The Internet of Things (IoT) is among the latest Information and Communication Technologies (ICT) developments that is making the boundaries between reality and fiction vanish. According to Mark Weiser, "...The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it." [5]. The International Data Corporation (IDC) also mentions the huge spending on IoT that "...will increase by a compound annual growth rate of 13.6% from 2017 to 2022, reaching $1.2 trillion within the next four years"1. To sustain this rapid growth, IoT should overcome different obstacles such as diversity of things' development technologies and communication standards, users' reluctance and sometimes rejection due to things invading their privacy, lack of killer applications that demonstrate the necessity of things, lack of an IoT-oriented software engineering discipline, and finally, the passive nature of things. To address these obstacles, the research community is putting forward many solutions that would make things proactive and responsive to their cyber-physical surroundings. This should allow things for instance, to reach out to peers that expose collaborative behavior, to form dynamic alliances when necessary, to avoid peers that expose malicious behavior, and to be accountable for their actions. In this keynote presentation, we discuss our ongoing research agenda on IoT with focus on four initiatives: process-of-things [3], mutation-of-things [1], cloud-fog-things [2, 6], and finally, vetting-things [4].
{"title":"Internet of Things: The Way Ahead","authors":"Z. Maamar","doi":"10.1145/3415958.3433085","DOIUrl":"https://doi.org/10.1145/3415958.3433085","url":null,"abstract":"The Internet of Things (IoT) is among the latest Information and Communication Technologies (ICT) developments that is making the boundaries between reality and fiction vanish. According to Mark Weiser, \"...The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.\" [5]. The International Data Corporation (IDC) also mentions the huge spending on IoT that \"...will increase by a compound annual growth rate of 13.6% from 2017 to 2022, reaching $1.2 trillion within the next four years\"1. To sustain this rapid growth, IoT should overcome different obstacles such as diversity of things' development technologies and communication standards, users' reluctance and sometimes rejection due to things invading their privacy, lack of killer applications that demonstrate the necessity of things, lack of an IoT-oriented software engineering discipline, and finally, the passive nature of things. To address these obstacles, the research community is putting forward many solutions that would make things proactive and responsive to their cyber-physical surroundings. This should allow things for instance, to reach out to peers that expose collaborative behavior, to form dynamic alliances when necessary, to avoid peers that expose malicious behavior, and to be accountable for their actions. In this keynote presentation, we discuss our ongoing research agenda on IoT with focus on four initiatives: process-of-things [3], mutation-of-things [1], cloud-fog-things [2, 6], and finally, vetting-things [4].","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125867725","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}
Spatial information retrieval is a common task of digital ecosystems due to the popularity of collecting and storing spatial information and phenomena in the world of the Internet of Things (IoT). Spatial relationships play an important role in this context by specifying how two or more spatial objects are related or connected. Examples of spatial relationships include topological relationships (e.g., intersect, overlap, contains), metric relationships (e.g., nearest neighbors), and direction relationships (e.g., cardinal directions like north and south). Many works in the literature have proposed definitions and implementations of spatial queries based on specific types of spatial relationships. Hence, a holistic view of these works is important to understand their applicability and relations. This paper advances in the literature by providing a comprehensive survey of the implementations and types of spatial queries that can be used by digital ecosystems. We present a novel characterization based on spatial relationships to define topological-based, metric-based, and direction-based spatial queries. For each type of spatial query, we present its intuitive and formal definitions together with possible strategies of implementation. Further, we identify hybrid spatial queries as combinations of two or more spatial relationships, and spatial joins as generalization cases. In addition, we present some equivalences between some types of queries. As a result, we point out future research topics in spatial information retrieval.
{"title":"Spatial Information Retrieval in Digital Ecosystems: A Comprehensive Survey","authors":"A. Carniel","doi":"10.1145/3415958.3433038","DOIUrl":"https://doi.org/10.1145/3415958.3433038","url":null,"abstract":"Spatial information retrieval is a common task of digital ecosystems due to the popularity of collecting and storing spatial information and phenomena in the world of the Internet of Things (IoT). Spatial relationships play an important role in this context by specifying how two or more spatial objects are related or connected. Examples of spatial relationships include topological relationships (e.g., intersect, overlap, contains), metric relationships (e.g., nearest neighbors), and direction relationships (e.g., cardinal directions like north and south). Many works in the literature have proposed definitions and implementations of spatial queries based on specific types of spatial relationships. Hence, a holistic view of these works is important to understand their applicability and relations. This paper advances in the literature by providing a comprehensive survey of the implementations and types of spatial queries that can be used by digital ecosystems. We present a novel characterization based on spatial relationships to define topological-based, metric-based, and direction-based spatial queries. For each type of spatial query, we present its intuitive and formal definitions together with possible strategies of implementation. Further, we identify hybrid spatial queries as combinations of two or more spatial relationships, and spatial joins as generalization cases. In addition, we present some equivalences between some types of queries. As a result, we point out future research topics in spatial information retrieval.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981542","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}
There are over 1900 cryptocurrencies trading in the market as of September 2020 and the number is rapidly growing. In the current crypto scene, cryptocurrencies are seen as investment vehicles by many, yet every crypto asset is designed to operate in a specific sector within a pre-defined business model. In addition to sector, there are various characteristics and factors that differentiate one crypto asset from another. Crypto investors can leverage these factors and characteristics and use these indicators to create different trading strategies. In our work, in order to guide the decision-making process for investors and to help them analyse crypto assets in a holistic manner, we classify the crypto assets under various characteristics, aiming towards a crypto asset taxonomy. In this paper, we focus on automated annotation of the cryptocurrencies in terms of sector, transaction anonymity and asset type through the public information. Though the information we utilise is public, it is scattered around quite a vast number of sources in different formats. Therefore, we generated an annotated dataset by collecting information from various sources. We utilised several supervised learning algorithms, including both traditional ones and more recent neural models, and analyzed the classification performance for the three aspects.
{"title":"Towards a Crypto Asset Taxonomy: A Text Classification-based Approach","authors":"Ozan Kose, P. Senkul, Gokce E. Phillips","doi":"10.1145/3415958.3433078","DOIUrl":"https://doi.org/10.1145/3415958.3433078","url":null,"abstract":"There are over 1900 cryptocurrencies trading in the market as of September 2020 and the number is rapidly growing. In the current crypto scene, cryptocurrencies are seen as investment vehicles by many, yet every crypto asset is designed to operate in a specific sector within a pre-defined business model. In addition to sector, there are various characteristics and factors that differentiate one crypto asset from another. Crypto investors can leverage these factors and characteristics and use these indicators to create different trading strategies. In our work, in order to guide the decision-making process for investors and to help them analyse crypto assets in a holistic manner, we classify the crypto assets under various characteristics, aiming towards a crypto asset taxonomy. In this paper, we focus on automated annotation of the cryptocurrencies in terms of sector, transaction anonymity and asset type through the public information. Though the information we utilise is public, it is scattered around quite a vast number of sources in different formats. Therefore, we generated an annotated dataset by collecting information from various sources. We utilised several supervised learning algorithms, including both traditional ones and more recent neural models, and analyzed the classification performance for the three aspects.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131648079","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}
Yared Dejene Dessalk, Nikolay Nikolov, M. Matskin, A. Soylu, D. Roman
Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows.
{"title":"Scalable Execution of Big Data Workflows using Software Containers","authors":"Yared Dejene Dessalk, Nikolay Nikolov, M. Matskin, A. Soylu, D. Roman","doi":"10.1145/3415958.3433082","DOIUrl":"https://doi.org/10.1145/3415958.3433082","url":null,"abstract":"Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"259 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133138499","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}
In this paper, we complement the research results provided with the OO-XAHM (Object-Oriented XML Adaptive Hypermedia Model), a state-of-the-art proposal for supporting adaptation features of the Web. In particular, in this contribution we provide: (i) the complete database-like implementation of OO-XAHM: (ii) a complete case study that focuses the attention on the well-known Italian archeological site Pompeii.
{"title":"Data-Intensive Object-Oriented Adaptive Web Systems: Implementing and Experimenting the OO-XAHM Framework","authors":"A. Cuzzocrea, Edoardo Fadda","doi":"10.1145/3415958.3433051","DOIUrl":"https://doi.org/10.1145/3415958.3433051","url":null,"abstract":"In this paper, we complement the research results provided with the OO-XAHM (Object-Oriented XML Adaptive Hypermedia Model), a state-of-the-art proposal for supporting adaptation features of the Web. In particular, in this contribution we provide: (i) the complete database-like implementation of OO-XAHM: (ii) a complete case study that focuses the attention on the well-known Italian archeological site Pompeii.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131956145","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}
Nitin Sukhija, Alexander Gessinger, Elizabeth Bautista
As the mainstream computing technology is entering into a post petascale era, the number and complexity of their computational components is on a sharp increase. With the increased pressure to pack more components per rack, the power and system densities are growing. Recently many researchers are focusing on Power Capping to address the power challenges in current and future computing systems. The power capping can be achieved by proactively estimating the power consumption of High Performance Computing (HPC) Jobs. In this study, we present our proposed machine learning framework to predict the power consumption of Lawrence Berkeley National Laboratory (LBNL) National Energy Scientific Computing Center (NERSC) Cori supercomputer workloads. We evaluate our framework using historical data of real production jobs executed on Cori to predict the amount of power required by a given job and to apply the predictions for enabling power capping in power-limited future systems to be commissioned at LBNL or other installation sites.
{"title":"Towards a Predictive Framework for Power Consumption of Jobs in HPC Facilities","authors":"Nitin Sukhija, Alexander Gessinger, Elizabeth Bautista","doi":"10.1145/3415958.3433042","DOIUrl":"https://doi.org/10.1145/3415958.3433042","url":null,"abstract":"As the mainstream computing technology is entering into a post petascale era, the number and complexity of their computational components is on a sharp increase. With the increased pressure to pack more components per rack, the power and system densities are growing. Recently many researchers are focusing on Power Capping to address the power challenges in current and future computing systems. The power capping can be achieved by proactively estimating the power consumption of High Performance Computing (HPC) Jobs. In this study, we present our proposed machine learning framework to predict the power consumption of Lawrence Berkeley National Laboratory (LBNL) National Energy Scientific Computing Center (NERSC) Cori supercomputer workloads. We evaluate our framework using historical data of real production jobs executed on Cori to predict the amount of power required by a given job and to apply the predictions for enabling power capping in power-limited future systems to be commissioned at LBNL or other installation sites.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130918945","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}
In this paper, Big-Data Driven Digital Ecosystem Framework (BDDDEF) for Online Predictive Control Systems is created. The proposed framework consists of different Agents, where each Agent is a distributed and virtual service. In our work, we provide solutions to the Big Data challenges in building Digital Ecosystems for Online Control including high volumes, velocity and variety of data, and the need for low data latency. We propose to use BDDDEF for building robust, reliable, fault-tolerant, scalable and high-loaded data pipelines for Online Predictive Control Systems. We review Big Data Main Systems for Online Predictive Control Architecture, review the literature for Digital Ecosystems design for Control Systems Online, design and describe main features, main architectural components and functional architecture of the framework, and finally, propose new Predictive Control methodology for Online Predictions.
{"title":"Big-Data Driven Digital Ecosystem Framework for Online Predictive Control","authors":"A. Suleykin, N. Bakhtadze, P. Panfilov","doi":"10.1145/3415958.3433077","DOIUrl":"https://doi.org/10.1145/3415958.3433077","url":null,"abstract":"In this paper, Big-Data Driven Digital Ecosystem Framework (BDDDEF) for Online Predictive Control Systems is created. The proposed framework consists of different Agents, where each Agent is a distributed and virtual service. In our work, we provide solutions to the Big Data challenges in building Digital Ecosystems for Online Control including high volumes, velocity and variety of data, and the need for low data latency. We propose to use BDDDEF for building robust, reliable, fault-tolerant, scalable and high-loaded data pipelines for Online Predictive Control Systems. We review Big Data Main Systems for Online Predictive Control Architecture, review the literature for Digital Ecosystems design for Control Systems Online, design and describe main features, main architectural components and functional architecture of the framework, and finally, propose new Predictive Control methodology for Online Predictions.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457032","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}
Hatem Khalloof, Mohammad Mohammad, Shadi Shahoud, Clemens Düpmeier, V. Hagenmeyer
Population-based metaheuristics -such as Evolutionary Algorithms (EAs)- are one of the most popular methods for solving highly complex and large-scale optimization problems. Nevertheless, finding an adequate solution with such approaches often requires computationally intensive fitness function evaluations especially in real-world applications. To speed up the computation, exploiting modern software techniques for parallelizing population-based metaheuristics on a cluster or a cloud is a viable approach. In the present paper, a generic, flexible and scalable framework for hierarchical hybridization of distributed population-based metaheuristics in a cluster environment is introduced. Three lightweight technologies, namely microservices, container virtualization and the publish/subscribe messaging paradigm are used to develop this framework. The combination of these technologies enables easy hybridizations of different parallelization models of population-based metaheuristics, a full decoupling between services providing basic building blocks of the algorithm and a seamless deployment in a scalable runtime environment. For evaluation purposes, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is exemplarily integrated into the framework and successfully deployed in a cluster environment. Scalability and applicability of the framework are explored by hybridizing the Coarse-Grained Model with the Global Model for solving the problem of unit commitment of distributed energy resources utilizing renewable energy generation. The results show that the new proposed framework introduces an excellent performance for scaling up the optimization speed of complex unit commitment optimization problems.
{"title":"A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics","authors":"Hatem Khalloof, Mohammad Mohammad, Shadi Shahoud, Clemens Düpmeier, V. Hagenmeyer","doi":"10.1145/3415958.3433041","DOIUrl":"https://doi.org/10.1145/3415958.3433041","url":null,"abstract":"Population-based metaheuristics -such as Evolutionary Algorithms (EAs)- are one of the most popular methods for solving highly complex and large-scale optimization problems. Nevertheless, finding an adequate solution with such approaches often requires computationally intensive fitness function evaluations especially in real-world applications. To speed up the computation, exploiting modern software techniques for parallelizing population-based metaheuristics on a cluster or a cloud is a viable approach. In the present paper, a generic, flexible and scalable framework for hierarchical hybridization of distributed population-based metaheuristics in a cluster environment is introduced. Three lightweight technologies, namely microservices, container virtualization and the publish/subscribe messaging paradigm are used to develop this framework. The combination of these technologies enables easy hybridizations of different parallelization models of population-based metaheuristics, a full decoupling between services providing basic building blocks of the algorithm and a seamless deployment in a scalable runtime environment. For evaluation purposes, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is exemplarily integrated into the framework and successfully deployed in a cluster environment. Scalability and applicability of the framework are explored by hybridizing the Coarse-Grained Model with the Global Model for solving the problem of unit commitment of distributed energy resources utilizing renewable energy generation. The results show that the new proposed framework introduces an excellent performance for scaling up the optimization speed of complex unit commitment optimization problems.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125844576","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}
Knowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach.
{"title":"Graph Embeddings in Criminal Investigation: Extending the Scope of Enquiry Protocols","authors":"V. Bellandi, P. Ceravolo, S. Maghool, S. Siccardi","doi":"10.1145/3415958.3433102","DOIUrl":"https://doi.org/10.1145/3415958.3433102","url":null,"abstract":"Knowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122885","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}
The need for a variety of auxiliary analytical tools to enhance marine safety and marine status awareness has been expressed by various platforms. The information that has been published while cruising is a rich resource for movement analysis of ships. Automatic Identification System (AIS), which is widely used in vessels, broadcasts information including the type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, and cargo. In this paper, to aid maritime operators, we work on arrival port, arrival time, and next position prediction on AIS messages, and propose three different approaches for the prediction of marine vessel movement. The experiments conducted against conventional supervised learning approaches reveal the improvement of the proposed solutions.
{"title":"Trajectory Prediction for Maritime Vessels Using AIS Data","authors":"Gozde Karatas, P. Senkul, Orhan Ayran","doi":"10.1145/3415958.3433079","DOIUrl":"https://doi.org/10.1145/3415958.3433079","url":null,"abstract":"The need for a variety of auxiliary analytical tools to enhance marine safety and marine status awareness has been expressed by various platforms. The information that has been published while cruising is a rich resource for movement analysis of ships. Automatic Identification System (AIS), which is widely used in vessels, broadcasts information including the type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, and cargo. In this paper, to aid maritime operators, we work on arrival port, arrival time, and next position prediction on AIS messages, and propose three different approaches for the prediction of marine vessel movement. The experiments conducted against conventional supervised learning approaches reveal the improvement of the proposed solutions.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610950","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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