Dumitru Roman

• Roman, Dumitru & Kifer, Michael (2018). ServLog: A unifying logical framework for service modeling and contracting. Semantic Web Journal.  ISSN 1570-0844.  9(2), s 257- 290 . doi: 10.3233/SW-170262 Show summary

  Implementing semantics-aware services, which includes semantic Web services, requires novel techniques for modeling and analysis. The problems include automated support for service discovery, selection, negotiation, and composition. In addition, support for automated service contracting and contract execution is crucial for any large scale service environment where multiple clients and service providers interact. Many problems in this area involve reasoning, and a number of logic-based methods to handle these problems have emerged in the field of Semantic Web Services. In this paper, we lay down theoretical foundations for service modeling, contracting, and reasoning, which we call ServLog, by developing novel techniques for modeling and reasoning about service contracts with the help of Concurrent Transaction Logic. With this framework, we significantly extend the modeling power of the previous work by allowing expressive data constraints and iterative processes in the specification of services. This approach not only captures typical procedural constructs found in established business process languages, but also greatly extends their functionality, enables declarative specification and reasoning about services, and opens a way for automatic generation of executable business processes from service contracts.

• Estrada, Jesus; Sánchez, Héctor; Hernanz, Lorena; Checa, María José & Roman, Dumitru (2017). Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment. ISPRS International Journal of Geo-Information.  ISSN 2220-9964.  6(8) . doi: 10.3390/ijgi6080255 Full text in Research Archive. Show summary

  A comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can be used for calibration and validation purposes, avoiding field visits and saving useful resources. In this paper, we propose a process for objective and automated identification of agricultural parcel features based on processing and combining Sentinel-2 data (to sense different types of irrigation patterns) and LiDAR data (to detect landscape elements). The proposed process was validated in several use cases in Spain, yielding high accuracy rates in the identification of irrigated areas and landscape elements. An important application example of the work reported in this paper is the European Union (EU) Common Agriculture Policy (CAP) funds assignment service, which would significantly benefit from a more objective and automated process for the identification of irrigated areas and landscape elements, thereby enabling the possibility for the EU to save significant amounts of money yearly.

• Gan, Dennis Yong Chun & Roman, Dumitru (2017). MOBILE BIG DATA: THE SILVER BULLET FOR TELCOS? A CASE STUDY IN THE NORWEGIAN TELCOS MARKET, In Yingcai Xiao & Ajith P. Abraham (ed.),  INTERNATIONAL CONFERENCES COMPUTER GRAPHICS, VISUALIZATION, COMPUTER VISION AND IMAGE PROCESSING 2017 and BIG DATA ANALYTICS, DATA MINING AND COMPUTATIONAL INTELLIGENCE 2017, Lisbon, Portugal July 21 - 23, 2017.  IADIS Press.  ISBN 978-989-8533-66-1.  artikkel.  s 263 - 272 Full text in Research Archive. Show summary

  The telecommunication industry has been undergoing tremendous changes in recent times. It is obvious that the industry is currently going through an identity crisis. Porter’s Five Forces analysis is used in this paper to investigate the current state of the telecommunication industry in Norway today. The dwindling voice revenue of telcos necessitates the race to find the next revenue stream. This study explores the potential of mobile big data as a resource for telcos to gain competitive advantage with the VRIO Framework (Value-Rare-Imitability-Organization) from the Resources-based View(RBV) theory. Multiple case studies with embedded units that are explanatory and exploratory are applied to Norwegian telcos, following an inductive approach. This study finds that the outlook of the telecommunication industry is rather bleak and despite its promises, mobile big data can only provide temporary competitive advantage to the telcos. Mobile big data is valuable, rare and the telcos are organizing their other resources around it to exploit it, however mobile big data is imitable and not unique. The same data can be obtained by their peers in the industry. Instead, the interview data collected from qualitative research in this study has pointed to organizational culture as the resource that can provide sustained competitive advantage to the telcos. In a highly-competitive industry such as the telecommunication industry, telcos have to constantly rely on resources that can give them temporary competitive advantage; the ability to do this will ultimately be a resource itself that will give them sustained competitive advantage. Telcos have to constantly mix, match and reconfigure their different resources and capabilities to address a rapidly changing environment.

• Mahasivam, Nivethika; Nikolov, Nikolay; Sukhobok, Dina & Roman, Dumitru (2017). Data preparation as a service based on Apache Spark. Lecture Notes in Computer Science.  ISSN 0302-9743.  10465, s 125- 139 . doi: 10.1007/978-3-319-67262-5_10 Show summary

  Data preparation is the process of collecting, cleaning and consolidating raw datasets into cleaned data of certain quality. It is an important aspect in almost every data analysis process, and yet it remains tedious and time-consuming. The complexity of the process is further increased by the recent tendency to derive knowledge from very large datasets. Existing data preparation tools provide limited capabilities to effectively process such large volumes of data. On the other hand, frameworks and software libraries that do address the requirements of big data, require expert knowledge in various technical areas. In this paper, we propose a dynamic, service-based, scalable data preparation approach that aims to solve the challenges in data preparation on a large scale, while retaining the accessibility and flexibility provided by data preparation tools. Furthermore, we describe its implementation and integration with an existing framework for data preparation – Grafterizer. Our solution is based on Apache Spark, and exposes application programming interfaces (APIs) to integrate with external tools. Finally, we present experimental results that demonstrate the improvements to the scalability of Grafterizer.

• Nikolov, Nikolay; Sukhobok, Dina; Dragnev, Stefan; Dalgard, Steffen Harald; Elvesæter, Brian; Zernichow, Bjørn Marius von & Roman, Dumitru (2017). DataGraft beta v2: New features and capabilities. CEUR Workshop Proceedings.  ISSN 1613-0073.  1963 Full text in Research Archive. Show summary

  In this demonstrator, we will introduce the latest features and capabil-ities added to DataGraft – a Data-as-a-Service platform for data preparation and knowledge graph generation. DataGraft provides data transformation, publishing and hosting capabilities that aim to simplify the data publishing lifecycle for data workers (i.e., Open Data publishers, Linked Data developers, data scientists). This demonstrator highlights the recent features added to DataGraft by exempli-fying data publication of statistical data – going from the raw data published at a public portal to published and accessible Linked Data with the help of the tools and features of the platform.

• Roman, Dumitru; Kobernus, Michael John; Ødegård, Rune Åvar; Nikolov, Nikolay; Sukhobok, Dina; Zernichow, Bjørn Marius von & Lech, Till Christopher (2017). ALaDIn: Shining a Light on Air Quality through Data Integration and Machine Learning, In Benoît Otjacques; Patrik Hitzelberger; Stefan Naumann & Wohlgemuth Volker (ed.),  From Science to Society: The Bridge provided by Environmental Informatics, Adjunct Proceedings of the 31st EnviroInfo Conference, Luxembourg, September 13-15. 2017.  Shaker Verlag.  ISBN 978-3-8440-5495-8.  artikkel.  s 293 - 298 Show summary

  To achieve the necessary level of accuracy when measuring air pollution for scientific purposes, expensive and complicated instrumentation is required. Consequently, only federal, local governments and some industries, collect data of sufficient quality for research, and only for a small number of Air Quality components. This limitation makes it difficult to implement added value services, such as exposure and health assessments. Furthermore, due to increasing urban and peri-urban population density and consequent rise in air pollution, Air Quality management problems are becoming more complex. As a result, there is a vital need for enhanced Air Quality and exposure monitoring capabilities. This has been severely hampered by the high cost of traditional monitoring stations and the lack of high resolution data.

• Roman, Dumitru; Nikolov, Nikolay; Pultier, Antoine; Sukhobok, Dina; Elvesæter, Brian; Berre, Arne- Jørgen; Ye, Xianglin; Dimitrov, Marin; Simov, Alex; Zarev, Momchill; Moynihan, Rick; Roberts, Bill; Berlocher, Ivan; Kim, Seon-Ho; Lee, Tony; Smith, Amanda & Heath, Tom (2017). DataGraft: One-Stop-Shop for Open Data Management. Semantic Web Journal.  ISSN 1570-0844.  s 1- 19 . doi: 10.3233/SW-170263 Full text in Research Archive. Show summary

  This paper introduces DataGraft (https://datagraft.net/) – a cloud-based platform for data transformation and publishing. DataGraft was developed to provide better and easier to use tools for data workers and developers (e.g. open data publishers, linked data developers, data scientists) who consider existing approaches to data transformation, hosting, and access too costly and technically complex. DataGraft offers an integrated, flexible, and reliable cloud-based solution for hosted open data management. Key features include flexible management of data transformations (e.g. interactive creation, execution, sharing, reuse) and reliable data hosting services. This paper provides an overview of DataGraft focusing on the rationale, key features and components, and evaluation.

• Roman, Dumitru; Paniagua, Javier; Tarasova, Tatiana; Georgiev, Georgi; Sukhobok, Dina; Nikolov, Nikolay & Lech, Till Christopher (2017). ProDataMarket: A data marketplace for monetizing linked data. CEUR Workshop Proceedings.  ISSN 1613-0073.  1963 Full text in Research Archive. Show summary

  Linked data has emerged as an interesting technology for Publishing structured data on the Web but also as a powerful mechanism for integrating disparate data sources. Various tools and approaches have been developed in the semantic Web community to produce and consume linked data, however little attention has been paid to monetization of linked data. In this paper we introduce a data marketplace – proDataMarket – that enables data providers to generate, advertise, and sell linked data, and data consumers to purchase linked data on the marketplace. The marketplace was originally designed with a focus on geospatial linked data (targeting property-related data providers and consumers) but its capabilities are generic and can be used for data in various domains. This demo will highlight the capabilities offered to the providers and consumers of the data made available on the marketplace.

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• Costantini, Stefania; Enrico, Franconi; Van Woensel, William; Kontchakov, Roman; Sadri, Fariba & Roman, Dumitru (ed.) (2017). Rules and Reasoning International Joint Conference, RuleML+RR 2017, London, UK, July 12–15, 2017, Proceedings. Springer.  ISBN 978-3-319-61251-5.  239 s.

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• Costantini, Stefania; Franconi, Enrico; van Woensel, William; Kontchakov, Roman; Sadri, Fariba & Roman, Dumitru (2017). Preface. Lecture Notes in Computer Science.  ISSN 0302-9743.  10364 LNCS, s V- VII

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