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The development of decision support systems is a complex, multi-stage process that requires considerable effort both methodically and technically. At the same time, with the proliferation of service-oriented architecture (and, more recently, the microservice approach based on it), technical development of such systems is often largely reduced to the construction of compositions of existing services implementing information processing functions, so that eventually the corresponding composition provides the necessary functionality of the decision support system. The paper proposes an approach to the construction of configurable service-oriented decision support systems based on the automated service composition, which will significantly reduce the effort required to develop such systems. Particularly, following results are presented: a) functional framework of different types of decision support systems, typical structures and patterns applicable in different types of decision support systems; b) a set of requirements for configurable service-oriented decision support systems and their main components (tools for describing services, and the purposes of the composite service, methods and algorithms for implementing the composition); c) a conceptual model of a configurable service-oriented decision support system.
Andrew Ponomarev; Nikolay Mustafin. Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model. Procedia Computer Science 2021, 186, 654 -660.
AMA StyleAndrew Ponomarev, Nikolay Mustafin. Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model. Procedia Computer Science. 2021; 186 ():654-660.
Chicago/Turabian StyleAndrew Ponomarev; Nikolay Mustafin. 2021. "Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model." Procedia Computer Science 186, no. : 654-660.
The development of a decision support system typically requires a significant effort from both domain modelling and technical perspectives. The paper proposes an approach for reducing the complexity of decision support system development leveraging automated service composition. The rationale behind the approach is that today many software systems (including decision support systems) are based on service-oriented architecture and to some extent the development of such systems can be represented as building composition of services satisfying the specified requirements. The problem of building such compositions can be successfully addressed by automated planning algorithms. Particularly, the paper presents the functional framework of decision support systems, requirements analysis for configurable service-oriented decision support systems and their main components, and a conceptual model of a configurable service-oriented decision support system.
N Mustafin; A Ponomarev; P Kopylov. An approach to the development of decision support systems with knowledge-driven automated service composition. Journal of Physics: Conference Series 2021, 1801, 012013 .
AMA StyleN Mustafin, A Ponomarev, P Kopylov. An approach to the development of decision support systems with knowledge-driven automated service composition. Journal of Physics: Conference Series. 2021; 1801 (1):012013.
Chicago/Turabian StyleN Mustafin; A Ponomarev; P Kopylov. 2021. "An approach to the development of decision support systems with knowledge-driven automated service composition." Journal of Physics: Conference Series 1801, no. 1: 012013.
Crowdsourcing provides a convenient solution for many information processing problems that are still hard or even intractable by modern AI techniques, but are relatively simple for many people. However, complete crowdsourcing solution cannot go by without a quality control mechanisms, as the results received from participants are not always reliable. The paper considers taxonomy-based crowd-labeling - a form of crowdsourcing, in which participants label objects with tags, and there exists an explicit taxonomy relation on the set of tags. We propose a method and an algorithm for label aggregation, allowing to estimate the likelihood of the true object label from a set of noisy labels received from the crowd, and to estimate the expected crowd members' accuracy. The proposed method and algorithm can be used in a wide range of crowd-labeling applications (e.g., classification of scientific literature collections, software repositories, etc.).
A Ponomarev; T Levashova; N Mustafin. An algorithm for labels aggregation in taxonomy-based crowd-labeling. Journal of Physics: Conference Series 2021, 1801, 012012 .
AMA StyleA Ponomarev, T Levashova, N Mustafin. An algorithm for labels aggregation in taxonomy-based crowd-labeling. Journal of Physics: Conference Series. 2021; 1801 (1):012012.
Chicago/Turabian StyleA Ponomarev; T Levashova; N Mustafin. 2021. "An algorithm for labels aggregation in taxonomy-based crowd-labeling." Journal of Physics: Conference Series 1801, no. 1: 012012.
Efficient collaboration of humans and machines has the great potential for improving many knowledge-intensive processes in variety of applications. Therefore, developing means supporting such collaboration and making it efficient is an important area of research. The paper presents a part of research aimed on the development of a collective intelligence environment that would support joint work of humans and machines on decision support problems, allowing participants to self-organize (define and adapt the plan of actions). In particular, it describes an approach to solving semantic interoperability issues in supporting human-machine collective intelligence for decision-making scenarios. The proposed approach is based on using multi-aspect ontologies and ontology-based smart spaces.
Alexander Smirnov; Andrew Ponomarev. Supporting Collective Intelligence of Human-Machine Teams in Decision-Making Scenarios. Advances in Intelligent Systems and Computing 2021, 773 -778.
AMA StyleAlexander Smirnov, Andrew Ponomarev. Supporting Collective Intelligence of Human-Machine Teams in Decision-Making Scenarios. Advances in Intelligent Systems and Computing. 2021; ():773-778.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev. 2021. "Supporting Collective Intelligence of Human-Machine Teams in Decision-Making Scenarios." Advances in Intelligent Systems and Computing , no. : 773-778.
Semantic labeling (describing objects using labels with explicitly defined semantic interpretation, e.g., with a help of ontology) is important for building semantic search systems. However, obtaining semantic labels is a hard and labor-intensive process. One of the approaches to perform semantic labeling is to use crowdsourcing. The use of crowdsourcing, in its turn, usually requires some quality control technique, because labels provided by individual participants may be unreliable. Despite there are many methods for label aggregation, most of them don’t take into account semantic relationships that may exist between labels. The paper proposes model, method and algorithm for semantic labels aggregation in crowdsourcing labeling applications. The proposed approach is based on the analysis of semantic relations between ontology classes, and takes into account inaccuracy and incompleteness of the results received from individual participants. It allows one to evaluate both the expected quality of the semantic labels received from individual crowdsourcing participants and the degree of belief of the collected labels. The proposed approach can be employed in semantic labeling crowdsourcing applications, e.g., to structure objects of a certain area in order to ensure semantic search. The only limiting factor in applying the approach is the existence of a pre-designed ontology of semantic relations, written in OWL 2.
Andrew Ponomarev. An Iterative Approach for Crowdsourced Semantic Labels Aggregation. Advances in Intelligent Systems and Computing 2020, 887 -894.
AMA StyleAndrew Ponomarev. An Iterative Approach for Crowdsourced Semantic Labels Aggregation. Advances in Intelligent Systems and Computing. 2020; ():887-894.
Chicago/Turabian StyleAndrew Ponomarev. 2020. "An Iterative Approach for Crowdsourced Semantic Labels Aggregation." Advances in Intelligent Systems and Computing , no. : 887-894.
A conceptual framework that supports configuration of service bundles and automatic provision of services from these bundles according to the consumer context is proposed. The framework adheres the idea of ontology-based product configuration where service bundles play the role of products. Service bundle is defined as a group of related customized services that are provided selectively in context-aware way on terms favorable to the consumer. Smart contracts regulate service provisioning/consumption. These contracts are represented in terms of the product/service ontology using SWRL-rules. The use case from the Internet services domain demonstrates the main ideas behind the framework.
Tatiana Levashova; Michael Pashkin. Context-Aware Smart-Contracts for Service Bundles. Advances in Intelligent Systems and Computing 2020, 512 -521.
AMA StyleTatiana Levashova, Michael Pashkin. Context-Aware Smart-Contracts for Service Bundles. Advances in Intelligent Systems and Computing. 2020; ():512-521.
Chicago/Turabian StyleTatiana Levashova; Michael Pashkin. 2020. "Context-Aware Smart-Contracts for Service Bundles." Advances in Intelligent Systems and Computing , no. : 512-521.
A model and method for generating context-driven recommendations for recommendation systems with multi-criteria ratings are proposed, which are applicable when the user’s attitude to the object is fixed not by using one integral criterion (assessment, overall rating), but by using a set of individual criteria that evaluate different aspects of the object. The proposed model and method allow one to solve two main problems of using recommender systems: to rank objects according to the predicted subjective integral utility with given weights of partial criteria and to rank objects according to the predicted subjective integral utility in a given context.
A. V. Smirnov; A. V. Ponomarev. Multicriteria Context-Driven Recommender Systems: Model and Method. Scientific and Technical Information Processing 2020, 47, 298 -303.
AMA StyleA. V. Smirnov, A. V. Ponomarev. Multicriteria Context-Driven Recommender Systems: Model and Method. Scientific and Technical Information Processing. 2020; 47 (5):298-303.
Chicago/Turabian StyleA. V. Smirnov; A. V. Ponomarev. 2020. "Multicriteria Context-Driven Recommender Systems: Model and Method." Scientific and Technical Information Processing 47, no. 5: 298-303.
This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use.
Alexey Kashevnik; Andrew Ponomarev; Nikolay Shilov; Andrey Chechulin. In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors. Sensors 2020, 20, 5049 .
AMA StyleAlexey Kashevnik, Andrew Ponomarev, Nikolay Shilov, Andrey Chechulin. In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors. Sensors. 2020; 20 (18):5049.
Chicago/Turabian StyleAlexey Kashevnik; Andrew Ponomarev; Nikolay Shilov; Andrey Chechulin. 2020. "In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors." Sensors 20, no. 18: 5049.
Today, crowd computing is successfully applied for many information processing problems in a variety of domains. One of the most acute issues with crowd-powered systems is the quality of results (as humans can make errors). Therefore, a number of methods have been proposed to process the results obtained from the crowd in order to compensate human errors. Most of the existing methods of processing contributions are constructed based on a (natural) assumption that the only information available is unreliable data obtained from the crowd. However, in some cases, additional information is available, and it can be utilized in order to improve the overall quality of the result. The paper describes a crowd computing application for community tagging of running race photos. It presents a utility analysis to identify situations in which community photo tagging is a reasonable choice. It also proposes a data fusion model making use of runners’ location information recorded in their Global Positioning System (GPS) tracks. Field experiments with the applications show that community-based tagging can collect enough contributors to process photosets from medium-sized running events. Simulation results confirm, that the use of data fusion in processing the results of crowd computing is a promising technique, and the use of probabilistic graphical models (e.g., Bayesian networks) for data fusion allows one to smoothly increase the quality of the results with an increase of the available information.
Andrew Ponomarev. Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion. Information 2020, 11, 385 .
AMA StyleAndrew Ponomarev. Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion. Information. 2020; 11 (8):385.
Chicago/Turabian StyleAndrew Ponomarev. 2020. "Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion." Information 11, no. 8: 385.
The paper describes conceptual and technological principles of the human-computer cloud, that allows to deploy and run human-based applications. It also presents two ways to build decision support services on top of the proposed cloud environment for problems where workflows are not (or cannot be) defined in advance. The first extension is represented by a decision support service leveraging task ontology to build the missing workflow, the second utilizes the idea of human-machine collective intelligence environment, where the workflow is defined in the process of a (sometimes, guided) collaboration of the participants.
Alexander Smirnov; Nikolay Shilov; Andrew Ponomarev. Human-Computer Systems for Decision Support: From Cloud to Self-organizing Environments. Communications in Computer and Information Science 2020, 1 -22.
AMA StyleAlexander Smirnov, Nikolay Shilov, Andrew Ponomarev. Human-Computer Systems for Decision Support: From Cloud to Self-organizing Environments. Communications in Computer and Information Science. 2020; ():1-22.
Chicago/Turabian StyleAlexander Smirnov; Nikolay Shilov; Andrew Ponomarev. 2020. "Human-Computer Systems for Decision Support: From Cloud to Self-organizing Environments." Communications in Computer and Information Science , no. : 1-22.
The paper presents an approach and case study of a distributed driver monitoring system. The system utilizes smartphone sensors for detecting dangerous states for a driver in a vehicle. We use a mounted smartphone on a vehicle windshield directed towards the driver’s face tracked by the front-facing camera. Using information from camera video frames as well as other sensors, we determine drowsiness, distraction, aggressive driving, and high pulse rate dangerous states that can lead to road accidents. We propose a cloud system architecture to capture statistics from vehicle drivers, analyze it and personalize the smartphone application for the driver. The cloud service provides reports on driver trips as well as statistics to developers. This allows to monitor and improve the system by developing modules for personification and taking into account context situation. We identified statistically that the driver eye closeness is related to the light brightness and drowsiness recognition should be adjusted accordingly.
Alexey Kashevnik; Igor Lashkov; Andrew Ponomarev; Nikolay Teslya; Andrei Gurtov. Cloud-Based Driver Monitoring System Using a Smartphone. IEEE Sensors Journal 2020, 20, 6701 -6715.
AMA StyleAlexey Kashevnik, Igor Lashkov, Andrew Ponomarev, Nikolay Teslya, Andrei Gurtov. Cloud-Based Driver Monitoring System Using a Smartphone. IEEE Sensors Journal. 2020; 20 (12):6701-6715.
Chicago/Turabian StyleAlexey Kashevnik; Igor Lashkov; Andrew Ponomarev; Nikolay Teslya; Andrei Gurtov. 2020. "Cloud-Based Driver Monitoring System Using a Smartphone." IEEE Sensors Journal 20, no. 12: 6701-6715.
The paper discusses a novel class of decision support systems, based on an environment, leveraging human-machine collective intelligence. The distinctive feature of the proposed environment is support for natural self-organization processes in the community of participants. Most of the existing approaches for leveraging human expertise in a computing system rely on a pre-defined rigid workflow specification, and those very few systems that try to overcome this limitation sidestep current body of knowledge of self-organization in artificial and natural systems. The paper outlines the general vision of the proposed environment, identifies main challenges that has to be dealt with in order to develop such environment and describes ways to address them. Potential applications of such decision support environment are ubiquitous and influence virtually all areas of human activities, especially in complex domains: business management, environment problems, and government decisions.
Alexander Smirnov; Andrew Ponomarev. Decision Support Based on Human-Machine Collective Intelligence: Major Challenges. Algorithms and Data Structures 2019, 113 -124.
AMA StyleAlexander Smirnov, Andrew Ponomarev. Decision Support Based on Human-Machine Collective Intelligence: Major Challenges. Algorithms and Data Structures. 2019; ():113-124.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev. 2019. "Decision Support Based on Human-Machine Collective Intelligence: Major Challenges." Algorithms and Data Structures , no. : 113-124.
Information processing systems utilizing the input received from human contributors are currently gaining popularity. One of the problems relevant to most of these systems is that they need a large number of contributors to function properly, while collecting this number of contributors may require significant effort and time. In the ongoing research, this problem is addressed by adaptation of cloud computing resource management principles to human-computer systems. The proposed human-computer cloud environment relies heavily on the use of ontologies for both resource discovery and automatic decision support workflow composition. This paper describes the set of main ontological models of the proposed human-computer cloud. Namely, the ontological model of the cloud environment, ontological model of the decision support system based on this environment and the ontology-based mechanism for workflow construction. The paper also illustrates the principles of dynamic workflow construction by an example from e-Tourism domain.
Alexander Smirnov; Tatiana Levashova; Nikolay Shilov; Andrew Ponomarev. Human-Computer Cloud for Decision Support: Main Ontological Models and Dynamic Resource Network Configuration. Advances in Intelligent Systems and Computing 2018, 16 -25.
AMA StyleAlexander Smirnov, Tatiana Levashova, Nikolay Shilov, Andrew Ponomarev. Human-Computer Cloud for Decision Support: Main Ontological Models and Dynamic Resource Network Configuration. Advances in Intelligent Systems and Computing. 2018; ():16-25.
Chicago/Turabian StyleAlexander Smirnov; Tatiana Levashova; Nikolay Shilov; Andrew Ponomarev. 2018. "Human-Computer Cloud for Decision Support: Main Ontological Models and Dynamic Resource Network Configuration." Advances in Intelligent Systems and Computing , no. : 16-25.
Alexander Smirnov; Maksim Shchekotov; Nikolay Shilov; Andrew Ponomarev. Decision Support Service Based on Dynamic Resource Network Configuration in Human-Computer Cloud. 2018 23rd Conference of Open Innovations Association (FRUCT) 2018, 1 .
AMA StyleAlexander Smirnov, Maksim Shchekotov, Nikolay Shilov, Andrew Ponomarev. Decision Support Service Based on Dynamic Resource Network Configuration in Human-Computer Cloud. 2018 23rd Conference of Open Innovations Association (FRUCT). 2018; ():1.
Chicago/Turabian StyleAlexander Smirnov; Maksim Shchekotov; Nikolay Shilov; Andrew Ponomarev. 2018. "Decision Support Service Based on Dynamic Resource Network Configuration in Human-Computer Cloud." 2018 23rd Conference of Open Innovations Association (FRUCT) , no. : 1.
The paper discusses a novel cloud architecture that unifies different types of resources (hardware, software and human) and leverages them for decision support. One of the distinctive features of the proposed cloud architecture is extensive use of ontological models and ontology-driven inference techniques. This paper describes some of the core ontological models used in human computer cloud and the ways they are used to deliver value for all stakeholders of the human-computer cloud environment. Tourism is among application domains that may especially benefit from this human-computer cloud as today's tourism applications heavily rely both on human and computer information processing and standardization of this processing via cloud interfaces will greatly simplify the creation of tourism decision support services.
Alexander Smirnov; Andrew Ponomarev; Nikolay Shilov. Ontology-Driven Human-Computer Cloud for Decision Support. 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) 2018, 1 -5.
AMA StyleAlexander Smirnov, Andrew Ponomarev, Nikolay Shilov. Ontology-Driven Human-Computer Cloud for Decision Support. 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC). 2018; ():1-5.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev; Nikolay Shilov. 2018. "Ontology-Driven Human-Computer Cloud for Decision Support." 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) , no. : 1-5.
The concept of human-computer cloud is an adaptation of a traditional cloud computing paradigm to applications that require human expertise for performing some of the information processing operations. In these environments, it is important to maintain rich contributor profile that would allow to automatically route task requests to the contributors who will most likely complete them with high quality. On the other hand, the burden of filling and updating the profile shouldn't be entirely on the shoulders of contributors. This paper describes the profile structure of human-computer cloud environment and several mechanisms for automatically filling it and keeping up-to-date.
Alexander Smirnov; Nikolay Teslya; Andrew Ponomarev; Alexey Kashevnik. Profiling Contributors in the Human-Computer Cloud. 2018 IEEE International Conference on Smart Computing (SMARTCOMP) 2018, 327 -332.
AMA StyleAlexander Smirnov, Nikolay Teslya, Andrew Ponomarev, Alexey Kashevnik. Profiling Contributors in the Human-Computer Cloud. 2018 IEEE International Conference on Smart Computing (SMARTCOMP). 2018; ():327-332.
Chicago/Turabian StyleAlexander Smirnov; Nikolay Teslya; Andrew Ponomarev; Alexey Kashevnik. 2018. "Profiling Contributors in the Human-Computer Cloud." 2018 IEEE International Conference on Smart Computing (SMARTCOMP) , no. : 327-332.
A variety of information processing and decision support tasks (especially in the context of smart city or smart tourist destination) rely both on the automated and human-based procedures. The article proposes a multi-layer cloud environment that, first, unifies various kinds of resources used by these information processing and decision-support scenarios (hardware, software, and human), and second, implements an ontology-based automatic service composition procedures that can be used to build ad hoc decision-support services for problems unknown in advance. The service composition is based on uniform description of all parts of the environment with a help of ontologies. The article describes the architecture and models of the novel human-computer cloud environment. It also describes several scenarios of decision support in tourism leveraging the proposed human-computer cloud concept.
Alexander Smirnov; Andrew Ponomarev; Nikolay Shilov; Alexey Kashevnik; Nikolay Teslya. Ontology-Based Human-Computer Cloud for Decision Support. International Journal of Embedded and Real-Time Communication Systems 2018, 9, 1 -19.
AMA StyleAlexander Smirnov, Andrew Ponomarev, Nikolay Shilov, Alexey Kashevnik, Nikolay Teslya. Ontology-Based Human-Computer Cloud for Decision Support. International Journal of Embedded and Real-Time Communication Systems. 2018; 9 (1):1-19.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev; Nikolay Shilov; Alexey Kashevnik; Nikolay Teslya. 2018. "Ontology-Based Human-Computer Cloud for Decision Support." International Journal of Embedded and Real-Time Communication Systems 9, no. 1: 1-19.
The number of crowd computing applications is rapidly growing; however, they currently lack unification and interoperability as each platform usually has its own model of tasks, resources and computation process. We aim at the development of a unifying ontology-driven platform that would support deployment of various human-based applications. Key features of the proposed human-computer cloud platform are ontologies and digital contracts. Ontological mechanisms (ability to precisely define semantics and use inference to find related terms) are employed to find and allocate human resources required by software applications. Whereas digital contracts are leveraged to achieve predictability required by cloud users (application developers). The paper describes major principles behind the platform.
Alexander Smirnov; Andrew Ponomarev; Tatiana Levashova; Nikolay Shilov. Platform-as-a-Service for Human-Based Applications: Ontology-Driven Approach. 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) 2017, 157 -162.
AMA StyleAlexander Smirnov, Andrew Ponomarev, Tatiana Levashova, Nikolay Shilov. Platform-as-a-Service for Human-Based Applications: Ontology-Driven Approach. 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2). 2017; ():157-162.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev; Tatiana Levashova; Nikolay Shilov. 2017. "Platform-as-a-Service for Human-Based Applications: Ontology-Driven Approach." 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) , no. : 157-162.
Alexander Smirnov; Andrew Ponomarev; Tatiana Levashova; Nikolay Shilov. Ontology-based cloud platform for human-driven applications. 2017 21st Conference of Open Innovations Association (FRUCT) 2017, 1 .
AMA StyleAlexander Smirnov, Andrew Ponomarev, Tatiana Levashova, Nikolay Shilov. Ontology-based cloud platform for human-driven applications. 2017 21st Conference of Open Innovations Association (FRUCT). 2017; ():1.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev; Tatiana Levashova; Nikolay Shilov. 2017. "Ontology-based cloud platform for human-driven applications." 2017 21st Conference of Open Innovations Association (FRUCT) , no. : 1.
The development of smart cities provides a lot of data and services that can be utilized to improve the tourists’ experience during the trip. Information technologies affect directly the development of tourism industry. Tourists and cities’ inhabitants take an active part in the production of tourism products, as well as in sharing their knowledge and experience. To help them in this activity and provide an interface to communicate with other people and computer resources the human-computer cloud concept has been viewed. The paper proposes a workflow that uses computer and human processing units for tourist’s itinerary planning. The workflow integrates data analysis from various sources with computer and human-based calculation of itineraries in the cloud system. The case is implemented based on the smart destination services of St. Petersburg, Russia.
Alexander Smirnov; Andrew Ponomarev; Nikolay Teslya; Nikolay Shilov. Human-Computer Cloud for Smart Cities: Tourist Itinerary Planning Case Study. Business Information Systems 2017, 179 -190.
AMA StyleAlexander Smirnov, Andrew Ponomarev, Nikolay Teslya, Nikolay Shilov. Human-Computer Cloud for Smart Cities: Tourist Itinerary Planning Case Study. Business Information Systems. 2017; ():179-190.
Chicago/Turabian StyleAlexander Smirnov; Andrew Ponomarev; Nikolay Teslya; Nikolay Shilov. 2017. "Human-Computer Cloud for Smart Cities: Tourist Itinerary Planning Case Study." Business Information Systems , no. : 179-190.