
Alexandros BousdekisNational Technical University of Athens | NTUA
Alexandros Bousdekis
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Publications (59)
Voice assistants, alternatively mentioned as conversational agents or Digital Intelligent Assistants (DIA), represent a new form of interaction between humans and machines, providing fast, intuitive, and potentially hands-free access to systems through voice-based interaction in order to increase the efficiency of certain activities. While the lite...
Process mining is an emerging research field which deals with discovering, monitoring and improving business processes by analyzing and mining data in the form of event logs. Event logs can be extracted by most of the existing enterprise information systems. Predictive business process monitoring is a sub-field of process mining and deals with pred...
Greater cognitive task load and the growing shortage of highly skilled labor provide ground for assistance systems based on Artificial Intelligence (AI). Conventional graphical interfaces to such systems are often hard to understand, obtrusive, and unintuitive. Natural language interactions provide one approach to address this shortcoming. Recently...
The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high c...
As one of the key components of electric vehicles, the Li-ion Battery Management System (BMS) is crucial to the industrialization and marketization of electric vehicles. Developing advanced and intelligent BMSs has been gathering the research interest. However, the internal states of the battery are affected by several factors, thus making the appl...
The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger o...
Developments in Machine Learning (ML) in the last years resulted in taking as granted their usage and their necessity clear in areas such as manufacturing and quality control. Such areas include case specific requirements and restrictions that require the human expert’s knowledge and effort to apply the ML algorithms efficiently. This paper propose...
Industry 5.0 complements the Industry 4.0 paradigm by highlighting research and innovation as drivers for a transition to a sustainable, human-centric and resilient industry. In this context, new types of interactions between operators and machines are facilitated, that can be realized through artificial intelligence (AI) based and voice-enabled Di...
Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. In this context, process mining enables business processes analysis based on their observed behaviour recorded in event logs by providing the means to discover, monitor, and improve processes. During the last years,...
Li-Ion batteries have been widely applied as energy storage systems, such as EVs. Data-driven methods for battery health estimation and prediction are gaining increasing interest in both academia and industry. These methods have been driven by recent advances in ML that exploit the large amounts of available data to improve BMS performance. This di...
Industrial maintenance strategies increasingly rely on artificial intelligence to predict asset conditions and prescribe maintenance actions. The related maintenance software and human maintenance actors can form a hybrid-augmented intelligence system where each side benefits from and enhances the other side's intelligence. This system requires opt...
The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produ...
Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely...
Augmented analytics is an emerging topic which deals with the enhancement of analytics with conversational interfaces as well as the exploitation of the human knowledge representation through intelligent digital assistants allowing users to easily interact with data and insights. The communication with the user by voice poses new challenges to the...
Business analytics use advanced techniques that can analyze and process large and diverse data sets in order to generate valuable insights and lead to better business decisions. Of the three types of business analytics – descriptive, predictive, and prescriptive – only the latter focus on decision making. This paper aims to address two limitations...
The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention; however, prescriptive analytics has just started to emerge as the nex...
Due to the emergence of sensing technology, a large number of sensors is used to monitor the health state of manufacturing equipment, thus enhancing the capabilities of predicting abnormal behaviours in (near) real-time. However, existing algorithms in predictive maintenance suffer from several limitations related to their scalability, efficiency,...
Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only t...
Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines direc...
Predictive maintenance systems are socio-technical systems where the interaction between maintenance personnel and the technical system is critical to achieving maintenance goals. Employees who use a predictive maintenance system should explore, modify, and verify their analysis and decision-making methods and rules. Conventional modes of interacti...
The recent advancements in Internet of Things (IoT) technology and the increasing amount of sensing devices that collect and/or generate massive sensor data streams enhances the use of streaming analytics for providing timely and meaningful insights. The current paper proposes a framework for supporting streaming analytics in edge-cloud computation...
The emergence of Artificial Intelligence (AI) reveals new opportunities in Industry 4.0 environments. However, the lack of appropriate data and the requirements for trustworthiness pose significant challenges in the applicability and the effectiveness of AI systems in manufacturing environments. On the other hand, Industry 4.0 enables new types of...
Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine l...
Big data analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at providing meaningful insights and supporting optimal decision making under time constraints. In this direction, prescriptive analytics has just started to emerge. Prescriptive analytics moves beyond descriptive and predictive analytics aiming at providing...
The emergence of Industry 4.0 enhances the capabilities of predictive maintenance and paves the way for efficient and optimized maintenance operations. Until now, the technical implications of adopting predictive maintenance solutions in Industry 4.0 environments have been reported in various studies. However, the business perspective is usually no...
Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future,...
The fourth industrial revolution is characterized by the introduction of the Internet of Things (IoT) into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The key issue of any design and system development in the context of Industry 4.0 is the proper implementation of Reference Architectu...
Since industrial maintenance is a key operation, modern manufacturing firms need to minimize maintenance losses and to improve their overall performance. In addition, emerging information technologies such as the Internet of things (IoT), cyber-physical systems, proactive computing and big data analysis in the context of Industry 4.0 are able to en...
Data analytics has gathered a lot of attention during the last years. Although descriptive and predictive analytics have become well-established areas, prescriptive analytics has just started to emerge in an increasing rate. In this paper, we present a literature review on prescriptive analytics, we frame the prescriptive analytics lifecycle and we...
The emergence of Industry 4.0 has led to a wide use of sensors which have facilitated manufacturing operations. Predictive maintenance has significantly benefited from these technological advancements with the use of real-time detection and prediction algorithms regarding future failures. During the last years, there is also an increasing interest...
This chapter presents the Proactive Sensing enterprise (ProaSense) platform that facilitates proactive decision making in predictive maintenance, as well as the results and the lessons learned from its deployment in HELLA Saturnus Slovenija, an automotive lighting equipment industry. It describes the HELLA use case and presents the ProaSense platfo...
In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there...
High-value, complex products such as aircraft are not produced by a single organization at a single production plant. Rather, a network of highly specialized suppliers collaborates with often geographically distributed OEM production plants focused on specific parts of the product. This means that a suitable predictive maintenance strategy needs to...
Manufacturing operations can take substantial advantage of the proactivity concept by utilising event-driven information systems, able to process the sensor data and to provide proactive recommendations. Despite the recent advances in technology and information systems and the variety of methods for prognosis, decision models for joint maintenance...
The evolution of Internet of Things (IoT) has significantly contributed to the development of the sensing enterprise concept and to the use of appropriate information systems for real-time processing of sensor data that are able to provide meaningful insights about potential problems in a proactive way. In the current article, the authors outline a...
The emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In...
The increasing use of sensors in manufacturing enterprises has led to the need for real-time data-driven information systems capable of processing huge amounts of data in order to provide meaningful insights about the actual and the predicted business performance. We propose a framework for real-time, event-driven proactive supplier selection drive...
The emergence of the Internet of Things paves the way for enhancing the monitoring capabilities of enterprises by means of extensive use of physical and virtual sensors generating a multitude of data. The generated real-time data streams provide the basis for anticipating future undesired events and thus enable enterprises to decide and act ahead o...
Maintenance is related to all the processes of a manufacturing firm and focuses not only on avoiding the equipment breakdown but also on improving business performance. However, the selection of the most appropriate maintenance strategy for a manufacturing company is a challenging task, since different maintenance strategies should be applied accor...
Proceedings of the 4th Student Conference of Hellenic Operational Research Society (HELORS) 2015 (17-18 December), pp. 60-65.
Η παρούσα ερευνητική δουλειά παρουσιάζει ένα Σύστημα Υποστήριξης Αποφάσεων (ΣΥΑ) ως μέρος μιας γενικής αρχιτεκτονικής για την υποστήριξη λήψης προδραστικών αποφάσεων στις βιομηχανικές επιχειρήσεις και μιας ολιστικής προσέγγισης της Διαγνωστικής Συντήρησης. Πιο συγκεκριμένα, παρουσιάζει την χρήση μιας γραφικής διεπαφής για την συλλογή και διαχείριση...
We outline a new architecture for supporting proactive decision making in manufacturing enterprises. We argue that event monitoring and data processing technologies can be coupled with decision methods effectively providing capabilities for proactive decision-making. We present the main conceptual blocks of the architecture and their role in the re...
Purpose
– The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the conte...
In manufacturing enterprises, maintenance is a significant contributor to the total company's cost. Condition Based Maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the current and predicted health state of equipment. Although decision support for CBM is not an extensively explored area, there ex...
In this paper we present a visionary approach about a new architecture for supporting proactive decision making in enterprises. We argue that a cognitive approach of continuous situation awareness can enable capabilities of proactive enterprise intelligence and propose a conceptual architecture outlining the main conceptual blocks and their role in...