos, T

Methods and Applications of Artificial Intelligence Third Helenic Conference on AI, SETN 2004 Book

Springer Verlag, 2004, ISBN: 978-3-540-21937-8.

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Partsakoulakis, I; Vouros, G

Personal Representative Agents for Reliable Participation in Social Contexts Conference

In Proceedings of the CEAS workshop – ECAI 04 Workshop, 2004.

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Aivaloglou, E; Vouros, G

Shoplet and the Personal Market Place model for e-commerce Journal Article

The electronic journal of e-commerce tools and applications, 1 , 2004.

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Partsakoulakis, I; Kourakos-Mavromichalis, V; Vouros, G

Socially Deliberating Agents for Human-Centered Knowledge Management Conference

In Proc of the IEEE International Conference on Systems, Man and Cybernetics, 2004.

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2003

Karakapilidis, N; Vouros, G; Darzentas, J

Applying Intelligent Agents Technology in a Collaborative Work Environment Journal Article

International Transactions in Operations Research, 10 , pp. 13-31, 2003.

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Kotis, K; Vouros, G; Kotis, K

Human Centered Ontology Management with HCONE Conference

ODS 2003 Workshop on Ontologies and Distributed Systems,International Joint Conference in Artificial Intelligence(IJCAI-03), CEUR Workshop Proceedings (CEUR-WS.org) CEUR Workshop Proceedings (CEUR-WS.org), Acapulco, Mexico, 2003.

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Vouros, G; Partsakoulakis, I; Kourakos-Mavromichalis, V

Realizing Human-Centered Systems via Socially Deliberating Agents Conference

In Proceedings of the 10th International Conference in Human-Computer Interaction, Crete, Greece, 2003.

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Vouros, G

Technological Issues towards Knowledge Powered Organizations Journal Article

Knowledge Management Journal, 7 , pp. 114-127, 2003.

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2002

Vouros, G

Discrete Mathematics Book

Hellenic Open University, Patras, 2002.

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Partsakoulakis, I; Vouros, G

Helping Young Students Reach Valid Decision Through Model Checking Conference

3rd Hellenic Conference on Technology of Information and Communication in Education, Rhodes, Greece, 2002.

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Partsakoulakis, I; Vouros, G

Importance and Properties of Roles in MAS Organization: A review of methodologies and systems Conference

in Proc. of the workshop on MAS Problem Spaces and Their Implications to Achieving Globally Coherent Behavior (AAMAS 02), Bologna, Italy, 2002.

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Partsakoulakis, I; Vouros, G; Partsakoulakis, I

Roles in Collaborative Activity Conference

Second Hellenic Conference on Artificial Intelligence, SETN 02, LNCS – Springer Verlag LNCS – Springer Verlag, Thessaloniki, Greece, 2002.

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Vouros, G; Eumeridou, E

Simple and EuroWordNet: Towards the Prometheus Ontological Framework Journal Article

Terminology – International journal of Theoretical and applied issues in Specialized Communication, 8 , pp. 245-281 (37), 2002.

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Vouros, G

Towards a Generic Architecture for Cooperative Learning Environments Journal Article

International Journal of Continuous education and Life-Long Learning, special issue in Intelligent Agents for education and Training Systems, 12 , pp. 331-347, 2002.

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2001

Kourakos-Mavromichalis, V; Vouros, G

Balancing Between Reactivity and Deliberation in the ICAGENT Framework Book Chapter

Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions), 2103 , pp. 53-75, LNAI Volume 2103, Springer-Verlag, 2001.

BibTeX

Kourakos-Mavromichalis, V; Vouros, G; Kourakos-Mavromichalis, V

“Building Intelligent Collaborative Interface Agents with the ICagent Development Framework” Conference

Proceedings of 8th Panhellenic Conference on Informatics (with international participation), 2001.

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Vouros, G

Conceptual Modeling of Multimedia Objects for User-Tailored Information Presentations Journal Article

Applied Artificial Intelligence, 15 , pp. 521-560, 2001.

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Vouros, G; Kourakos-Mavromichalis, V

Towards a Generic Framework for Building Intelligent Collaborative Agents Journal Article

ERCIM News, 2001.

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2000

Vouros, G; Vidalis, M I; Papadopoulos, H T

Heuristic Algorithm for Buffer Allocation in Unreliable Production Lines Journal Article

International Journal of Operations and Quantitative Management, 6 , pp. 23-44, 2000.

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Kourakos-Mavromichalis, V; Vouros, G

ICAGENT : Balancing between Reactivity and Deliberation Conference

Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications, ECAI 2000 Workshop, Springer Springer, 2000.

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Vouros, G; Pantelakis, J; Lekkas, T D

Knowledge Representation in an activated sludge plant diagnosis system Journal Article

International Journal Expert Systems with Applications, 17 , pp. 226-240, 2000.

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P.Tselios, ; Platis, A; Vouros, G

Providing Advice to Website Designers towards effective Websites Structure Re-Organisation Conference

4th European Conference PKDD 2000, Lyon, France, 2000.

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21.	Apostolos Glenis, George Vouros A: Balancing Between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings. International Conference on Discovery Science, Lecture Notes in Computer Science, 12323 , LNAI Springer, 2020. (Type: Conference \| Abstract \| BibTeX) @conference{Glenis2020, title = {Balancing Between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings}, author = {Apostolos Glenis, George A. Vouros}, year = {2020}, date = {2020-10-15}, booktitle = {International Conference on Discovery Science, Lecture Notes in Computer Science}, volume = {12323}, pages = {265-279}, publisher = {Springer}, series = {LNAI}, abstract = {As big data sources providing time series increase, and data is provided in increased velocity and volume, we need to efficiently recognize data provided, classifying it according to their type, origin etc. This is a first important step in doing analytics on data provided from disparate data sources, such as archival sources, multiple sensors, or social media feeds. Time series classification is the task labeling time series using a set of predefined labels. In this paper we present the K-BOSS-VS algorithm for time series classification. The proposed algorithm is based on state-of-the-art symbolic time series classification algorithms, and aims to achieve high accuracy, balancing with computational efficiency. K-BOSS-VS exploits K representatives of each time series class to classify new series. This provides opportunities for representing intra-class differences, thus increasing the classification accuracy, while incurring a small performance overhead compared to methods using one class representative. Additionally, K-BOSS-VS offers a solution for classifying time-series in batch and streaming settings, due to the opportunities for increasing computational efficiency and the low memory requirements.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close As big data sources providing time series increase, and data is provided in increased velocity and volume, we need to efficiently recognize data provided, classifying it according to their type, origin etc. This is a first important step in doing analytics on data provided from disparate data sources, such as archival sources, multiple sensors, or social media feeds. Time series classification is the task labeling time series using a set of predefined labels. In this paper we present the K-BOSS-VS algorithm for time series classification. The proposed algorithm is based on state-of-the-art symbolic time series classification algorithms, and aims to achieve high accuracy, balancing with computational efficiency. K-BOSS-VS exploits K representatives of each time series class to classify new series. This provides opportunities for representing intra-class differences, thus increasing the classification accuracy, while incurring a small performance overhead compared to methods using one class representative. Additionally, K-BOSS-VS offers a solution for classifying time-series in batch and streaming settings, due to the opportunities for increasing computational efficiency and the low memory requirements. Close
22.	Christos Spatharis Konstantinos Blekas, George Vouros A: Apprenticeship learning of flight trajectories prediction with inverse reinforcement learning.. In: SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, ACM, 2020. (Type: Inproceedings \| Abstract \| Links \| BibTeX) @inproceedings{Spatharis2020, title = {Apprenticeship learning of flight trajectories prediction with inverse reinforcement learning.}, author = {Christos Spatharis, Konstantinos Blekas, George A. Vouros}, doi = {10.1145/3411408.3411427}, year = {2020}, date = {2020-09-01}, booktitle = {SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece}, publisher = {ACM}, abstract = {One of the primary goals of Artificial Intelligence research is to develop machines with human-like intelligence, perception and reasoning. In this direction teaching apprentice agents by observing demonstrations delivered by experts is a framework of imitation learning that can provide improved solutions and it is possible to significantly outperform the demonstrator. Inverse reinforcement learning (IRL) is a paradigm relying on Markov Decision Processes (MDPs) that has a twofold target: to learn optimum policies of autonomous agents for solving complex tasks from successful demonstrations, and also to discover the unknown reward function that could explain the expert behavior. In this article we are addressing the trajectory prediction problem in the aviation domain by using an IRL approach. The proposed learning scheme provides an imitation process where the algorithm tries to imitate demonstrated trajectories, exploiting raw trajectory data enriched with contextual features and learn an efficient reward model that is learned during imitation and has generalization capabilities to unknown cases. We show several experimental results using real trajectory data from the Spanish FIR that confirms the effectiveness of our approach in automatically predicting trajectories.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close One of the primary goals of Artificial Intelligence research is to develop machines with human-like intelligence, perception and reasoning. In this direction teaching apprentice agents by observing demonstrations delivered by experts is a framework of imitation learning that can provide improved solutions and it is possible to significantly outperform the demonstrator. Inverse reinforcement learning (IRL) is a paradigm relying on Markov Decision Processes (MDPs) that has a twofold target: to learn optimum policies of autonomous agents for solving complex tasks from successful demonstrations, and also to discover the unknown reward function that could explain the expert behavior. In this article we are addressing the trajectory prediction problem in the aviation domain by using an IRL approach. The proposed learning scheme provides an imitation process where the algorithm tries to imitate demonstrated trajectories, exploiting raw trajectory data enriched with contextual features and learn an efficient reward model that is learned during imitation and has generalization capabilities to unknown cases. We show several experimental results using real trajectory data from the Spanish FIR that confirms the effectiveness of our approach in automatically predicting trajectories. Close doi:10.1145/3411408.3411427 Close
23.	Georgios M. Santipantakis Apostolos Glenis, Kostas Patroumpas Akrivi Vlachou Christos Doulkeridis George Vouros Nikos Pelekis Yannis Theodoridis A: SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources. In: Future Gener. Comput. Syst., 110 , pp. 540-555, 2020. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{Santipantakis2020, title = {SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources}, author = {Georgios M. Santipantakis, Apostolos Glenis, Kostas Patroumpas, Akrivi Vlachou, Christos Doulkeridis, George A. Vouros, Nikos Pelekis, Yannis Theodoridis}, doi = {10.1016/j.future.2018.07.007}, year = {2020}, date = {2020-06-13}, journal = {Future Gener. Comput. Syst.}, volume = {110}, pages = {540-555}, abstract = {An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects’ trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding – among other – surveillance, weather, and contextual data. Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities’ trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects’ trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding – among other – surveillance, weather, and contextual data. Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities’ trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets. Close doi:10.1016/j.future.2018.07.007 Close
24.	Costas Vassilakis Konstantinos Kotis, Dimitris Spiliotopoulos Dionisis Margaris Vlasios Kasapakis Christos-Nikolaos Anagnostopoulos Georgios Santipantakis George Vouros Theodore Kotsilieris Volha Petukhova Andrei Malchanau Ioanna Lykourentzou Kaj Michael Helin Artem Revenko Nenad Gligoric Boris Pokric M A: A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems. In: Big Data Cogn. Comput, 4 (2), 2020. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{DBLP:journals/bdcc/VassilakisKSMKA20, title = {A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems}, author = {Costas Vassilakis, Konstantinos Kotis, Dimitris Spiliotopoulos, Dionisis Margaris, Vlasios Kasapakis, Christos-Nikolaos Anagnostopoulos, Georgios M. Santipantakis, George A. Vouros, Theodore Kotsilieris, Volha Petukhova, Andrei Malchanau, Ioanna Lykourentzou, Kaj Michael Helin, Artem Revenko, Nenad Gligoric, Boris Pokric}, doi = {10.3390/bdcc4020006}, year = {2020}, date = {2020-04-20}, journal = {Big Data Cogn. Comput}, volume = {4}, number = {2}, abstract = {This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences. Close doi:10.3390/bdcc4020006 Close
25.	George A. Vouros Gennady L. Andrienko, Christos Doulkeridis Nikolaos Pelekis Alexander Artikis Anne-Laure Jousselme Cyril Ray José Manuel Cordero Garcia David Scarlatti : Big Data Analytics for Time-Critical Mobility Forecasting, From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains. Springer, 2020, ISBN: 978-3-030-45163-9. (Type: Book \| BibTeX) @book{Vouros2020, title = {Big Data Analytics for Time-Critical Mobility Forecasting, From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains}, author = {George A. Vouros, Gennady L. Andrienko, Christos Doulkeridis, Nikolaos Pelekis, Alexander Artikis, Anne-Laure Jousselme, Cyril Ray, José Manuel Cordero Garcia, David Scarlatti}, isbn = {978-3-030-45163-9}, year = {2020}, date = {2020-03-01}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {book} } Close
26.	Karampelas, Andreas; Vouros, George A: Time and Space Efficient Large Scale Link Discovery using String Similarities. In: Fundamenta Informaticae, 172 , pp. 299-325, 2020, ISSN: 0169-2968 (P). (Type: Journal Article \| Links \| BibTeX) @article{351, title = {Time and Space Efficient Large Scale Link Discovery using String Similarities}, author = {Andreas Karampelas and George A Vouros}, url = {https://content.iospress.com/articles/fundamenta-informaticae/fi1906?resultNumber=0&totalResults=158&start=0&q=Time+and+Space+Efficient&resultsPageSize=10&rows=10}, doi = {10.3233/FI-2020-1906}, issn = {0169-2968 (P)}, year = {2020}, date = {2020-02-01}, journal = {Fundamenta Informaticae}, volume = {172}, pages = {299-325}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://content.iospress.com/articles/fundamenta-informaticae/fi1906?resultNumbe[…] doi:10.3233/FI-2020-1906 Close
27.	Kotis, Konstantinos; Vouros, George A; Spiliotopoulos, Dimitris: Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations. In: The Knowledge Engineering Review, 35 , 2020. (Type: Journal Article \| Links \| BibTeX) @article{358, title = {Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations}, author = {Konstantinos Kotis and George A Vouros and Dimitris Spiliotopoulos}, url = {https://doi.org/10.1017/S0269888920000065}, doi = {https://doi.org/10.1017/S0269888920000065}, year = {2020}, date = {2020-01-01}, journal = {The Knowledge Engineering Review}, volume = {35}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://doi.org/10.1017/S0269888920000065 doi:https://doi.org/10.1017/S0269888920000065 Close
28.	Vouros, George A; Glenis, Apostolis; Doulkeridis, Christos: The delta big data architecture for mobility analytics. 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE IEEE, Oxford, UK, 2020. (Type: Conference \| BibTeX) @conference{359, title = {The delta big data architecture for mobility analytics}, author = {George A Vouros and Apostolis Glenis and Christos Doulkeridis}, year = {2020}, date = {2020-01-01}, booktitle = {2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)}, publisher = {IEEE}, address = {Oxford, UK}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
29.	Kravaris, Theocharis; et al.,: Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods. In: arXiv, cs.MA , 2019. (Type: Journal Article \| Links \| BibTeX) @article{354, title = {Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods}, author = {Theocharis Kravaris and et al.}, url = {https://arxiv.org/pdf/1912.06860.pdf}, year = {2019}, date = {2019-12-01}, journal = {arXiv}, volume = {cs.MA}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://arxiv.org/pdf/1912.06860.pdf Close
30.	G.Vouros, ; Santipantakis, G; Doulkeridis, C; Vlachou, A; Andrienko, G; Andrienko, N; Fuchs, G; Martinez, Miguel Garcia; Cordero, Jose Manuel Garcia: The datAcron Ontology for the Specification of Semantic Trajectories: Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics. In: Journal Of Data Semantics, 8 , 2019. (Type: Journal Article \| Links \| BibTeX) @article{352, title = {The datAcron Ontology for the Specification of Semantic Trajectories: Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics}, author = {G.Vouros and G Santipantakis and C Doulkeridis and A Vlachou and G Andrienko and N Andrienko and G Fuchs and Miguel Garcia Martinez and Jose Manuel Garcia Cordero}, url = {http://link.springer.com/article/10.1007/s13740-019-00108-0}, doi = {10.1007/s13740-019-00108-0}, year = {2019}, date = {2019-11-01}, journal = {Journal Of Data Semantics}, volume = {8}, chapter = {235}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close http://link.springer.com/article/10.1007/s13740-019-00108-0 doi:10.1007/s13740-019-00108-0 Close
31.	Petrou, P; et al.,: ARGO: A Big Data Framework for Online Trajectory Prediction. SSTD 2019, 2019. (Type: Conference \| BibTeX) @conference{345, title = {ARGO: A Big Data Framework for Online Trajectory Prediction}, author = {P Petrou and et al.}, year = {2019}, date = {2019-01-01}, booktitle = {SSTD 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
32.	Spatharis, C; et al.,: Collaborative multiagent reinforcement learning schemes for air traffic management. IISA 2019, 2019. (Type: Conference \| BibTeX) @conference{349, title = {Collaborative multiagent reinforcement learning schemes for air traffic management}, author = {C Spatharis and et al.}, year = {2019}, date = {2019-01-01}, booktitle = {IISA 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
33.	Vouros, G; Vlachou, A; Doulkeridis, C; Glenis, A; Santipantakis, G: Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases. SAC 2019, 2019. (Type: Conference \| BibTeX) @conference{346, title = {Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases}, author = {G Vouros and A Vlachou and C Doulkeridis and A Glenis and G Santipantakis}, year = {2019}, date = {2019-01-01}, booktitle = {SAC 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
34.	Doulkeridis, Christos; Qu, Qiang; Vouros, George A; ~a, Jo: Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data.. In: GEOINFORMATICA, 23 , 2019. (Type: Journal Article \| Links \| BibTeX) @article{353, title = {Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data.}, author = {Christos Doulkeridis and Qiang Qu and George A Vouros and Jo ~a}, url = {https://link.springer.com/article/10.1007%2Fs10707-019-00374-x}, year = {2019}, date = {2019-01-01}, journal = {GEOINFORMATICA}, volume = {23}, chapter = {235}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://link.springer.com/article/10.1007%2Fs10707-019-00374-x Close
35.	Koutroumanis, N; et al.,: Integration of Mobility Data with Weather Information.. EDBT/ICDT Workshops 2019, 2019. (Type: Conference \| BibTeX) @conference{348, title = {Integration of Mobility Data with Weather Information.}, author = {N Koutroumanis and et al.}, year = {2019}, date = {2019-01-01}, booktitle = {EDBT/ICDT Workshops 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
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