@article {835,
	title = {On the Quality and Validity of Course Evaluation Questionnaires Used in Tertiary Education in Greece},
	journal = {Trends in Higher Education},
	volume = {3},
	year = {2024},
	pages = {221{\textendash}234},
	abstract = {<p>In compliance with national legislation, Greek tertiary education institutions assess educational quality often using a standardized anonymous questionnaire completed by students. This questionnaire aims to independently evaluate various course components, including content organization, instructor quality, facilities, infrastructure, and grading methods. Despite widespread use across universities, the questionnaire{\textquoteright}s validity remains unexamined. This study addresses this gap by analyzing 48,000+ questionnaire responses from the University of the Peloponnese (2014{\textendash}2022), encompassing 68 undergraduate and graduate programs. Confirmatory factor analyses were used to assess the quality of the questionnaire, while exploratory factor analyses were used to assess the dimensions of the tool based on the data. Both analyses reveal shortcomings: confirmatory analysis detects strong correlations between supposedly different factors, and exploratory analysis identifies dimensions inconsistent with the expected structure. These findings question the questionnaire{\textquoteright}s quality and the validity of drawn conclusions, while additionally identifying opportunities for reducing the number of questions, which can contribute to increased questionnaire submission rates. Given its common use across Greek universities and its influence on shaping courses, urgent redesigning of the questionnaire for tertiary education evaluation is recommended.</p>
},
	keywords = {CFA, EFA, Higher Education, quality assurance},
	doi = {https://doi.org/10.3390/higheredu3020013},
	url = {https://www.mdpi.com/2813-4346/3/2/13},
	author = {Papadogiannis, Ilias and Vassilakis, Costas and Wallace, Manolis and Katsis, Athanassios}
}
@conference {588,
	title = {The quest for the appropriate cyber-threat intelligence sharing platform},
	booktitle = {Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA), Prague, Czech Republic},
	year = {2019},
	month = { 07/2009},
	abstract = {Cyber-threat intelligence (CTI) is any information that can help an organization identify, assess, monitor, and respond to cyber-threats. It relates to all cyber components of an organization such as networks, computers, and other types of information technology. In the recent years, due to the major increase of cyber-threats, CTI sharing is becoming increasingly important both as a subject of research and as a concept of providing additional security to organizations. However, selecting the proper tools and platforms for CTI sharing, is a challenging task, that pertains to a variety of aspects. In this paper, we start by overviewing the CTI procedure (threat types, categories, sources and the general CTI life-cycle). Then, we present a set of seven high-level CTI platform recommendations that can be used to evaluate a platform and subsequently we survey six state-of- the-art cyber-threat intelligence platforms. Finally, we compare and evaluate the six aforementioned platforms by means of the earlier proposed recommendations.},
	keywords = {Cyber-threat, Intelligence, Sharing},
	url = {http://users.uop.gr/~trifon/papers/pdf/data19-CKSKTBK.pdf},
	author = {Thanasis Chantzios and Paris Koloveas and Spiros Skiadopoulos and Nikos Kolokotronis and Christos Tryfonopoulos and Vasiliki-Georgia Bilali and Dimitris Kavallieros}
}
@article {marg2017a-fgcs,
	title = {Query personalization using social network information and collaborative filtering techniques},
	journal = {Future Generation of Computer Systems},
	volume = {Special Issue on Recommender Systems for Large-Scale Social Networks},
	year = {2017},
	month = {jan},
	pages = {440{\textendash}450},
	abstract = {Query personalization has emerged as a means to handle the issue of information volume growth, aiming to tailor query answer results to match the goals and interests of each user. Query personalization dynamically enhances queries, based on information regarding user preferences or other contextual information; typically enhancements relate to incorporation of conditions that filter out results that are deemed of low value to the user and/or ordering results so that data of high value are presented first. In the domain of personalization, social network information can prove valuable; users{\textquoteright} social networks profiles, including their interests, influence from social friends, etc. can be exploited to personalize queries. In this paper, we present a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.},
	doi = {10.1016/j.future.2017.03.015},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X17303692},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}
}
@article {581,
	title = {Query Reorganisation Algorithms for Efficient Boolean Information Filtering},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	volume = {29},
	year = {2017},
	pages = {418{\textendash}432},
	abstract = {In the information filtering paradigm, clients subscribe to a server with continuous queries that express their information needs and get notified every time appropriate information is published. To perform this task in an efficient way, servers employ indexing schemes that support fast matches of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, state-of-the-art indexing schemes are sensitive to the query insertion order and cannot adopt to an evolving query workload, degrading the filtering performance over time. In this paper, we present an adaptive trie-based algorithm that outperforms current methods by relying on query statistics to reorganise the query database. Contrary to previous approaches, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves more than 96\% filtering time improvement over its state-of-the-art competitors. Finally, we demonstrate that our solution is easily extensible to multi-core machines.},
	keywords = {dissemination, indexing methods, Information filtering, user profiles and alert services},
	doi = {10.1109/TKDE.2016.2620140},
	url = {https://doi.org/10.1109/TKDE.2016.2620140},
	author = {Lefteris Zervakis and Christos Tryfonopoulos and Spiros Skiadopoulos and Manolis Koubarakis}
}
@conference {Christos2009271,
	title = {QoS-driven adaptation of BPEL scenario execution},
	booktitle = {2009 IEEE International Conference on Web Services, ICWS 2009},
	year = {2009},
	note = {cited By (since 1996) 0},
	month = {7},
	pages = {271-278},
	abstract = {BPEL/WSBPEL is the predominant approach for combining individual web services into integrated business processes, allowing for the specification of their sequence, control flow and data exchanges. BPEL however does not include mechanisms for considering the invoked services{\textquoteright} Quality of Service (QoS) parameters and thus BPEL scenarios can neither tailor their execution to the individual user{\textquoteright}s needs or adapt to the highly dynamic environment of the WEB, where new services may be deployed, old ones withdrawn or existing ones changing their QoS parameters. Moreover, infrastructure failures in the distributed environment of the web introduce an additional source of failures that must be considered in the context of QoS-aware service execution. In this work we propose a framework for addressing the issues identified above; the framework allows the users to specify the QoS parameters that they require and it undertakes the task of locating and invoking suitable services. Finally, the proposed framework intercepts and resolves faults occurring during service invocation, respecting the QoS restrictions specified by the consumer. Β{\textcopyright} 2009 IEEE.},
	keywords = {Adaptive execution, BPEL, Control flows, Data exchange, Distributed environments, Dynamic environments, Infrastructure failures, Integrated business, New services, Performance evaliation, QoS parameters, Quality of service, Quality of Service parameters, Service execution, Service invocation, Web services},
	doi = {10.1109/ICWS.2009.80},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70449472686\&partnerID=40\&md5=5303ebeff04a4e7a062f77209521d561},
	author = {Christos Kareliotis and Costas Vassilakis and S. Rouvas and P. Georgiadis}
}
@conference {567,
	title = {Query Processing in Super-Peer Networks with Languages Based on Information Retrieval: the P2P-DIET Approach},
	booktitle = {Proceedings of the International Workshop on Peer-to-peer Computing and Databases (P2P\&DB @ EDBT)},
	year = {2004},
	month = {March},
	address = {Herakion, Greece},
	author = {Stratos Idreos and Christos Tryfonopoulos and Manolis Koubarakis and Yannis Drougas}
}
@article {DBLP:journals/ai/KoubarakisS00,
	title = {Querying temporal and spatial constraint networks in PTIME},
	journal = {Artif. Intell.},
	volume = {123},
	number = {1-2},
	year = {2000},
	pages = {223{\textendash}263},
	doi = {10.1016/S0004-3702(00)00055-2},
	url = {https://doi.org/10.1016/S0004-3702(00)00055-2},
	author = {Manolis Koubarakis and Spiros Skiadopoulos}
}
@conference {DBLP:conf/aaai/KoubarakisS99,
	title = {Querying Temporal Constraint Networks in PTIME},
	booktitle = {Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, July 18-22, 1999, Orlando, Florida, USA.},
	year = {1999},
	pages = {745{\textendash}750},
	url = {http://www.aaai.org/Library/AAAI/1999/aaai99-106.php},
	author = {Manolis Koubarakis and Spiros Skiadopoulos}
}
