ICDSST 2022 Keynote Speakers:
Professor Evangelos Grigoroudis, Technical University of Crete, Greece: “Multiple Criteria Decision Analysis and Online Ratings: Theoretical Models and Applications”
Online ratings and reviews are currently considered as extremely useful tools for both consumers and business organizations. Considering the current technological progress, they are becoming increasingly available, widespread, and influential. Consumers generate and rely on these ratings and reviews for their purchasing decisions, while business organizations are developing systems to collect and analyze this online information. Regardless of the context of analysis, online ratings and reviews are widely considered as effective management-marketing and strategy tools for businesses. Current research reveals that they affect, like no advertisement does, consumers’ purchasing decisions. Given the structure and nature of rating and review data, multiple criteria decision analysis (MCDA) has been widely used to analyze them. The majority of existing MCDA tools adopts a regression-based approach. In this context, the main principles of ordinal regression are presented, focusing on its ability to analyze ordinal data and explain consumer behavior through a set of quantitative indices and perceptual maps. Building on existing large-scale datasets, online tourist ratings are analyzed, in order to demonstrate the applicability and the benefits of ordinal regression analysis. The presented application focuses on identifying the attributes that influence customers’ satisfaction, as well as the strengths and weaknesses of the provided tourism services.
Evangelos Grigoroudis is Professor on management of quality processes in the School of Production Engineering and Management of the Technical University of Crete, Greece (2002-). He followed postgraduate studies in Technical University of Crete, Greece from where he received his Ph.D. degree in 1999. He has received distinctions from the Hellenic Operational Research Society, the Academy of Business and Administrative Sciences, the World Automation Congress, the Foundation of Ioannis and Vasileia Karayianni, the Technical University of Crete, and the State Scholarships Foundation of Greece. He acts as reviewer for more than 70 scientific journals, and he is associate editor of the Operational Research: An International Journal, the Journal of Knowledge Management, the Journal of the Knowledge Economy (senior associate editor), the Journal of Innovation and Entrepreneurship (senior associate editor), the International Journal of Decision Support Systems, the International Journal of Social Ecology and Sustainable Development, the International Journal of Food and Beverage Manufacturing and Business Models, and the Palgrave Communications and member of the Editorial Board of the scientific journals: International Journal of Information and Decision Sciences, International Journal of Information Systems in the Service Sector, International Journal of Multicriteria Decision Making, Journal of Marketing and Operation Management Research και World Journal of Applied Agricultural Sciences and Engineering. He coauthored/coedited more than 15 books in service quality measurement, corporate strategy and published more than 170 articles in scientific journal, books and conference proceedings. His research interests include service quality measurement processes, customer and employee satisfaction, performance evaluation, business excellence, operational research (evaluation methodologies and techniques), multicriteria decision analysis, data analysis (qualitative data analysis methods), and marketing (market and customer satisfaction surveys).
Professor Zoran Obradovic, Statistics Department, Temple University, USA: “Disruptive Even Detection and Categorization from Streaming Data with Scarce and Imprecise Labels”
Accurate predictions at multiple temporal and spatial scales from many sensors can potentially enable innovations across various industries. For example, moving from corrective to predictive maintenance of complex infrastructure based on knowledge extracted by many instruments could be more cost effective since this can facilitate early and interpretable risk predictions with uncertainty estimates and allow optimization of damage mitigation and prevention strategies. Similarly, in proactive emergency monitoring, a network of sensors could estimate operating conditions before they occur, which can direct deployment of control measures for avoiding undesirable outcomes. In this talk an overview of our recently developed methods to facilitate such end-to-end solutions will be discussed within the context of our ongoing DOE funded project aimed at predictive analytics in a large electricity grid from multiple phasor measurement units. Challenges and the proposed solutions will be discussed related to (1) deep-learning based detection and classification of local and system-wide events using rapidly refined, partially inspected event labels; (2) digital-twin based data enhancement for events insufficiently represented in field-recordings over the training period; and (3) transfer learning to leverage relevant labeled events from a different network to minimize additional labeling effort.
Zoran Obradovic is a Distinguished Professor and a Center director at Temple University, an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He mentored 45 postdoctoral fellows and Ph.D. students, many of whom have independent research careers at academic institutions (e.g. Northeastern Univ., Ohio State Univ.) and industrial research labs (e.g. Amazon, Facebook, Hitachi Big Data, IBM T.J.Watson, Microsoft, Yahoo Labs, Uber, Verizon Big Data, Spotify). Zoran is the editor-in-chief at the Big Data journal and the steering committee chair for the SIAM Data Mining conference. He is also an editorial board member at 13 journals and was the general chair, program chair, or track chair for 11 international conferences. His research interests include data science and complex networks in decision support systems addressing challenges related to big, heterogeneous, spatial and temporal data analytics motivated by applications in healthcare management, power systems, earth and social sciences. His studies were funded by AFRL, DARPA, DOE, KAUST, NIH, NSF, ONR, and the PA Department of Health and industry. For more details see http://www.dabi.temple.edu/zoran-obradovic