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1. Blockchain For Mobile Health Applications: Acceleration With GPU Computing

Author

Drakopoulos, Georgios, Marountas, Michail, Liapakis, Xenophon, Tzimas, Giannis, Mylonas, Phivos, and Sioutas, Spyros

Subjects
Computer Science - Cryptography and Security, Computer Science - Information Retrieval, 65Y05, 68Q05, 68Q10, 68W10
Abstract

Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it found a number of applications in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health application by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.

Published
2023
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6. Employing Natural Language Processing Techniques for Online Job Vacancies Classification

Author

Varelas, George, Lagios, Dimitris, Ntouroukis, Spyros, Zervas, Panagiotis, Parsons, Kenia, Tzimas, Giannis, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, and Cortez, Paulo, editor

Published
2022
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7. ACTH vs steroids for the treatment of acute gout in hospitalized patients: a randomized, open label, comparative study

Author

Daoussis, Dimitrios, Kordas, Panagiotis, Varelas, George, Michalaki, Marina, Onoufriou, Anny, Mamali, Irene, Iliopoulos, George, Melissaropoulos, Konstantinos, Ntelis, Konstantinos, Velissaris, Dimitrios, Tzimas, Giannis, Georgiou, Panagiotis, Vamvakopoulou, Sofia, Paliogianni, Fotini, Andonopoulos, Andrew P., and Georgopoulos, Neoklis

Published
2022
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8. Using Hadoop for Large Scale Analysis on Twitter: A Technical Report

Author

Nodarakis, Nikolaos, Sioutas, Spyros, Tsakalidis, Athanasios, and Tzimas, Giannis

Subjects
Computer Science - Databases, Computer Science - Computation and Language, Computer Science - Information Retrieval, H.2.4
Abstract

Sentiment analysis (or opinion mining) on Twitter data has attracted much attention recently. One of the system's key features, is the immediacy in communication with other users in an easy, user-friendly and fast way. Consequently, people tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide diversity of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since none can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample, is not representative to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this paper, we go one step further and develop a novel method for sentiment learning in the MapReduce framework. Our algorithm exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner. Moreover, we utilize Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable and confirm the quality of our sentiment identification., Comment: 8 pages, 3 tables, 3 figures

Published
2016

9. Virus Propagation in Multiple Profile Networks

Author

Rapti, Angeliki, Tsichlas, Kostas, Sioutas, Spiros, and Tzimas, Giannis

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based on their profile? For example, if there are two profiles $\mathcal{A}$ and $\mathcal{B}$ in a network and the nodes of profile $\mathcal{A}$ and profile $\mathcal{B}$ are susceptible to a highly spreading virus with probabilities $\beta_{\mathcal{A}}$ and $\beta_{\mathcal{B}}$ respectively, what percentage of both profiles will actually get infected from the virus at the end? To reverse the question, what are the necessary conditions so that a predefined percentage of the network is infected? We assume that nodes of different profiles can infect one another and we prove that under realistic conditions, apart from the weak profile (great sensitivity), the stronger profile (low sensitivity) will get infected as well. First, we focus on cliques with the goal to provide exact theoretical results as well as to get some intuition as to how a virus affects such a multiple profile network. Then, we move to the theoretical analysis of arbitrary networks. We provide bounds on certain properties of the network based on the probabilities of infection of each node in it when it reaches the steady state. Finally, we provide extensive experimental results that verify our theoretical results and at the same time provide more insight on the problem.

Published
2015

10. Interval Time Series Forecasting: An Innovative Approach Transforming Interval to Single Time Series.

Author

Varelas, George and Tzimas, Giannis

Subjects
*VECTOR autoregression model, *MARKET timing, *INSURANCE companies, *TIME series analysis, *FORECASTING methodology
Abstract

ABSTRACT Interval Time Series are present in everyday life. An example is the opening and closing value of some stock in a market for certain time intervals. The forecasting plays an essential role in many financial organisations. The development of new mathematical tools or improving the existing ones will lead to more accurate forecasting techniques. Interval Arithmetic is a mathematical field that uses intervals by nature and algorithms that use it are involved in the solution of Interval Time Series. Another classical algorithm is VAR models. In this paper, a method that comes from the insurance sector is used to forecast Brent Oil monthly values. The innovation of what we propose is that it converts the Interval Time Series system into a single time series and can propagate the results back to each time series of the system. This way the researcher works with only one time series instead of two (or more). The forecasting algorithm is a choice of the researcher, expediting the development of forecasting (even ARIMA can be applied). We demonstrate our methodology in forecasting the Brent Oil monthly prices by applying the ANFIS algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Rapid AkNN Query Processing for Fast Classification of Multidimensional Data in the Cloud

Author

Nodarakis, Nikolaos, Sioutas, Spyros, Tsoumakos, Dimitrios, Tzimas, Giannis, and Pitoura, Evaggelia

Subjects
Computer Science - Databases, H.2.4
Abstract

A $k$-nearest neighbor ($k$NN) query determines the $k$ nearest points, using distance metrics, from a specific location. An all $k$-nearest neighbor (A$k$NN) query constitutes a variation of a $k$NN query and retrieves the $k$ nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only (i.e. $k$NN joins in databases, classification in data mining). So, it is very crucial to develop methods that answer them efficiently. In this work, we propose a novel method for classifying multidimensional data using an A$k$NN algorithm in the MapReduce framework. Our approach exploits space decomposition techniques for processing the classification procedure in a parallel and distributed manner. To our knowledge, we are the first to study the classification of multidimensional objects under this perspective. Through an extensive experimental evaluation we prove that our solution is efficient and scalable in processing the given queries. We investigate many different perspectives that can affect the total computational cost, such as different dataset distributions, number of dimensions, growth of $k$ value and granularity of space decomposition and prove that our system is efficient, robust and scalable., Comment: 12 pages, 14 figures, 4 tables (it will be submitted to DEXA 2014)

Published
2014

13. Computing Long Sequences of Consecutive Fibonacci Integers with TensorFlow

Author

Drakopoulos, Georgios, Liapakis, Xenophon, Spyrou, Evaggelos, Tzimas, Giannis, Mylonas, Phivos, Sioutas, Spyros, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, MacIntyre, John, editor, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, and Pimenidis, Elias, editor

Published
2019
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14. An Approach for Domain-Specific Design Pattern Identification Based on Domain Ontology

Author

Gkantouna, Vassiliki, Papaioannou, Vaios, Tzimas, Giannis, Sabic, Zlatan, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, MacIntyre, John, editor, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, and Pimenidis, Elias, editor

Published
2019
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15. SART: Speeding up Query Processing in Sensor Networks with an Autonomous Range Tree Structure

Author

Sioutas, Spyros, Panaretos, Alexandros, Karydis, Ioannis, Tsoumakos, Dimitrios, Tzimas, Giannis, and Tsolis, Dimitrios

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Databases, H.2
Abstract

We consider the problem of constructing efficient P2P overlays for sensornets providing "Energy-Level Application and Services". The method presented in \cite{SOPXM09} presents a novel P2P overlay for Energy Level discovery in a sensornet. However, this solution is not dynamic, since requires periodical restructuring. In particular, it is not able to support neither join of sensor\_nodes with energy level out of the ranges supported by the existing p2p overlay nor leave of \emph{empty} overlay\_peers to which no sensor\_nodes are currently associated. On this purpose and based on the efficient P2P method presented in \cite{SPSTMT10}, we design a dynamic P2P overlay for Energy Level discovery in a sensornet, the so-called SART (Sensors' Autonomous Range Tree). The adaptation of the P2P index presented in \cite{SPSTMT10} guarantees the best-known dynamic query performance of the above operation. We experimentally verify this performance, via the D-P2P-Sim simulator (D-P2P-Sim is publicly available at http://code.google.com/p/d-p2p-sim/)., Comment: 11 pages, 23 figures, 5 algorithms or operations

Published
2012

16. From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence.

Author

Tzimas, Giannis, Zotos, Nikos, Mourelatos, Evangelos, Giotopoulos, Konstantinos C., and Zervas, Panagiotis

Subjects
*JOB postings, *COVID-19 pandemic, *JOB descriptions, *DATA mining, *MACHINE learning
Abstract

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Mobile Data Fusion for Maritime Surveillance

Author

Sakkopoulos, Evangelos, Viennas, Emmanouil, Ioannou, Zafeiria-Marina, Gkantouna, Vassiliki, Sourla, Efrosini, Paschou, Mersini, Tsakalidis, Athanasios, Tzimas, Giannis, Sioutas, Spyros, Masys, Anthony J., Editor-in-chief, Bichler, Gisela, Series editor, Bourlai, Thirimachos, Series editor, Johnson, Chris, Series editor, Karampelas, Panagiotis, Series editor, Leuprecht, Christian, Series editor, Morse, Edward C., Series editor, Skillicorn, David, Series editor, and Yamagata, Yoshiki, Series editor

Published
2018
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19. Development and Integration of WWW-Based Services in an Existing University Environment.

Author

Garofalakis, John, Kappos, Panagiotis, Tsakalidis, Athanasios, Tsaknakis, John, Tzimas, Giannis, and Vassiliadis, Vassilios

Abstract

This paper describes the experience and the problems solved in the process of developing and integrating advanced World Wide Web-based services into the University of Patras (Greece) system. In addition to basic network services (e.g., e-mail, file transfer protocol), the final system will integrate the following set of advanced services: a Web-based information service, intranet services to support administrative operations within the campus, distance learning by means of online teletraining via the Web, teleworking facilities, videoconferencing facilities, and applications supporting collaborative work. The paper describes in detail the services provided by the system, system architecture, and the implementation and introduction of the services to users. This project has shown that the development of the services, as well as their integration and introduction to the users, has to follow a well-defined, user-oriented implementation plan. Two figures present the representation of Web servers in the campus network and the intranet architecture. Contains 19 references. (AEF)

Published
1997

21. A Genetic Algorithm for Discovering Linguistic Communities in Spatiosocial Tensors with an Application to Trilingual Luxemburg

Author

Drakopoulos, Georgios, Stathopoulou, Fotini, Tzimas, Giannis, Paraskevas, Michael, Mylonas, Phivos, Sioutas, Spyros, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Boracchi, Giacomo, editor, Iliadis, Lazaros, editor, Jayne, Chrisina, editor, and Likas, Aristidis, editor

Published
2017
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22. (A)kNN Query Processing on the Cloud: A Survey

Author

Nodarakis, Nikolaos, Rapti, Angeliki, Sioutas, Spyros, Tsakalidis, Athanasios K., Tsolis, Dimitrios, Tzimas, Giannis, Panagis, Yannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sellis, Timos, editor, and Oikonomou, Konstantinos, editor

Published
2017
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26. Mining Domain-Specific Design Patterns

Author

Gkantouna, Vassiliki, Tzimas, Giannis, Tampakas, Basil, Tsaknakis, John, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor

Published
2016
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28. kdANN+: A Rapid AkNN Classifier for Big Data

Author

Nodarakis, Nikolaos, Pitoura, Evaggelia, Sioutas, Spyros, Tsakalidis, Athanasios, Tsoumakos, Dimitrios, Tzimas, Giannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hameurlain, Abdelkader, editor, Küng, Josef, editor, Wagner, Roland, editor, Decker, Hendrik, editor, Lhotska, Lenka, editor, and Link, Sebastian, editor

Published
2016
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29. Convex Polygon Planar Range Queries on the Cloud: Grid vs. Angle-Based Partitioning

Author

Nodarakis, Nikolaos, Sioutas, Spyros, Gerolymatos, Panagiotis, Tsakalidis, Athanasios, Tzimas, Giannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Karydis, Ioannis, editor, Sioutas, Spyros, editor, Triantafillou, Peter, editor, and Tsoumakos, Dimitrios, editor

Published
2016
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30. Graph DBs vs. Column-Oriented Stores: A Pure Performance Comparison

Author

Kendea, Marios, Gkantouna, Vassiliki, Rapti, Angeliki, Sioutas, Spyros, Tzimas, Giannis, Tsolis, Dimitrios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Karydis, Ioannis, editor, Sioutas, Spyros, editor, Triantafillou, Peter, editor, and Tsoumakos, Dimitrios, editor

Published
2016
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32. Exploiting Expert Systems in Cardiology: A Comparative Study

Author

Economou, George-Peter K., Sourla, Efrosini, Stamatopoulou, Konstantina-Maria, Syrimpeis, Vasileios, Sioutas, Spyros, Tsakalidis, Athanasios, Tzimas, Giannis, Cohen, Irun R., Series editor, Lajtha, N.S. Abel, Series editor, Paoletti, Rodolfo, Series editor, Lambris, John D., Series editor, Vlamos, Panayiotis, editor, and Alexiou, Athanasios, editor

Published
2015
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33. An Approach for Mining Social Patterns in the Conceptual Schema of CMS-Based Web Applications

Author

Gkantouna, Vassiliki, Athanasios, Tsakalidis, Tzimas, Giannis, Viennas, Emmanouil, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Daniel, Florian, editor, and Diaz, Oscar, editor

Published
2015
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34. A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems.

Author

Yu He, Xiaofan Dong, Simos, Theodore E., Mourtas, Spyridon D., Katsikis, Vasilios N., Lagios, Dimitris, Zervas, Panagiotis, and Tzimas, Giannis

Subjects
JOB classification, SOCIAL science research, BIOLOGICALLY inspired computing, METAHEURISTIC algorithms, MACHINE learning, OCCUPATIONAL exposure, CLASSIFICATION algorithms
Abstract

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Towards a Domain-Specific Conceptual Modeling Approach of Open Source CMS

Author

Gkantouna, Vassiliki, Ioannou, Zafeiria-Marina, Tsakalidis, Athanasios, Tsaknakis, John, Tzimas, Giannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Parsons, Jeffrey, editor, and Chiu, Dickson, editor

Published
2014
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38. Efficient Multidimensional AkNN Query Processing in the Cloud

Author

Nodarakis, Nikolaos, Pitoura, Evaggelia, Sioutas, Spyros, Tsakalidis, Athanasios, Tsoumakos, Dimitrios, Tzimas, Giannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Decker, Hendrik, editor, Lhotská, Lenka, editor, Link, Sebastian, editor, Spies, Marcus, editor, and Wagner, Roland R., editor

Published
2014
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40. Exploiting Fuzzy Expert Systems in Cardiology

Author

Sourla, Efrosini, Syrimpeis, Vasileios, Stamatopoulou, Konstantina-Maria, Merekoulias, Georgios, Tsakalidis, Athanasios, Tzimas, Giannis, Iliadis, Lazaros, editor, Papadopoulos, Harris, editor, and Jayne, Chrisina, editor

Published
2013
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42. Proposing a Novel Monitoring and Early Notification System for Heart Diseases

Author

Sourla, Efrosini, Tsakalidis, Athanasios, Tzimas, Giannis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bursa, Miroslav, editor, Khuri, Sami, editor, and Renda, M. Elena, editor

Published
2013
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45. A Web and Smart Phone System for Tibia Open Fractures

Author

Gkintzou, Vasiliki, Papablasopoulou, Theodora, Syrimpeis, Vasileios, Sourla, Efrosini, Tzimas, Giannis, Tsakalidis, Athanasios, Cruz-Cunha, Maria Manuela, editor, Varajão, João, editor, Powell, Philip, editor, and Martinho, Ricardo, editor

Published
2011
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47. A Personality Mining System for Automated Applicant Ranking in Online Recruitment Systems

Author

Faliagka, Evanthia, Kozanidis, Lefteris, Stamou, Sofia, Tsakalidis, Athanasios, Tzimas, Giannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Auer, Sören, editor, Díaz, Oscar, editor, and Papadopoulos, George A., editor

Published
2011
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48. Investigating the Integration of Hand-Held Haptic Devices in Daily Work Activities: The Case of a Tennis Coaching Assistant on iPhone

Author

Faliagka, Evanthia, Karkoulias, Petros, Rigkou, Maria, Sirmakessis, Spiros, Tzimas, Giannis, Tsakalidis, Athanasios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Marcus, Aaron, editor

Published
2011
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