@conference {824, title = {Implementing the infrastructure for Dimitris Papaioannou{\textquoteright}s archive:approaching the degrees of separation in his work}, booktitle = {DARIAH 2023 (poster presentation)}, year = {2023}, abstract = {

Creating the infrastructure for an archive is considered, nowadays, to be a trivial procedure. Several tools (e.g. Omeka [2], CollectiveAccess[3], CollectionSpace[4]) and prototypes (eg. Dublin Core [5], VRA CORE [6]) are available for use in order to, either directly install and use a CMS for cultural related data (first case), or build a system {\textquotedblleft}from scratch{\textquotedblright} based on a common prototype (second case). This procedure would lead to the digitization of at least an archive{\textquoteright}s metadata and to the organization of the archive itself; it would offer the ability to search for information in the archive in an easy way, it would allow people to access the information of the archive, and it could also be part of a larger cultural data lake (Europeana). The solutions for these use cases, are extant, present and available. On the other hand, occasionally one comes across archives of artists that have a special view of art, are ahead of their time, design their own universe, and they keep evolving it throughout their life. In this paper, we discuss the implementation of the archive for Dimitiris Papaioannou, a diverse archive which includes several different types of data. In this case, we have selected a special approach, in order to fully capture the artistic aspects of the archive{\textquoteright}s content, which was analyzed in the context of the GENESIS project [1]. Throughout this project, the procedure through which the artist approaches the performance arts and generates ideas was recorded and analyzed. Following the aforementioned analysis, the archive material provides insight for the artist{\textquoteright}s visual arts, his dancing roads and his performances, and unveils his approach to performance arts (or art, in general). Then, the archive is structured in a way that would allow all archive visitors, including the general public as well as researchers, to obtain information about the individual elements of the work, but also locate the {\textquotedblleft}genetic material{\textquotedblright} of the artist into the archive. To achieve this goal, interdisciplinary research involving the cooperation of scientists and researchers from both the humanities and the technology domains was required. The research team designed an information layer entailing objective information (e.g. works, performances, participants and their roles etc.) and subjective information (e.g. tagging or free text descriptions), and accommodating semantic links between them. Metadata were also associated to information items, where applicable. The information layer fully serves the needs for information search and retrieval, while it additionally provides the underpinnings for data mining procedures that can possibly reveal patterns or data correlations that have remained unobserved.

}, keywords = {cultural informatics, Cultural Metadata, Cultural Semantics, Performance art}, author = {Vassilis Poulopoulos and Eleni Papalexiou and Costas Vassilakis and Avra Xepapadakou and Valia Vraka} } @inbook {808, title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {51{\textendash}76}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.

}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @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} } @article {margaris-IJBDI-2019-b, title = {Improving collaborative filtering{\textquoteright}s rating prediction accuracy by considering users{\textquoteright} dynamic rating variability}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {59--71}, abstract = {

Users that populate ratings databases, follow different marking practices, in the sense that some are stricter, while others are more lenient. Similarly, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some users may be entering ratings close to their mean, while other users may be entering more extreme ratings, close to the limits of the rating scale. While this aspect has been recently addressed through the computation and exploitation of an overall rating variability measure per user, the fact that user rating practices may vary along the user{\textquoteright}s rating history time axis may render the use of the overall rating variability measure inappropriate for performing the rating prediction adjustment. In this work, we (1) propose an algorithm that considers two variability metrics per user, the global (overall) and the local one, with the latter representing the user{\textquoteright}s variability at prediction time, (2) present alternative methods for computing a user{\textquoteright}s local variability and (3) evaluate the performance of the proposed algorithm in terms of rating prediction quality and compare it against the state-of-the-art algorithm that employs a single variability metric in the rating prediction computation process.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, prediction accuracy, users{\textquoteright} ratings dynamic variability}, doi = {10.1504/ijbdi.2020.107373}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-IJBDI-2019-a, title = {Improving collaborative filtering{\textquoteright}s rating prediction coverage in sparse datasets by exploiting the {\textquotedblleft}friend of a friend{\textquotedblright} concept}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {47}, abstract = {

Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbours{\textquotedblright} and recommendations for a user are based on her near neighbours{\textquoteright} ratings. However, for a number of users no near neighbours can be found, a problem termed as the {\textquotedblleft}gray sheep{\textquotedblright} problem. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time improving rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, friend-of-a-friend, Pearson Correlation Coefficient, prediction accuracy, Recommender Systems, Sparse Datasets}, issn = {2053-1389}, doi = {10.1504/ijbdi.2020.106178}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisIISA_CIRPaC, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbors{\textquotedblright} and recommendations for a user are based on his near neighbors{\textquoteright} ratings. In order to measure similarity between users, so as to determine a user{\textquoteright}s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users{\textquoteright} or items{\textquoteright} ratings history. However users{\textquoteright} ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced {\textendash}to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we (1) propose an algorithm that considers the common item rating past in the rating prediction computation process, aiming to improve rating prediction quality, and (2) evaluate the proposed algorithm against seven widely used datasets, both dense and sparse, and considering two widely used user similarity metrics.}, keywords = {collaborative filtering, Common Item Rating Past Criterion, Cosine Similarity, Evaluation, Items{\textquoteright} Rating Sequence, Pearson Correlation Coeffi-cient}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {margaris_iisa2019_VNNs, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by ex-ploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formu-late a recommendation for a user, her near neighbors{\textquoteright} ratings are extracted and form the basis for the recommendation. Col-laborative filtering algorithms however exhibit the problem commonly referred to as {\textquotedblleft}gray sheep{\textquotedblright}: this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The {\textquotedblleft}gray sheep{\textquotedblright} problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the {\textquotedblleft}gray sheep{\textquotedblright} problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and consid-ering two correlation metrics which are widely used in Collabo-rative Filtering research, namely the Pearson Correlation Coef-ficient and the Cosine Similarity. The results show that the pro-posed algorithm considerably leverages the capability of a Col-laborative Filtering system to compute personalized recommen-dations in the context of sparse datasets, tackling thus efficiently the {\textquotedblleft}gray sheep{\textquotedblright} problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Sparse Datasets, Virtual Near Neighbors}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {716, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as {\textquotedblleft}near neighbors" and recommendations for a user, are based on his near neighbors{\textquoteright} ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as {\textquotedblleft}Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, item adoption eagerness, Pearson Correlation Coefficient, rating prediction quality}, isbn = {978-1-4503-6934-3}, doi = {10.1145/3350546.3352544}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {DBLP:journals/dke/AllamSK18, title = {Improved suffix blocking for record linkage and entity resolution}, journal = {Data Knowl. Eng.}, volume = {117}, year = {2018}, pages = {98{\textendash}113}, doi = {10.1016/j.datak.2018.07.005}, url = {https://doi.org/10.1016/j.datak.2018.07.005}, author = {Amin Allam and Spiros Skiadopoulos and Panos Kalnis} } @conference {margarisDatacom2018a, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Considering Users{\textquoteright} Rating Variability}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, publisher = {IEEE}, organization = {IEEE}, abstract = {When rating predictions are computed in user-user collaborative filtering, each individual rating is typically adjusted by the mean of the ratings entered by the specific user. This practice takes into account the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some user may be entering ratings close to her mean, while another user may be entering more extreme ratings, close to the limits of the rating scale. In this work, we (1) propose an algorithm that considers users{\textquoteright} ratings variability in the rating prediction computation process, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets considering three widely used variability measures and two user similarity metrics. The proposed algorithm, using the {\textquotedblleft}mean absolute deviation around the mean{\textquotedblright} variability measure, has been found to intro-duce considerable gains in rating prediction accuracy, in every dataset and under both user similarity metrics tested.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Users{\textquoteright} Ratings Variability}, doi = {10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145}, url = {https://ieeexplore.ieee.org/document/8512012}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisDatacom2018b, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets by Exploiting User Dissimilarity}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, month = {aug}, publisher = {IEEE}, organization = {IEEE}, abstract = {Collaborative filtering systems analyze the ratings databases to identify users with similar likings and preferences, termed as near neighbors, and then generate rating predictions for a user by examining the ratings of his near neighbors for items that the user has not yet rated; based on rating predictions, recommenda-tions are then formulated. However, these systems are known to exhibit the {\textquotedblleft}gray sheep{\textquotedblright} problem, i.e. the situation where no near neighbors can be identified for a number of users, and hence no recommendation can be formulated for them. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose a method for alleviating this problem by exploiting user dissimilarity, under the assumption that if some users have exhibited opposing preferences in the past, they are likely to do so in the future. The proposed method has been eval-uated against seven widely used datasets and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time maintaining or slightly improving rating prediction quality.}, keywords = {collaborative filtering, Evaluation, Negative Correlation, Pearson Correlation Coefficient, Sparse Datasets, User-User Correlation}, doi = {10.1109/dasc/picom/datacom/cyberscitec.2018.00150}, url = {https://ieeexplore.ieee.org/document/8512017}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-cbi17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Considering Shifts in Rating Practices}, booktitle = {Proceedings of the 19th IEEE International Conference on business informatics (CBI17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {Users that populate ratings databases, such as IMDB, follow different marking practices, in the sense that some are stricter, while others are more lenient. This aspect has been captured by the most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, which adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities. However, relying on the mean value presumes that the users{\textquoteright} marking practices remain constant over time; in practice though, it is possible that a user{\textquoteright}s marking practices change over time, i.e. a user could start as strict and subsequently become lenient, or vice versa. In this work, we propose an approach to take into account marking practices shifts by (1) introducing the concept of dynamic user rating averages which follow the users{\textquoteright} marking practices shifts, (2) presenting two alternative algorithms for computing a user{\textquoteright}s dynamic averages and (3) performing a comparative evaluation among these two algorithms and the classic static average (unique mean value) that the Pearson Correlation uses.}, doi = {10.1109/CBI.2017.24}, url = {https://ieeexplore.ieee.org/abstract/document/8010718/}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-iscc17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality in Dense Datasets, by Pruning Old Ratings}, booktitle = {Proceedings of the 22nd IEEE Symposium on Computers and Communications (ISCC17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {In this paper, we introduce a pruning algorithm which removes aged user ratings from the rating database used by collaborative filtering algorithms, in order to (1) improve prediction quality and (2) minimize the rating database size, as well as the rating prediction generation time. The proposed algorithm needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values) and can be used with all rating databases that include a timestamp. Furthermore, we propose and validate a method for identifying the most prominent combination of a pruning algorithm and a pruning level for datasets, allowing thus to perform the selection of pruning algorithm and pruning level in an unsupervised fashion.}, doi = {10.1109/ISCC.2017.8024683}, url = {https://ieeexplore.ieee.org/document/8024683/}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {DBLP:conf/gis/Chatzigeorgakidis17, title = {Indexing Geolocated Time Series Data}, booktitle = {Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017, Redondo Beach, CA, USA, November 7-10, 2017}, year = {2017}, pages = {19:1{\textendash}19:10}, doi = {10.1145/3139958.3140003}, url = {https://doi.org/10.1145/3139958.3140003}, author = {Georgios Chatzigeorgakidis and Dimitrios Skoutas and Kostas Patroumpas and Spiros Athanasiou and Spiros Skiadopoulos} } @conference {margaris2016a, title = {Improving QoS Delivered by WS-BPEL Scenario Adaptation through Service Execution Parallelization}, booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing}, year = {2016}, pages = {1590-1596}, publisher = {ACM}, organization = {ACM}, abstract = {WS-BPEL scenario execution adaptation has been proposed by researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we study the potential to parallelize the execution of the WS-BPEL scenario in order to minimize its response time and/or achieving higher scores in the other qualitative dimensions, such as cost, reliability, etc., at the same time. We also describe, develop and validate a parallelization algorithm for realizing the proposed enhancements.}, doi = {10.1145/2851613.2851805}, url = {https://dl.acm.org/citation.cfm?id=2851805}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {vas-euromed16, title = {Interconnecting Objects, Visitors, Sites and (Hi)Stories Across Cultural and Historical Concepts: The CrossCult Project}, booktitle = {Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection}, year = {2016}, month = {10}, pages = {501{\textendash}510}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {Human History, is a huge mesh of interrelated facts and concepts, spanning beyond borders, encompassing global aspects and finally constituting a shared, global experience. This is especially the case regarding European history, which is highly interconnected by nature; however, most History-related experiences that are today offered to the greater public, from schools to museums, are siloed. The CrossCult project aims to provide the means for offering citizens and cultural venue visitors a more holistic view of history, in the light of cross-border interconnections among pieces of cultural heritage, other citizens viewpoints and physical venues. To this end, the CrossCult project will built a comprehensive knowledge base encompassing information and semantic relationships across cultural information elements, and will provide the technological means for delivering the contents of this knowledge base to citizens and venue visitors in a highly personalized manner, creating narratives for the interactive experiences that maximise situational curiosity and serendipitous learning. The CrossCult platform will also exploit the cognitive/emotional profiles of the participants as well as temporal, spatial and miscellaneous features of context, including holidays and anniversaries, social media trending topics and so forth.}, doi = {10.1007/978-3-319-48496-9_39}, url = {http://link.springer.com/chapter/10.1007/978-3-319-48496-9_39}, author = {Costas Vassilakis and Angela Antoniou and Giorgos Lepouras and Manolis Wallace and Ioanna Lykourentzou and Y. Naudet} } @article {margaris14a, title = {An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques}, journal = {Science of Computer Programming}, volume = {98}, year = {2015}, pages = {707{\textendash}734}, abstract = {In this paper, we present a framework which incorporates runtime adaptation for BPEL scenarios. The adaptation is based on (a) the quality of service parameters of available services, allowing for tailoring their execution to the diverse needs of individual users and (b)collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes. The proposed framework also caters maintaining the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1016/j.scico.2014.10.007}, url = {http://www.sciencedirect.com/science/article/pii/S0167642314004778}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {masie2013, title = {Improving museum visitors{\textquoteright} Quality of Experience through intelligent recommendations: A visiting style-based approach}, booktitle = {Proceedings of MASIE 2013 Workshop, co-located with the 9th International Conference on Intelligent Environments IE{\textquoteright}13}, year = {2013}, month = {7}, address = {Athens, GR}, abstract = {This paper investigates the effect that smart routing and recommendations can have on improving the Quality of Experience of museum visitors. The novelty of our approach consists of taking into account not only user interests but also their visiting styles, as well as modeling the museum not as a sterile space but as a location where crowds meet and interact, impacting each visitor?s Quality of Experience. The investigation is done by an empirical study on data gathered by a custom-made simulator tailored for the museum user routing problem. Results are promising and future potential and directions are discussed.}, doi = {10.3233/978-1-61499-286-8-507}, author = {Ioanna Lykourentzou and Claude, X. and Y. Naudet and Tobias, E. and Angela Antoniou and Giorgos Lepouras and Costas Vassilakis} } @conference {Margaris2013SAC, title = {An integrated framework for QoS-based adaptation and exception resolution in WS-BPEL scenarios}, booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC \textquotesingle13}, year = {2013}, month = {3}, publisher = {ACM Press}, organization = {ACM Press}, abstract = {In this paper, we present a framework which incorporates runtime quality of service-based adaptation for BPEL scenarios, allowing for tailoring their execution to the diverse needs of individual users. The proposed framework also caters for automatically resolving system-level exceptions, such as machine outages or network partitionings, while both scenario execution adaptation and exception resolution maintain the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1145/2480362.2480714}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {vasiliadis2010a, title = {Improving performance of a wireless multimedia traffic-oriented network through prediction of routing}, journal = {International Journal on Information Technologies \& Security}, volume = {3}, year = {2010}, pages = {3-16}, abstract = {Wireless Local Area Networks (WLANs) have developed into a viable technology to support multimedia traffic and are expected to support multimedia services with guaranteed Quality of Service (QoS) for diverse traffic types (video, audio, and data). In this paper, we consider the incorporation of prediction into a generic distance-vector routing protocol for WLANs, evaluate the performance of the resulting routing scheme. Our study considers the enhancement of Distributed Bellman-Ford algorithm, which is a widely used algorithm, and assesses the effectiveness of the enhanced version on top of a fourth generation system (4-G). In order to compare the performance of the standard protocol against that of the prediction-enhanced version, we gather metrics for the two most important network performance factors, namely packet throughput and delay under different mobility and traffic conditions, using the simulation environment of NS-2. Both medium- and high-mobility configurations have been considered in this study.}, author = {D.C. Vasiliadis and G.E. Rizos and Costas Vassilakis} } @article {583, title = {Information Filtering and Query Indexing for an Information Retrieval Model}, journal = {ACM Transactions on Information Systems (TOIS)}, volume = {27}, year = {2009}, pages = {1{\textendash}47}, author = {Christos Tryfonopoulos and Manolis Koubarakis and Yannis Drougas} } @article {Kareliotis2009284, title = {IQoS-aware exception resolution for BPEL processes: A middleware-based framework and performance evaluation}, journal = {International Journal of Web and Grid Services}, volume = {5}, number = {3}, year = {2009}, note = {cited By (since 1996) 0}, pages = {284-320}, abstract = {Web Services Business Process Execution Language (WS-BPEL) is widely used nowadays for specifying and executing composite business processes. During the execution, however, of such business processes, a number of faults stemming from the nature of SOA may occur. The WS-BPEL scenario designer must therefore use the provisions offered by WS-BPEL to catch these exceptions and resolve them. The system fault handler specification, however, is an additional task for the WS scenario designer, while the presence of such handlers within the scenario necessitates extra maintenance activities. In this paper, we propose a middleware-based framework for system exception resolution, which undertakes the tasks of failure interception, discovery of alternative services and their invocation. The process of selecting the alternative services can be driven by process consumer-specified Quality of Service (QoS) policy. Moreover, the middleware arranges for bridging syntactic differences between the originally invoked services and functionally equivalent replacements to it, by employing XSLT-based transformations. The middleware is deployed and maintained independently of the WS-BPEL scenarios, thus removing the need for specifying and maintaining system fault handlers within the scenarios. We also present performance measures, establishing that the overhead imposed by the addition of the proposed middleware layer is minimal. Copyright Β{\textcopyright} 2009, Inderscience Publishers.}, keywords = {Ad hoc networks, BPEL process, Exception handling, Exception resolution, Middleware, Performance metrics, QoS, Quality control, Quality of service, Scalability, Telecommunication services, Web services}, doi = {10.1504/IJWGS.2009.028346}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70349462474\&partnerID=40\&md5=4511b42fde076617cd6ac0139e3f588d}, author = {Christos Kareliotis and Costas Vassilakis and S. Rouvas and P. Georgiadis} } @conference {595, title = {iCluster: a Self-Organizing Overlay Network for P2P Information Retrieval}, booktitle = {Proceedings of 30th European Conference on Information Retrieval (ECIR)}, year = {2008}, month = {March}, address = {Glasgow, Scotland}, abstract = {We present iCluster, a self-organizing peer-to-peer overlay network for supporting full-fledged information retrieval in a dynamic environment. iCluster works by organizing peers sharing common interests into clusters and by exploiting clustering information at query time for achieving low network traffic and high recall. We define the criteria for peer similarity and peer selection, and we present the protocols for organizing the peers into clusters and for searching within the clustered organization of peers. iCluster is evaluated on a realistic peer-to-peer environment using real-world data and queries. The results demonstrate significant performance improvements (in terms of clustering efficiency, communication load and retrieval accuracy) over a state-of-the-art peer- to-peer clustering method. Compared to exhaustive search by flooding, iCluster exchanged a small loss in retrieval accuracy for much less message flow.}, author = {Paraskevi Raftopoulou and E.G.M. Petrakis} } @conference {Vasiliadis200823, title = {Improving performance of finite-buffered blocking delta networks with 2-class priority routing through asymmetric-sized buffer queues}, booktitle = {Proceedings - 4th Advanced International Conference on Telecommunications, AICT 2008}, year = {2008}, note = {cited By (since 1996) 1}, month = {6}, pages = {23-29}, abstract = {In this paper the performance of asymmetric-sized finite-buffered Delta Networks with 2-class routing traffic is presented and analyzed in the uniform traffic conditions under various loads using simulations. We compare the performance of 2-class priority mechanism against the single priority one, by gathering metrics for the two most important network performance factors, namely packet throughput and delay. We also introduce and calculate a universal performance factor, which includes the importance aspect of each of the above main performance factors. We found that the use of asymmetric-sized buffered systems leads to better exploitation of network capacity, while the increments in delays can be tolerated. The goal of this paper is to help network designers in performance prediction before actual network implementation and in understanding the impact of each parameter factor. Β{\textcopyright} 2008 IEEE.}, keywords = {Computer networks, Delta networks, International conferences, Performance factors}, doi = {10.1109/AICT.2008.13}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50949090335\&partnerID=40\&md5=1a38fdae7e54da27ace165ed90b5a386}, author = {D.C. Vasiliadis and G.E. Rizos and Costas Vassilakis} } @conference {549, title = {Information Retrieval and Filtering over Self-Organising Digital Libraries}, booktitle = {Proceedings of the 12th European Conference on Research and Advanced Technology for Digital Libraries (ECDL)}, year = {2008}, month = {September}, address = {Aarhus, Denmark}, author = {Paraskevi Raftopoulou and E.G.M. Petrakis and Christos Tryfonopoulos and Gerhard Weikum} } @article {vas2008d, title = {An Integrated Environment for Cataloguing and Online Presentation of Museum Exhibits}, journal = {DESIDOC Journal of Library and Information Technology}, volume = {28(4)}, year = {2008}, pages = {72-78}, abstract = {The paper presents an integrated environment which enables museum personnel to catalogue and at the same time publish online museum exhibits. The system is based on international standards and is highly customisable to cater the needs of a variety of museum types. Moreover, the underlying database allows storing for the same exhibit documentations for different audiences and in multiple languages, while it is extendable to accommodate new media types, languages, exhibits and information categories. The administrative part of the environment permits the restriction of certain functions to specific personnel roles, enforcing thus a general museum security policy regarding access to and modification of information. The environment presented is currently in use at the Athens University History Museum.}, author = {Costas Vassilakis and Akrivi Katifori and I. Daradimos and Giorgos Lepouras} } @inbook {gol2009a, title = {Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies}, year = {2008}, pages = {188{\textendash}204}, publisher = {IGI Publishing}, organization = {IGI Publishing}, chapter = {Context and Adaptivity-Driven Visualization Method Selection}, abstract = {Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and advanced functionalities. Besides, research in the field of user modeling is progressing in order to personalize these visualization systems, according to its users{\textquoteright} individual profiles. However, employing a single visualization system, may not suit best any information seeking activity. In this paper we present a visualization environment, which is based on a visualization library, i.e. is a set of visualization methods, from which the most appropriate one is selected for presenting information to the user. This selection is performed combining information extracted from the context of the user, the system configuration and the data collection. A set of rules inputs such information and assigns a score to all candidate visualization methods. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria.}, author = {M. Golemati and Costas Vassilakis and Akrivi Katifori and Giorgos Lepouras and C. Halatsis}, editor = {C. Mourlas and P. Germanakos} } @conference {Golemati2008106, title = {An interview-based user study on the use of visualizations for folder browsing}, booktitle = {Proceedings of the International Conference on Information Visualisation}, year = {2008}, note = {cited By (since 1996) 0}, month = {7}, pages = {106-112}, abstract = {Hierarchically structured data collections often need to be visualized for the purposes of digital information management and presentation. File browsing, in particular, has an inherent hierarchical structure and plays an important role in the context of Personal Information Management (PIM). A multitude of file browsers are nowadays available, offering different functionalities, while users adopt diverse practices and habits for browsing activities. In this paper, we investigate these aspects to obtain insights into their advantages and disadvantages and suggest solutions in the area of PIM, as well as in other domains employing similar visualization paradigms. The presented study focuses on the two most widespread visualizations used by file browsers, the indented list and zoomable interface paradigms, and assesses their effectiveness for various tasks and contexts, both by exploiting results on existing evaluations on hierarchy visualizations and folder hierarchy visualizations in particular, and by conducting an interviewbased user study. Β{\textcopyright} 2008 IEEE.}, keywords = {Administrative data processing, File browser visualization, Indented list visualization, Information analysis, Information management, Information science, Intermodulation, Intermodulation measurement, International conferences, Management, Management information systems, Telecommunication services, User study, Windows explorer}, doi = {10.1109/IV.2008.90}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-51749097531\&partnerID=40\&md5=66afc30e74191773d1c950ac83ba9ac7}, author = {M. Golemati and Akrivi Katifori and E.G. Giannopoulou and I. Daradimos and Costas Vassilakis and Giorgos Lepouras and C. Halatsis} } @conference {568, title = {Implementing Publish/Subscribe Systems with Languages from Information Retrieval on Top of Structured Overlay Networks}, booktitle = {Proceedings of the International Workshop on Peer-to-Peer and IR (P2P\&IR @ SIGIR)}, year = {2004}, month = {July}, address = {Sheffield, UK}, author = {Christos Tryfonopoulos and Manolis Koubarakis} } @article {DBLP:journals/eg/VassilakisLRG04, title = {Integrating e-government public transactional services into public authority workflows}, journal = {Electronic Government}, volume = {1}, number = {1}, year = {2004}, pages = {49-60}, abstract = {Documents submitted by citizens through electronic services deployed in the context of e-Government must usually undergo processing by some organisational information system, in order to complete the citizens? requests and for the reply to be returned to the citizen. The integration, however, of the e-service delivery platform and the organisational information system is often hindered for a number of reasons, including security considerations, platform diversity or idiosyncrasies of legacy information systems. In this paper we present a generic method for providing seamless communication between the two platforms, enabling the full integration of documents submitted through electronic services into the organisational workflow, leveraging thus the quality of services offered to the citizens and facilitating e-service development and operation.}, doi = {10.1504/EG.2004.004136}, author = {Costas Vassilakis and Giorgos Lepouras and S. Rouvas and P. Georgiadis} } @conference {DBLP:conf/caise/VassilakisLRG03, title = {Improving e-Form Layout Through Analysis of Form Semantics and Validation Checks}, booktitle = {CAiSE Short Paper Proceedings}, year = {2003}, month = {6}, abstract = {Transaction services offered by public authorities vary from simple forms with few fields to multi-form compound documents with hundreds of input areas. In the latter case, field placement within forms is of particular importance for facilitating the filling and error correction processes. In this paper we present an approach to improving the form layout by exploiting validation checks that are usually associated with electronic forms, as well as semantic information that may be attached to form fields by designers.}, url = {http://ceur-ws.org/Vol-74/files/FORUM_38.pdf}, author = {Costas Vassilakis and Giorgos Lepouras and S. Rouvas and P. Georgiadis} } @conference {546, title = {Information Alert in Distributed Digital Libraries: The Models, Languages, and Architecture of DIAS}, booktitle = {Proceedings of the 6th European Conference on Research and Advanced Technology for Digital Libraries (ECDL)}, year = {2002}, month = {September}, address = {Rome, Italy}, author = {Manolis Koubarakis and Theodoros Koutris and Christos Tryfonopoulos and Paraskevi Raftopoulou} } @article {Vassilakis1998335, title = {Implementation of transaction and concurrency control support in a temporal DBMS}, journal = {Information Systems}, volume = {23}, number = {5}, year = {1998}, note = {cited By (since 1996) 2}, pages = {335{\textendash}350}, abstract = {Transactions and concurrency control are significant features in database systems, facilitating functions both at user and system level. However, the support of these features in a temporal DBMS has not yet received adequate research attention. In this paper, we describe the techniques developed in order to support transaction and concurrency control in a temporal DBMS that was implemented as an additional layer to a commercial DBMS. The proposed techniques make direct use of the transaction mechanisms of the DBMS. In addition, they overcome a number of limitations such as automatic commit points, lock release and log size increment, which are imposed by the underlying DBMS. Our measurements have shown that the overhead introduced by these techniques is negligible, less than 1\% in all cases. The approach undertaken is of general interest, it can also be applied to non-temporal DBMS extensions. Copyright Β{\textcopyright} 1998 Elsevier Science Ltd. All rights reserved.}, keywords = {Concurrency control, Data structures, Database systems, Temporal databases}, doi = {10.1016/S0306-4379(98)00015-5}, url = {https://doi.org/10.1016/S0306-4379(98)00015-5}, author = {Costas Vassilakis and N. Lorentzos and P. Georgiadis} } @conference {754888, title = {Implementing Embedded Valid Time Query Languages}, booktitle = {DEXA {\textquoteright}98: Proceedings of the 9th International Conference on Database and Expert Systems Applications}, year = {1998}, month = {8}, pages = {561{\textendash}572}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {London, UK}, abstract = {Application development on top of database systems is heavily based on the existence of embedded and 4GL languages. However, the issue of designing and implementing embedded or 4GL temporal languages has not been addressed insofar. In this paper, we present a design approach for implementing an embedded temporal language that supports valid time. Furthermore, we introduce implementation techniques that can be used for implementing any embedded temporal language that supports valid time on top of a DBMS.}, isbn = {3-540-64950-6}, doi = {10.1007/BFb0054514}, author = {Costas Vassilakis and P. Georgiadis and Sellis, Timos K.} }