Abstract: The aim of this paper is to highlight the potentials of the Internet of things (IoT) in supporting sustainable economical, ecological, social and especially business growth, according to the Multi-Objective (MO) Cloud Computing (CC) Sustainability assessment model. IoT is highly related to the CC as its inherent environment and platform for the operations. It has motivated us to present the ways of harnessing IoT capacities in realization of the defined sustainable development goals (SDGs), aiming to bring sustainable development, taking care of the present needs but without jeopardizing the ability of next generations to meet their own necessities. The MO CC Sustainability Assessment Framework is perfectly matched with IoT paradigm, taking a rule of support platform for storage and analysis of data collected from the IoT networked devices. Our idea is to provide a wide sustainability assessment framework that will encompass all the diversity of design and use variations as well as the integration of the CC technology as a support platform for more specific environments and architectural designs.

Abstract: The emergence of the Internet of Things (IoT), coupled with other capabilities such as Cloud Computing, Big Data, Artificial Intelligence or Block Chain, creates opportunities for the development of Cyber-Physical Systems (CPS) at the digital business ecosystem scale. It will impact the way engineers work, in order to design, develop, deploy, operate and maintain complex systems-of-systems, with increasing needs concerning security, agility and interoperability. This paper presents the evolutionary Federated Interoperability Framework (FIF) which has been developed continuously for Aeronautic Space and Defence over the last years in order to ensure continuous Product Lifecycle Management (PLM) interoperability. The evolutionary nature of the FIF is demonstrated, by describing how Cloud and Portal virtualization technologies were included into the FIF in order to short the time to usage of PLM standards within a Dynamic Manufacturing Network (DMN). However the FIF should similarly support identifying and addressing new interoperability challenges and scientific gaps related to Virtual Manufacturing based on other emerging Internet technologies.

Abstract: Systems that can tightly integrate physical with virtual components have represented a priority for the Systems Engineering Research. Research efforts have been concentrated in domains such as: Internet of Things, Internet of Services and recently in the domain of Cyber Physical Systems. The main objective of this paper refers to the investigation of applying process mining techniques on streams of data collected from existing sensing networks. A method based on automated planning problem is proposed and implemented.

Abstract: Continuous need for collaborations of large number of systems has created a requirement to achieve interoperability of the systems involved in collaborations. The subject of the research work is to find a solution that ensures interoperability of those systems whose business is rigid and there is no possibility of introducing standards. The existing solutions dealing with a similar problem are described. However, those solutions are very limited when flexibility and easy expandability are needed. This paper proposed an approach based on the domain-specific knowledge base that contains the referent rules and models for each system which participates in collaboration.

Abstract: Big Data analytics refers to analysis of large volumes of data of various types and sources for patterns that identify potential risks and opportunities for an organization. This analysis almost certainly performed by highly skilled data analysis specialists such as data scientists in a flexible analytical environment called analytics sandbox. However, the most of organizations do not base their business on such flexible environments, but the traditional Enterprise Information Systems (EISs). In order to enable organizations to use the results of Big Data analysis specialists, Big Data analytics should be integrated with existing EISs, used by business users and consumers. This research focuses on the development of a model for Big Data analytics integration with existing EISs. We propose Big Data analytics integration based on schema on read modeling approach, and three scenarios of integration: 1) integration on the corporate data model level and on the data warehouse level, 2) integration on the corporate data model level and on the report level and 3) integration on the corporate data model level, on the data warehouse level and on the report level. In order to evaluate our approach we have realized the case study in the traffic domain. We carried out custom analysis of road traffic data on Hadoop platform and integrate it with the local SQL Server database, custom traffic geo-application and Business Intelligence tool, according to proposed integration approach.

Abstract: In the fourth industrial revolution, the current Enterprise Information Systems (EIS) are facing a set of new challenges raised by the applications of Cyber-Physical Systems (CPS) and Internet of Things (IoT). In this scenario, a data-intensive EIS involves networks of physical objects with sensing, data collection, transmission and actuation capabilities, and vast endpoints in the cloud, thereby offering large amounts of data. Such systems can be considered as a multidisciplinary complex system with strong inter-relations between the involved components. In order to cope with the big heterogeneousness of those physical objects and their intrinsic information, the authors propose a notificationbased approach derived from the so-called Notification Oriented Paradigm (NOP), a new rule and event driven approach for software and hardware specification and execution. However, the heterogeneity of those information and their interpretation relatively to an evolving context impose the definition of model-driven patterns based on some formal knowledge modelled by a set of skill-based ontologies. Thus, the paper focuses on the open issue related to the formalisation of such ontology-based patterns for their verification, ensuring the coherence of the whole set of data in each contextual engineering domain involved in the EIS.

Abstract: The transition from traditional to Big Data technologies has put new challenges in front of existing database modeling techniques. The “Design then implement” approach to relational databases modeling, could not be fully applied to “Discover and analyze” databases with rapid data growth. In the context of Big Data, models have preserved a descriptive role: they are used to analyze and understand the architecture of hybrid data structures. From the Model-Driven Engineering point of view, several questions emerge: Could Big Data and hybrid database models represent a formal specification for a solution, which can be transformed to an executable specification for a specific target platform, using automated transformations? Does the refining mechanism have its place in the process of hybrid data structure development? What is the role of modeling languages in the process of hybrid data structure specification? An approach to modeling hybrid databases, based on empirically validated concepts of Model-Driven Engineering, is presented in this paper. The approach was applied during the implementation of Precision Agriculture Based Information Service (PAIS) application – a web-based service for the collection and archiving of diverse data on crops, and their presentation to farmers.

Abstract: The e-health platform Pinga is an integrated health information system represents a central electronic system, in which all medical and health related information about patients, health workers, facilities, documents, and procedures is stored and processed. The platform is implemented in Serbia as MojDoktor (My Doctor), and Macedonia as MojTermin (My Appointment). The architecture of the system was designed to allow for process oriented development with agile methodologies. This methodology allowed for fast deployment and adoption but a change in the architecture to a more formal approach is required to assure its extensibility, soundness, interoperability and standardization. In this paper we propose a transformation of the design framework from Process Oriented to a Model-Driven Architecture.

Abstract: Operations over data represent the main functionality of web business applications. User-specific operations extend the set of common create, read, update, and delete (CRUD) operations. As user-specific operations require an underlying algorithm definitions, they are not as generic as CRUD operations are. By this, user-specific operations do not have an extensive model-driven development support and the abstract models of existing model-driven tools do not support such algorithms by default. Without a proper development support, developers use custom manual workarounds to develop user-specific operations. This paper proposes a new model-driven approach that supports user-specific operation definitions through visual algorithm modeling. The approach also supports an automated code writing from the aforementioned algorithm model. The proposed approach supports unified declaration of the same user-specific operation across different platforms. By this, the same operations would not have a different declaration for different platforms and a portability of operations would not require an extra implementation effort.

Abstract: Increase in number of internet users in recent years conditioned reportion of work between client and server applications. Client applications had to become richer, aware of data model and states. Front-end developers now have to shape not only views but also states, transitions, communication with server side etc. Each of those requires mastering different formalism that can represent an additional burden for a developer. The main goal of this implementation is to provide a uniform and consistent manner of describing graphical user interface (GUI) for data-oriented web application on higher abstraction level. This paper presents the implementation of domain-specific language along with code generator developed for this language. GenAn DSL is implemented using TextX meta-language. This project aims to enable users to write simple, platform-independent, model-based application descriptions that include GUI specification. Considerable benefit of this approach is that it leads to significant decrease in the number of code lines necessary for application description, higher component reusability and faster development process.

Abstract: In this paper we present MicroBuilder, the tool used for the specification of software architecture that follows REST microservice design principles. MicroBuilder comprises MicroDSL and MicroGenerator modules. The MicroDSL module provides the MicroDSL domain-specific language used for specification of microservice architecture. In this paper we present the MicroDSL meta-model, specified in Ecore, the concrete syntax of the MicroDSL and examples of its usage. The MicroGenerator module is used to generate executable program code based on MicroDSL specifications.

Abstract: To increase energy efficiency, heat pumps are becoming part of an integral solution for residential heating, ventilation, and air-conditioning. Depending on the electricity tariff, coefficient of performance (COP) and thermal load, the heat pump can also be combined with other heat sources (smart grid). All these new possibilities additionally complicate the heat pump control. In On-Off control, special attention is paid to start-up/shut-down sequences which repeat periodically during the entire life of the heat pump. For the design of control logic for startup/ shut-down sequences, the paper uses the model-driven approach instead of the traditional method. The design is organized around the model which is developed iteratively and incrementally through several use cases. Harel’s statecharts implemented by Matlab tool Stateflow are used in all phases of development: from the specification of requirements up to implementation.

Abstract: Component-based software development (CBSD) offers techniques for extending base functionalities of a software solution, usually through plug-ins. This approach positively affects software's flexibility and potentially its longevity through community contribution. Despite the merits of CBSD and its widespread use, there is no standard way of doing it. In this paper we present a framework for component software in Python, which strives to cover fundamental concepts of CBSD while maintaining the simplicity advocated by the Python community.

Abstract: In the last few decades, there have been a lot of different approaches to the information system (IS) design and development. They are based on different data models. The selection of a data model for conceptual design depends on the problem domain, the knowledge and the personal preferences of an information system designer. The approaches based on the Extended Entity-Relationship (EER) data model are broadly accepted throughout the community of IS designers. In the recent years the eXtensible Markup Language (XML) has spread to many application fields and it is being widely adopted as a standard language for data representation, exchange and storage. As the number of possible usages of XML grows, so does the need of easy management of XML data sources and their integration. The XML documents represent real-world aspects defined by a conceptual schema expressed by means of a selected data model. Having different model representations is an advantage for the flexibility they offer to the user in selecting the right representation based on their requirements. On the other side, model-based heterogeneity raises challenges for schema interoperability. In the paper we use model-driven approach to address the problem of interoperability between an EER database schema and XML schema specification. The presented transformation of a schema expressed by concepts of EER model to an equivalent schema expressed by means of XML data model is integrated into Multi-Paradigm Information System Modeling Tool that provides a model-driven approach to information system development and evolution.

Abstract: the data quality assessment method specially customized to environmental measurement systems is presented in this paper. The methodology is based on mathematical data relations and may be used in any acquisition system where sufficient data redundancy or functional correlations between measured quantities exist. It relies on quantitative comparison of the measured data and predicted data with the result that is expressed in the form of continuous data quality grade. It may be customized to be used on-line, with real-time data streams, for model data assimilation purposes or off-line for off-line applications and analysis. The system is tested on both testing data, where the capabilities of the framework were defined and on real world real time data, obtained from the system of cascading dams. Although the system provided fairly good results in the real time use, some pitfalls were identified that are mostly focused on the data quality interpretation once that the data quality grade is obtained.

Abstract: In the field of Water Distribution Network (WDN) analysis sectorisation is regarded as one of the main strategies for efficient management of WDNs. This paper presents an ICT-based solution which helps the managers to design the optimal number and size of sectors within the WDN. Presented algorithm relies on Graph Theory to search for the Strong Connected Components (SCCs) within the graph (water network), that are later topologically sorted and aggregated into sectors. Process of aggregation is driven by engineering criteria and relevant heuristics (e.g. sectors of approximately equal size with smallest number of links connecting them). Interventions in the network are not implemented in order to avoid negative effects on the networks’ hydraulics. This is important especially at primary stages of sectorisation in which preserving hydraulic performance and minimal investment are the main objectives. Methodology is illustrated on the real size WDN. Preliminary results show that new algorithm is efficient in terms of the computational speed and has the potential to be used inside an optimization algorithm where multiple alternatives and scenarios have to be investigated. Also, due to its robustness, algorithm can serve as an effective support tool for decision making process in engineering practice.

Abstract: In this paper, a novel data assimilation method for improved management of open channel hydraulic system is presented. As opposed to standard data assimilation methods, which either require linearization of the used numeric simulation model or time-consuming data analyzing steps, the proposed method involves the use of “full” free surface flow equations and simple data correction step based on the proportional-integral-derivative (PID controller) theory. The novel method is verified in the case of river Danube and reservoir management of the Iron Gate I and Iron Gate II power plants. As described in this paper, the presented data assimilation methodology is particularly suitable in the operational management of systems with high inertia, such as reservoirs on the Danube River.

Abstract: This paper addresses the issue of providing data quality exchange in data management platforms to ensure adequate data interpretation across different systems. Data quality metadata exchange is analyzed in data exchange in hydrology and hydropower monitoring, with emphasis on exchange of data for further use in mathematical modeling. A service-oriented solution is proposed in data quality modelling and exchange for seamless integration in existing data management platforms. Examples from hydro-information system for Drina river basin are used throughout the paper.

Abstract: A procedure for modelling of mean monthly flow time series using records of the Great Morava River (Serbia) is presented in this paper. The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components. The former component can be further decomposed into a composite trend and two periodic components. The deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). The results infer a lower value of Kling-Gupta Efficiency in the case of transfer functions, whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output.

Abstract: Data gathered through decades of hydrological monitoring practice, often through elaborate measurement strategies is crucial for the process of decision making in electrical energy production. There is a substantial need for temporal and spatial integration of gathered data which is best achieved by accessible software solutions. We have developed hydro-information software system to handle data complexity through acquisition, organization, analysis, storage, and data presentation to end users. We have used modified server-client architecture by combining the advantages of non-structured databases with the power of modern web-development techniques. Our system is now installed and operational in hydro-electric power industry environment, and its users now have intuitive, flexible, and detailed access to all aspects of the gathered data as well as facilities to model, observe and forecast future data.