@conference {801, title = {An Interface for User-Centred Process and Correlation Between Large Datasets}, booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: Design, User Experience, and Usability: UX Research and Design}, year = {2021}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Standard database query systems are designed to process data on a single installation only, and do not provide optimal solutions for cases that data from multiple sources need to be queried. In these cases, the sources may have different data schemata, data representations etc., necessitating extensive coding and data transformations to retrieve partial results and combine them to reach the desired outcome. Differences in schemata and representations may be subtle and remain unnoticed, leading to the production of erroneous results. The goal of this paper is to present an easy-to-use solution for the end users, enabling them to query data from a given set of databases through a single user interface. This user interface allows users to visualize database contents and query results, while facilities for uploading and validating the data are also accommodated. To demonstrate the applicability of our approach, a use case is presented where data from two different sources are uploaded into the system and thereafter the data from the two databases can be utilized in tandem. The usability evaluation involved software developers in free evaluation scenarios.

}, keywords = {big data, Multiple data sources, Query, Usability, user interface, Visualisation}, isbn = {978-3-030-78221-4}, doi = {https://doi.org/10.1007/978-3-030-78221-4_32}, author = {Spiliotopoulos, Dimitris and Giannakopoulos, Theodoros and Vassilakis, Costas and Wallace, Manolis and Lantzouni, Marina and Poulopoulos, Vassilis and Margaris, Dionisis} } @conference {802, title = {A User Interface for Personalising WS-BPEL Scenarios}, booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations}, year = {2021}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Due to the huge volume of web services available, both locally and in the cloud, the performance of users and systems need significant research attention. Since WS-BPEL is the dominant language for orchestrating individual web services into business processes, by composing WS-BPEL scripts/scenarios, graphical notations facilitating WS-BPEL design can be extremely useful. Current user interfaces allow WS-BPEL designers not only to invoke selected web services, but also to explicitly ask for recommendations. Then, the user interface appends the services achieving the highest score, according to the attributes{\textquoteright} importance, set by the designer, to their WS-BPEL scenario. However, since the final selection is produced automatically, rather than relying on the designers{\textquoteright} choices, many times, from a personalisation point of view, the adaptation fails. This work reports on the design, development and user evaluation of a user interface that incorporates functionalities that support the designers{\textquoteright} selection performance, thereby upgrading the personalisation level of the WS-BPEL scenarios, as well as the success of the adaptation.

}, keywords = {Personalisation, Recommender Systems, Usability, User evaluation, user interface, Web services, WS-BPEL scenarios}, isbn = {978-3-030-77750-0}, doi = {https://doi.org/10.1007/978-3-030-77750-0_25}, author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Vassilakis, Costas} } @article {782, title = {Data-Assisted Persona Construction Using Social Media Data}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {aug}, pages = {21}, abstract = {

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.

}, keywords = {Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/bdcc4030021}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {723, title = {A Fine-grained Social Network Recommender System}, journal = {Social Network Analysis and Mining}, volume = {10}, year = {2020}, abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.}, keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability}, issn = {1869-5469}, doi = {10.1007/s13278-019-0621-7}, author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos} } @article {781, title = {Multimodal Interaction: Correlates of Learners{\textquoteright} Metacognitive Skill Training Negotiation Experience}, journal = {Information}, volume = {11}, year = {2020}, month = {jul}, pages = {381}, abstract = {

Metacognitive training reflects knowledge, consideration and control over decision-making and task performance evident in any social and learning context. Interest in understanding the best account of effective (win-win) negotiation emerges in different social and cultural interactions worldwide. The research presented in this paper explores an extended study of metacognitive training system during negotiation using an embodied conversational agent. It elaborates on the findings from the usability evaluation employing 40 adult learners pre- and postinteraction with the system, reporting on the usability and metacognitive, individual- and community-level related attributes. Empirical evidence indicates (a) higher levels of self-efficacy, individual readiness to change and civic action after user-system experience, (b) significant and positive direct associations between self-efficacy, self-regulation, interpersonal and problem-solving skills, individual readiness to change, mastery goal orientation and civic action pre- and postinteraction and (c) gender differences in the perceptions of system usability performance according to country of origin. Theoretical and practical implications in tandem with future research avenues are discussed in light of embodied conversational agent metacognitive training in negotiation.

}, keywords = {big data, Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/info11080381}, author = {Dimitris Spiliotopoulos and Eleni Makri and Costas Vassilakis and Dionisis Margaris} } @article {770, title = {A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {4/2020}, pages = {6}, abstract = {This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.}, keywords = {cultural experience, intelligent interaction, Mixed Reality, semantics, Usability}, doi = {https://doi.org/10.3390/bdcc4020006}, author = {Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos and Dionisis Margaris and Vlasios Kasapakis and Christos-Nikolaos Anagnostopoulos and Georgios Santipantakis and George A. Vouros and Theodore Kotsilieris and Volha Petukhova and Andrei Malchanau and Ioanna Lykourentzou and Kaj Michael Helin and Artem Revenko and Nenad Gligoric and Boris Pokric} }