We’re innovating today for tomorrow’s solutions
Innovation Start Hub Director
[email protected]
+30 2310 500 181
The Research and Innovation Department of DOTSOFT is the key driver of the company’s technological evolution and differentiation, shaping the future of the solutions it offers. Through continuous participation in European and international research programmes, the company maintains an active presence in the innovation ecosystem and integrates the latest technological developments into its services.
The Department operates as a dynamic knowledge hub, where research, technology and business application are combined to create new products and services. Through collaborations with universities, research institutions and international organisations, access to cutting-edge expertise is ensured, along with the development of high added-value solutions.
Our activities include:
Get to Know Us!
Our team is a dynamic group of engineers, developers and researchers, aiming to expand the boundaries of technology. With expertise in Artificial Intelligence, Machine Learning, IoT, Extended Reality and Data Spaces, we turn ideas into real-world solutions.
Guided by collaboration and creativity, we develop cutting-edge technologies that shape the fields of smart cities, tourism, culture, health, construction, mobility and transport. Our vision is to harness innovation to improve digital experiences, making technology more intuitive, efficient and meaningful.
Dr. DESPOINA ELISAVET FILIPPIDOU
Innovation Start Hub Director
PhD in Information Systems Development and MSc in Computer Engineering. She has more than 20 years of experience in the ICT sector and in the management of complex technology projects. She specializes in strategic planning, the coordination of multidisciplinary teams, and the implementation of innovative digital solutions with significant operational and social impact. She is distinguished by her integrated approach to technological innovation, organizational efficiency, and successful project management at both national and European levels.
GIORGOS KARANASIOS
R&D Software Engineer
Giorgos is a Software Engineer with an MSc in Advanced Computer Science and a BSc (Hons) in Computer Science with Industrial Placement. He has expertise in software development, AI, machine learning, and robotics, with strong programming skills in Python, Java, JavaScript, C#, and modern C++. His experience includes developing intelligent navigation and path-planning algorithms for autonomous robots. He is passionate about high-quality software engineering and innovative solutions in robotics and AI-driven automation.
GIOULI CHALITSIOU
Planner - Project Economist
Giouli is a Financial and Project Management Specialist holding a Bachelor's Degree in Accounting, with more than 20 years of experience in the financial management of national and European co-funded projects. She specializes in budget planning, financial monitoring, reporting, and compliance with funding requirements. She has extensive experience in the management of ESIF-funded and European programmes, contributing to the effective utilization of resources and the successful implementation of complex projects. She is recognized for her organizational skills, reliability, and efficient management of financial processes.
ZOI BEGKA
Accounting and Financial Management
Zoi is a professional in the fields of Accounting and Financial Management, holding a Bachelor's Degree in Accounting. She possesses strong analytical skills and significant experience in accounting and financial management. She specializes in the use of ERP systems and has proven expertise in optimizing financial operations, managing banking transactions, and supporting projects funded by European programmes. Her attention to detail contributes to effective financial management.
NANTIA SALMAN
Administrative Assistant – Proposals
Nantia is an experienced Office Administrator and HR Manager with a Master’s degree in Real Estate Economics and more than 10 years of experience in administrative support, human resources management, and project coordination. She is a graduate of Panteion University and holds a Master’s degree in Real Estate Economics. She supports proposal and project management, collaboration with partners, and the preparation of documentation, ensuring accuracy, consistency, and high-quality deliverables.
STEPHANIE ZYGOMANI
Project Officer - Communications & Reporting
Stephanie is a Project Officer with a Diploma in Journalism and more than five years of experience in strategic communication, digital marketing, and European Union-funded projects. She specializes in project coordination, proposal development, results dissemination, and the design of communication strategies for international initiatives. She has experience in planning and implementing communication activities, developing dissemination and exploitation plans, and supporting the successful delivery of projects that promote innovation, social impact, and sustainable development.
NIKOS GKOUTZAS
Full Stack Web Developer
Nikos is a Full Stack Web Developer with a Master’s degree in Computer Science and Computer Engineering. He specializes in Python, Django, FastAPI, and SQL technologies, and has a strong background in robotics through his postgraduate research. He has many years of experience with Arduino and Raspberry Pi platforms, as well as in the design and implementation of robotic systems. He combines technical expertise and hands-on experience to develop innovative and reliable technological solutions.
ANNA SFAIROPOULOU
Application Analyst
Anna is an Application Analyst with a PhD in Computer Science and Digital Communications. She has a strong background in requirements analysis, acting as the link between business needs and technical development teams. In parallel, she is an experienced Full Stack Web Developer with a passion for software development and knowledge sharing. She combines analytical thinking, technical expertise, and effective communication, contributing to the transformation of complex requirements into practical and efficient digital solutions.
VASILIS ARGYROS
Mobile Software Engineer
Vasilis is a Mobile Software Engineer with a Bachelor’s degree in Information Science and Informatics and five years of experience in designing, developing, and scaling high-standard mobile applications. He specializes in Flutter, building solutions for both Android and iOS with a focus on clean architecture, high performance, and maintainable code. He has strong leadership skills and transforms complex requirements into reliable, user-friendly experiences, aligning technology with business objectives.
ALKIS KIPOUROS
AI Engineer
Alkis is a Software Developer with a Master’s degree in Data Science, specializing in research on Natural Language Processing (NLP) and Large Language Models (LLMs). He leverages Python, Java, and SQL programming languages to develop innovative, high-impact solutions.
SOTIRIS KARAVARSAMIS
AI Engineer
Sotiris is an AI Engineer holding two Master’s degrees in Computer Science and Applied Mathematics, as well as a Bachelor’s degree in Computer Science and Engineering. He has significant experience in machine learning, computer vision, and complex networks. He has worked as a research associate on European Union-funded projects. He combines a strong theoretical background with practical experience in developing computational methods for machine learning and computer vision applications.
PANAGIOTIS PALTSOKAS
AI Engineer
Panagiotis is an AI Engineer with a strong background in Mathematics and a Master’s degree in Data Science and Machine Learning. He focuses on developing reliable and well-structured machine learning solutions, with a particular interest in Natural Language Processing (NLP), predictive modeling, and the design of data-driven systems. He emphasizes clear architecture, practical implementation, and continuous improvement, combining analytical thinking with a collaborative mindset.
KOSTAS NTELIAS
Technical Project Manager
Kostas is a Technical Project Manager with a strong academic background in Computer Science, holding a Master’s degree in Computer Science and an Executive MBA. He specializes in software project management and digital transformation, with a focus on Agile methodologies, coordination of multidisciplinary teams, and structured management of complex projects. He combines technical expertise with business acumen and effective leadership, promoting collaboration, clear communication, and practical problem-solving to achieve successful outcomes.
ANTONIA AXARLI
UI/UX & Web Designer
Antonia is a UI/UX & Web Designer with an academic background in Automation Engineering and a Master’s degree in Interactive Systems Technologies. She specializes in creating human-centered digital experiences, focusing on website design, WordPress development, wireframing, and interface usability. She combines design thinking with technical understanding, contributing effectively to both the design and implementation of projects. She emphasizes simplicity, functionality, and collaboration, delivering practical and efficient digital solutions.
PANAGIOTIS GEORGITSEAS
AI Engineer
Panagiotis is an AI Engineer with a Bachelor’s degree in Computer Science and specialization in Generative Artificial Intelligence, Large Language Models (LLMs), and multi-agent systems. He focuses on developing end-to-end artificial intelligence solutions, with an emphasis on RAG architectures, spatiotemporal modeling, and scalable machine learning infrastructures. He has experience in predictive systems and data-driven applications, combining analytical thinking, practical implementation, and effective collaboration.
ARIS CHEIMARIOTIS
AI Manager
Aris is an AI Manager and Research Scientist with a PhD and MSc in Medical Informatics, as well as an MSc and BSc in Electrical and Computer Engineering. He has more than 10 years of experience in European Union-funded research projects, specializing in the design and development of machine learning and data-driven solutions. He focuses on deep learning and applied artificial intelligence, and has authored scientific publications while leading research activities in multidisciplinary teams. He combines analytical thinking with practical experience, developing innovative solutions with measurable impact.
ATHANASIOS SMPOUKIS
AI Engineer
Thanos is an AI Engineer with an Integrated Master’s degree (Diploma) in Computer and Information Engineering. He specializes in deep learning and computer vision, with experience in object detection, semantic image segmentation, and the development of artificial intelligence systems for production environments. He focuses on building practical and scalable machine learning solutions, combining analytical thinking, research experience, and model optimization. His work centers on transfer learning, architecture optimization, and error analysis, aiming to improve the performance of real-world AI applications. With a strong commitment to problem-solving, he develops research-driven and production-ready solutions for complex challenges.
PANAGIOTIS PANAGIOTOU
AI Engineer
Panagiotis is an AI Engineer with a Master’s degree (MSc) in Data Science, a Master’s degree (MSc) in Law and Information & Communication Technologies, and a Bachelor’s degree (BSc) in Software Engineering. He specializes in Machine Learning and Artificial Intelligence, with a focus on Natural Language Processing (NLP), agentic AI systems, generative artificial intelligence, time series forecasting, and recommender systems. He has experience leading innovation projects and developing applied AI solutions in the domains of Smart Cities, Cybersecurity, and Education. He combines research and industry experience, with a strong interest in Responsible Artificial Intelligence and compliance with the EU AI Act.
FILIPPOS KITSOS
Software Developer
Filippos is a Software Developer with more than six years of professional experience. He holds an MCA in Computer Engineering and specializes in software architecture design and the development of reliable and scalable software solutions, ranging from web applications to artificial intelligence systems. He combines technical expertise, creativity, and practical problem-solving skills with the aim of building efficient and innovative technological solutions. In his free time, he enjoys exploring nature on his motorcycle, engaging with music, and spending time with friends.
VAIOS CHRISTODOULOU
Mobile Software Engineer
Vaios is a Mobile Software Engineer with a Master’s degree in Electrical and Computer Engineering. His interests include Linux systems, mobile application development, and robotics. He enjoys playing, creating, and listening to music, exploring himself through artistic expression.
THOMAS PANTOS
Flutter Mobile App Developer
Thomas is a Flutter mobile application developer with a Bachelor’s degree in Computer Science and experience in developing applications using Flutter, as well as for iOS and Android platforms. He has experience in building e-commerce and Internet of Things (IoT) applications, with a strong focus on creating user-friendly, scalable, and easily maintainable mobile solutions. He is committed to continuous learning, clean code practices, and delivering high-quality products through collaboration, teamwork, and innovation.
GIORGOS LIVITSANOS
Full Stack Engineer
Giorgos is a Full Stack Engineer with a background in Mechanical Engineering, specializing in transforming complex research and engineering requirements into reliable, end-to-end software solutions. His work covers system architecture, backend services, data pipelines, interactive web applications, and systems with integrated artificial intelligence. He combines an engineer’s analytical mindset with hands-on development experience, focusing on clean architecture, efficient problem-solving, scalability, and effective multidisciplinary collaboration.
Publications – Presentations – Case Studies
Knowledge Graphs and Machine Learning in the Detection of Fake News and Misinformation
- The expansion of digital platforms has enabled the rapid spread of misinformation, which poses significant social, political, and economic challenges. Knowledge Graphs (KGs) are emerging as powerful tools for enhancing the accuracy, interpretability, and scalability of fake news detection systems, addressing key limitations of traditional machine learning approaches that rely primarily on linguistic features.
This study presents a literature review synthesizing recent research on the application of Knowledge Graphs in misinformation detection. It examines how KGs improve detection by representing real-world relationships, enabling contextual reasoning, and increasing model interpretability, while also discussing existing limitations related to scalability, data completeness, and adaptability to specific contexts.
The reviewed studies highlight the need for future research focused on developing scalable, real-time, and multilingual Knowledge Graph-based models aimed at strengthening misinformation detection capabilities at a global scale.
In addition, preliminary findings from two case studies are presented, illustrating a Knowledge Graph construction methodology that can serve as a practical framework for addressing the spread of misinformation.
Dynamic Demand Forecasting with AI: Maximizing Value in the Supply Chain
- This paper presents the development of a dynamic demand forecasting platform designed for accurate demand prediction and for quantifying its added value in supply chain management, leveraging advanced machine learning and business management algorithms.
The platform collects and processes real-time data from multiple sources, such as point-of-sale systems, e-commerce transactions, competitor pricing strategies, weather conditions, and consumer sentiment data. By integrating environmental and product-related parameters, the platform optimizes inventory levels, delivery routing, and overall operational efficiency.
The solution achieves high forecasting accuracy by using models such as XGBoost and Support Vector Regression (SVR), which consistently outperform traditional forecasting methods. At the same time, it is designed to be scalable, cost-effective, and user-friendly, making it particularly suitable for small and medium-sized enterprises (SMEs), which often face technological and financial constraints in adopting advanced forecasting systems.
The implementation of the system significantly enhances decision-making processes, leading to reduced operational costs and improved customer satisfaction.
Real-World Case Study on Sales Forecasting Using Artificial Intelligence
- 28th Panhellenic Conference on Progress in Informatics with International Participation (PCI),
13–15 December 2024, Athens, Greece
Accurate sales forecasting is a critical factor for optimizing operations and supporting strategic decision-making in the e-commerce sector. This study presents a comprehensive review of the existing literature on sales forecasting algorithms, covering both traditional statistical approaches and advanced artificial intelligence (AI) techniques.
Using a dataset from one of the largest e-commerce companies in Greece, multiple AI-based models are applied to forecast sales trends. Preliminary results highlight the effectiveness of AI algorithms in identifying complex patterns within the data, achieving higher prediction accuracy compared to conventional methods, especially when semantic data from external sources such as weather conditions and Google Trends are incorporated.
These findings emphasize the potential of integrating artificial intelligence into sales forecasting processes, contributing to improved competitiveness and operational efficiency in e-commerce businesses.
White Paper: Cancer Survivorship and AI for Wellbeing – A Collaborative Approach from European Research Projects
- A White Paper has been published presenting the outcomes of the collaboration among 11 CORDIS projects, offering valuable recommendations and research directions in the fields of wellbeing, cancer, and artificial intelligence in healthcare.
This document is the result of synergies between multiple Horizon 2020 projects participating in the “Cancer Survivorship and AI for Wellbeing (CS-AIW)” cluster, aiming to promote innovative approaches to improving patients’ quality of life and care delivery.
The BD4QoL project made a significant contribution, particularly to Chapter 4, titled “Lessons learned from our collaboration,” which focuses on the conclusions and insights derived from the cooperation among the participating research projects.
Smartphone-Based Strategy for Monitoring the Quality of Life of Head and Neck Cancer Survivors
- Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST, Volume 488)
- Monitor real-time infrastructures and apply self-management practices through active symptom reporting,
- Maintain healthy lifestyle habits,
- Interact with an integrated artificial intelligence system acting as an additional communication channel between patient and physician,
- Complete standardized Quality of Life questionnaires via a web platform from home.
IoT systems based on smartphones have the potential to effectively predict and monitor Quality of Life indicators when designed using an appropriate methodology. This paper aims to develop a comprehensive strategy for building a dedicated application to monitor the Quality of Life of head and neck cancer survivors.
First, it presents the results of a literature review on mHealth services for cancer patients. It then describes in detail the clinical study protocol, within which patients are encouraged to:
The challenges encountered, the non-invasive data collection procedures adopted, and the qualitative data derived from physical, social, and behavioral indicators together form a valuable set of guidelines and requirements for future research efforts on post-treatment cancer patient monitoring through wearable IoT devices.
Multicenter Randomized Trial for the Evaluation of Quality of Life Using Non-Invasive Intelligent Tools During Post-Curative Follow-up in Patients with Head and Neck Cancer
- Head and Neck Medical Oncology Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
- Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain
- Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
- Department of Psychology, Educational Science and Human Movement (SPPEFF), University of Palermo, Palermo, Italy
- Information Technology Programme Management Office, DOTSOFT, Thessaloniki, Greece
- DeustoTech, Faculty of Engineering, Universidad de Deusto, Bilbao, Spain
- Institute of Head and Neck Studies and Education, University of Birmingham, Birmingham, United Kingdom
- Division of Epidemiology and Health Care Research, JGU – Johannes Gutenberg University, Mainz, Germany
- Division of Oral and Maxillofacial Surgery – Bristol Dental Hospital, University of Bristol – Bristol Medical School, Bristol, United Kingdom
- Maxillo-Facial Surgery, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
- Maxillo-Facial Surgery, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Bari, Italy
- MultiMed Engineers srls, Parma, Italy
Patients who survive head and neck cancer (HNC) face significant physical, psychological, and socioeconomic burdens. Achieving cancer-free survival while maintaining a high quality of life (QoL) is a key objective in the management of HNC patients, making lifelong monitoring essential.
A particularly ambitious goal is the implementation of this monitoring through advanced analysis of environmental, emotional, and behavioral data, discreetly collected via mobile devices.
This clinical study aims to reduce the proportion of HNC survivors — who completed their treatment between 3 months and 10 years earlier — experiencing clinically significant deterioration in quality of life during follow-up, using non-invasive tools such as patients’ mobile devices.
The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary objective is to assess clinically significant deterioration in quality of life based on the EORTC QLQ-C30 score during a 24-month post-treatment follow-up period. The total sample size is 420 patients.
Participants are randomly assigned either to the BD4QoL monitoring group or to the group receiving standard clinical care. The BD4QoL platform includes an integrated set of services for patient monitoring and empowerment through two main tools:
In both groups, participants complete QoL questionnaires at baseline and every six months, while also following the standard post-treatment clinical follow-up protocol.
Patients in the intervention arm (n=280) gain access to the BD4QoL platform, while those in the control arm (n=140) do not. Key inclusion criteria include completion of treatment for non-metastatic HNC and use of an Android smartphone, while exclusion criteria include ongoing treatment or concurrent cancer diagnoses.
Digital Health for Supporting Head and Neck Cancer Survivors
- CONFERENCE: IEEE BHI-BSN 2021
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’21), jointly organized with the 17th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN’21)
This paper presents the innovative capabilities that technological solutions can offer in supporting patients who have survived head and neck cancer.
The research was conducted within the framework of the BD4QoL project and focuses on leveraging digital health technologies to improve quality of life, monitoring, and overall patient support during the post-treatment period.
Smartphone-Based Strategy for Monitoring the Quality of Life of Head and Neck Cancer Survivors
- EAI PervasiveHealth 2022 – 16th EAI International Conference on Pervasive Computing Technologies for Healthcare
- apply self-management practices through active symptom reporting,
- maintain healthy lifestyle habits,
- interact with an integrated artificial intelligence system acting as an additional communication channel between patient and physician,
- complete standardized QoL questionnaires via a web platform from home.
12–14 December 2022
Thessaloniki, Greece
IoT systems based on smartphones have the potential to effectively predict and monitor Quality of Life (QoL) indicators when designed using an appropriate methodology. This paper aims to develop a comprehensive strategy for creating a dedicated application for monitoring the Quality of Life of head and neck cancer survivors.
First, it presents the results of a literature review on mHealth services for cancer patients. It then describes in detail the clinical study protocol, within which patients are encouraged to:
The challenges encountered, the non-invasive data collection procedures adopted, and the qualitative data derived from physical, social, and behavioral indicators together form a valuable set of guidelines and requirements for future research efforts on post-treatment cancer patient monitoring through wearable IoT devices.
Using Classification for Traffic Prediction in Smart Cities
- Conference paper – First Online: 29 May 2020
Part of the book series IFIP Advances in Information and Communication Technology (IFIPAICT, Volume 583)
Smart Cities emerge as highly complex and interconnected ecosystems, providing smart services and innovative solutions to improve citizens’ everyday lives. This paper examines the use of classification techniques in Smart City projects, with a focus on traffic prediction.
Through a systematic literature review, the main thematic areas and methods used in smart city applications are analyzed, with particular emphasis on data harvesting and data mining processes.
The study investigates whether it is possible to predict traffic load based on historical data and meteorological conditions. The results show that different predictive models can be developed using weather data, with varying levels of accuracy and effectiveness, highlighting the potential of applying AI and machine learning techniques to traffic management in Smart City environments.
Smart Cities Data Classification for the Prediction of Electricity Consumption & Traffic
- Smart Cities are continuously evolving into highly complex and interconnected ecosystems, offering smart services and innovative solutions. These ecosystems generate and exploit large volumes of data from multiple sources, creating new challenges as well as opportunities for the development of efficient smart city services and applications.
- to what extent it is possible to predict electricity consumption and traffic load based on historical data and meteorological conditions,
- which data features are most suitable for prediction or decision-support in the context of energy consumption.
This paper examines the connection between Data Mining techniques and Smart City projects through a systematic literature review that highlights the main thematic areas and methods applied. The study places particular emphasis on data harvesting processes and the analysis of urban data using data mining techniques.
In addition, two main research questions are investigated:
The results show that in both cases effective predictive models can be developed by leveraging weather data, highlighting the potential of applying AI and machine learning techniques in Smart City environments.
Methodology for Traffic Prediction Using Classification: Highlighting the Impact of COVID-19
- Journal: Integrated Computer-Aided Engineering, vol. 28, no. 4, pp. 417–435, 2021
This paper presents an innovative classification methodology for day-ahead traffic prediction. The study investigates whether traffic conditions can be predicted based on meteorological conditions, seasonality, time intervals, and mobility restriction measures related to the COVID-19 pandemic.
Robust predictive models are proposed, leveraging smaller and more targeted datasets. Beyond feature selection, the methodology incorporates new attributes related to COVID-19 mobility restrictions, forming a new data model for traffic analysis.
The proposed approach explores the optimal training subset for model training. The results show that different models can be developed with varying levels of success, while the highest accuracy is achieved when combining all relevant features and using the proposed training subset.
The methodology demonstrates a significant improvement in accuracy compared to previously published approaches, highlighting the potential of AI and machine learning techniques for traffic prediction in Smart City environments.
Methodology for Traffic Prediction Using Classification: Highlighting the Impact of COVID-19
- Issue title: Selection of papers from the 21st EANN (Engineering Applications of Neural Networks) and 16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference
This paper presents an innovative classification methodology for day-ahead traffic prediction. The study investigates whether traffic conditions can be predicted based on meteorological conditions, seasonality, time intervals, and mobility restriction measures related to the COVID-19 pandemic.
Robust predictive models are proposed, leveraging smaller and more targeted datasets. Beyond feature selection, the methodology incorporates new attributes related to COVID-19 mobility restrictions, forming a new data model for traffic analysis.
The proposed approach explores the optimal training subset for model training. The results show that different models can be developed with varying levels of success, while the highest accuracy is achieved when combining all relevant features and using the proposed training subset.
The methodology demonstrates a significant improvement in accuracy compared to previously published approaches, highlighting the potential of AI and machine learning techniques for traffic prediction in Smart City environments.
Game-Based Smart System for the Detection of Developmental Speech and Language Disorders in Children’s Communication: Protocol Toward Digital Clinical Diagnostic Procedures
- Interactive Mobile Communication, Technologies and Learning Conference
- an interactive game-based digital application for the child,
- an online data collection environment for parents and clinical experts,
- a complete functional specification of the game-based activities and the overall system architecture.
IMCL 2021: New Realities, Mobile Systems and Applications, pp. 559–568
Although international literature reports high prevalence rates of developmental speech and language disorders in children (3–17%), many cases remain undiagnosed, depriving children of the opportunity for early and effective intervention.
The use of digital and mobile technologies in healthcare and education creates new opportunities for monitoring, decision-making, classification, and assessment processes. This study presents and validates a design and development protocol for a digital approach supporting screening and early detection of developmental speech and language difficulties in children’s communication.
The proposed solution leverages smart computing models, sensors, and early indicators of language and communication difficulties and is developed in sequential phases. The system design includes:
The proposed smart system has the potential to enhance digital healthcare processes for children’s communication skills, while contributing to positive social and economic impact in line with current trends in digital transformation in healthcare.
Co-Design of User Experience in Location-Based Games for a Museum Network: Participation of Cultural Heritage Professionals and Local Communities
- The design of location-based games (LBGs) in the field of cultural heritage requires the active involvement of local communities and cultural professionals to ensure authenticity, cultural relevance, and content validity.
- field visits,
- design workshops,
- field playtesting,
- field studies.
- the centrality and representativeness of participants,
- the generation of meaningful outcomes through meetings,
- co-creation of content through playtesting,
- the impact of the COVID-19 pandemic on the participatory process.
This paper presents a participatory and co-design approach for the development of LBGs that promote awareness and learning around the intangible cultural heritage of craftsmanship and traditional techniques. The research was carried out within a long-term project covering the entire process, from initial awareness to final implementation.
Following a design thinking methodology, the study presents the participatory methods used to engage cultural heritage professionals, local communities, and visitors of museums and cultural sites. These methods include:
At the same time, the paper analyzes participatory design issues that emerged during the project, such as:
The paper presents a comprehensive case of participatory and co-design development of location-based games for cultural heritage, emphasizing long-term collaboration and continuous stakeholder involvement in the design of three different LBGs for a network of museums and cultural sites.
Framework for Content Personalization in Cultural Routes: Case Study of the TRACCE Project
- Recent advances in digital storytelling technologies in the field of cultural tourism have fostered new trends toward more seamless, interactive, and personalized travel experiences. The development of recommendation systems and personalization techniques creates new opportunities for enhancing the experience of cultural heritage visitors.
This paper presents an innovative narrative system for cultural routes, which combines users’ multimedia content preferences with different typologies of cultural tourists.
The McKercher typology is used as the research foundation, categorizing cultural heritage visitors into five main groups. Through the use of a specially designed questionnaire, an enhanced typology of cultural tourists is developed, in which three additional categories are introduced based on users’ preferences regarding multimedia content.
The proposed approach contributes to the creation of more personalized and interactive cultural experiences, enhancing visitor engagement and interaction within cultural routes and heritage environments.
Redefining Smart Tourism through Smart Gamified and Gaming Applications in Greece
- World Academy of Science, Engineering and Technology – International Journal of Social and Business Sciences
- “Hologrammatic Corfu” for the old town of Corfu,
- “BRENDA” for the Kilkis region.
- photographs,
- 360° videos,
- augmented reality,
- hologrammatic videos.
Vol. 15, No. 01, 2021
Smart technologies are increasingly being used to enhance the travel experience and shape the image of a destination through digital applications and social media platforms. This paper presents the design and implementation of smart management applications that promote culture, sustainability, and accessibility in two different tourist destinations in Greece.
The first case concerns Corfu, a highly popular destination and UNESCO World Heritage Site, where high visitor demand creates challenges in terms of experience organization and infrastructure management. The second concerns Kilkis, a low-tourism and highly seasonal destination, where traditional cultural promotion and local experience strategies have not produced the desired results.
To address these challenges, two different systems were developed:
Although the two systems were designed independently and address different needs, they are based on a common gaming and gamification methodology.
The Hologrammatic Corfu application was designed for the comprehensive exploration of the city before, during, and after the trip, leveraging transmedia content such as:
In addition, statistical analysis of point-of-interest visitation is used to enable dynamic visitor redirection, supporting sustainability, accessibility, and balanced tourist flow management.
The BRENDA application was designed to promote gastronomic and historical tourism, using serious gaming and gamification mechanisms that connect local businesses with cultural points of interest. This approach encourages active participation from both local communities and visitors, fostering sustainable and interactive tourism experiences.
Finally, the paper highlights the potential for reusing gaming mechanisms and transmedia elements in new areas of interest, contributing to the transformation of destinations into modern smart destinations.
Conference Title: ICSTDM 2021 – International Conference on Smart Tourism and Destination Management
Conference Location: Istanbul, Turkey
Conference Dates: 28–29 January 2021
Participatory Urban Design through an Online WebGIS Platform: Functions and Tools
- The present paper introduces an online WebGIS platform for participatory urban design, focusing on its objectives, functions, and tools within the framework of the ppCITY research project.
- the creation of projects and sub-projects according to the stages of participatory urban design,
- the use of libraries of proposed solutions,
- the combination of user profiling and opinion weighting,
- the management and analysis of geospatial data through WebGIS tools.
The development of Information and Communication Technologies (ICT), and in particular WebGIS tools, has significantly contributed to strengthening public participation processes, expanding the involvement of different stakeholder groups in urban planning and decision-making.
The platform is technically based on open-source ppGIS solutions and integrates innovative online tools that support the key methodological stages of participatory design, using real spatial and urban planning challenges.
The system enables:
The main objective of the platform is to highlight key characteristics of public space and to organize processes for meaningful and equal citizen participation in urban planning.
This research approach also aims to create an open community of users, experts, designers, and developers around an open-source ppWebGIS software, enhancing collaboration and participatory innovation in the fields of Smart Cities and urban planning.
