Quality of Service and Optimization in Data Integration Systems

This work presents techniques for the construction of a global data integrations system. Similar to distributed databases this system allows declarative queries in order to express user-specific information needs. Scalability towards global data integration systems and openness were major design goals for the architecture and techniques developed in this work. It is shown how service composition, extensibility and quality of service can be supported in an open system of providers for data, functionality for query processing operations, and computing power.Diese Arbeit präsentiert Techniken für den Aufbau eines globalen Datenintegrationssystems. Analog zu verteilten Datenbanken unterstützt dieses System deklarative Anfragen, mit denen Benutzer die gesuchte Information beschreiben können. Die Skalierbarkeit in einem globalen Kontext und die Offenheit waren hauptsächliche Entwicklungsziele der Architektur und der Techniken, die in dieser Arbeit entstanden sind. Es wird gezeigt wie Dienstekomposition, Erweiterbarkeit und Dienstgüte in einem offenen System von Anbietern für Daten, Anfrageverarbeitungsfunktionalität und Rechenleistung unterstützt werden können

The management systems and the performance indicators: the integration way

The last decade has seen the worldwide proliferation of management systems standards, preceded by a period of nearly twenty years where the quality assurance systems, which evolved later to quality management systems, were the only ones. This diversity of standards accompanied the organizations changing needs in the optimization of its subsystems and systematization of management promoted by market imperatives, customer, statutory regulations, the dictates of regulators of the sector, as well as by concerns of efficiency improvements and operational control. This implied a systematic orientation towards integration of the different management systems. However, in Portugal, after a decade of coexistence of various subsystems, the effective integration is not a current reality. In addition, overlapping and partial integration continues to prevail, either through lack of knowledge or incapacity of those who run the systems, either by structural difficulties of the organizations or even top management options. However, stakeholders learning process - leaders of organizations, consulting, certification or normalization entities - although with rhythms and different approaches, led to a significant development, both in the aspect of regulatory harmonization and consolidation of intra-organizational practices, as well as use of monitoring tools and performance indicators from the perspective of systems optimization in the service of an appropriate response to the increasing demands of the dynamics of current management. The data collection methodology used in this study was supported by a set of semi-structured interviews. The results obtained constitute the scope of the analyses and the conclusions of this publication, with crossing findings to other published studies in this domain. Important findings of this study are that there is not a unique methodology for integration and that there is still an inefficient use of KPI systems for decision support, mainly within the integrated systems. The critical success factors towards the integration of management systems are essentially inner motivation for the integration and top management commitment as well as competent and professional organization governance, regardless the sectors involved

Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks.Comment: 8 pages, 4 figure

Design and Analysis of an Optimized Scheduling Approach using Decision Making over IoT (TOPSI) for Relay based Routing Protocols

This research work focuses on support towards QoS approaches over IoT using computational models based on scheduling schemes to enable service oriented systems. IoT system supports on application of day-to-day physical tasks with virtual objects which inter-connect to create opportunities for integration of world into computer-based systems. The QoS scheduling model TOPSI implements a top-down decision making process over top to bottom interconnected layers using service supportive optimization algorithms based on demandable QoS requirements and applications. TOPSI adopts Markov Decision Process (MDP) at the three layers from transport layer to application layer which identifies the QoS supportive metrics for IoT and maximizes the service quality at network layer. The connection cost over multiple sessions is stochastic in nature as service is supportive based on decision making algorithms. TOPSI uses QoS attributes adopted in traditional QoS mechanisms based on transmission of sensor data and decision making based on sensing ability. TOPSI model defines and measures the QoS metrics of IoT network using adaptive monitoring module at transport layer for the defined service in use. TOPSI shows optimized throughput for variable load in use, sessions and observed delay. TOPSI works on route identification, route binding, update and deletion process based on the validation of adaptive QoS metrics, before the optimal route selection process between source and destination. This research work discusses on the survey and analyzes the performance of TOPSI and RBL schemes. The simulation test beds and scenario mapping are carried out using Cooja network simulator

Knowledge Management as a New Strategy of Innovative Development

Theoretical framework:  The relevance of studying the growing role of knowledge management in the context of ensuring the innovative activity of enterprises is increasing. The results demonstrate a number of important conclusions that have made it possible to expand the existing theoretical and practical aspects of the knowledge management concept.   Design/methodology/approach:  The conducted study in service companies has revealed a low culture of using this concept in the Ukrainian context. In the course of the research, it has been established similar and different features of the influence of knowledge management on innovation of service companies, direct and indirect relationship between variables, complex structure and indirect impact of production, integration, application of information on the speed, quality and number of innovations in enterprises.   Findings:  The complex structure of the relationship between knowledge and innovative activity can be a consequence of insufficient level of personnel competencies, available information and data management technologies, methods and practices of production, integration and application. Therefore, the low innovativeness of the service companies under study in the context of system orientation and technological capabilities of the companies, in particular, in the practice of knowledge management, and the low impact of knowledge and information on the innovation of services and processes on the market of Ukraine have been revealed.   Research, Practical & Social implications:  This is precisely why the strategy of personalization significantly prevails in its use in knowledge management compared to the codification strategy, which is manifested in the limited practice of using systems and technologies for the generation, accumulation, storage, use, dissemination of knowledge.   Originality/value:  In addition, the use of the personalization strategy is explained by the fact that the service companies under consideration focus more on processes, the optimization of which significantly affects interaction with customers and the efficiency of operations

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea