A Reflection on the Words Uncertain and Probable

Transcription

1 Keynote Speakers Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

2 A Reflection on the Words Uncertain and Probable Enric Trillas Keynote Speaker Enric Trillas, (Barcelona, 1940), got his Ph.D in Sciences by the University of Barcelona. Full professor in 1974, with a chair in Mathematics at the Technical University of Catalonia, served there up to 1988, when he moved into the Department of Artificial Intelligence at the Technical University of Madrid. Retired in 2006, and up to the beginning of 2016 he was an Emeritus Researcher at the European Centre for Soft Computing (Mieres, Spain), and currently is an Honorary Emeritus Professor at the University of Oviedo. He is recognized as who introduced Fuzzy Logic in Spain. Formerly working in Ordered Semigroups and Probabilistic and Generalized Metrics, from 1976 is active in Fuzzy Logic, having received several international E. Trillas ( ) European Centre for Soft Computing, Mieres (Asturias), Spain etrillasetrillas@gmail.com Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

3 88 E. Trillas distinctions and honors. Doctor Honoris Causa by the Public University of Navarra and the University of Santiago de Compostela in Spain, is a Distinguished Visiting Professor at the University of Córdoba (Argentina). He published over 400 papers in National and International journals, conferences and edited books, as well as is editor or author of some other books in English, Spanish and Catalan. His work in Fuzzy Logic began by studying fuzzy entropies, shifted to logical connectives where he characterized strong negations, introduced t-norms and t-conorms, did the first analysis of the Modus Ponens inequality, introduced T-Indistinguishabilities, and the concept of conjecture. Currently, he mainly works in the analysis of conjecturing in Ordinary Reasoning, the representation of the meaning of words, the linguistic roots of fuzzy sets, the creative speculation, and is deeply interested in the philosophical reflection on these subjects. He did stays and seminars in several universities and research centers in Spain, France, Germany, Italy, United States, Japan, Argentina and Chile. Between 1983 and 1996 served in the Spanish Government being, for instance, the President of the High Council for Scientific Research (CSIC), and the Director General of the National Institute for Aerospace Technology (INTA). Holds several civil and military decorations from Spain and other countries, like Italy and the Republic of Peru. Fellow of IFSA, and Honorary Member of EUSFLAT, was distinguished with the EUSFLAT s European Fuzzy Pioneer Award, the IEEE-CIS Fuzzy Pioneer Award, the Kampé de Fériet Award, the Medal of the Polish Academy of Sciences, and the IFSA s Outstanding Achievement Award. He also holds the Spanish Cajal and Monturiol Medals, as well as a National Award in Computer Science.

4 Semantics Data Integration Through the Use of Fuzzy Approximate Reasoning and Defuzzification Elizabeth Chang Keynote Speaker Elizabeth Chang is a Professor in IT and Logistics and Canberra Fellow at the University of New South Wales at the Australian Defence Force Academy (ADFA). She hold BSc, MSc and PhD in Computer Science and Software Engineering and has been CIO/CTO in Multinational Logistics and Transport Company in Hong Kong. Professor Chang leads the research group at UNSW Canberra at ADFA targeting the key issues in Logistics big data management over the heterogeneous logistics, supply and transport networks, predictive analytics, situation awareness, cyber-physical systems, asset sustainment, trust measurement and prediction and risk assessment and quantification. She has delivered 41 Keynote/Plenary speeches largely at major IEEE Conferences and most recent ones are in the area of Seman- E. Chang ( ) University of New South Wales, Kensington, NSW, Australia e.chang@adfa.edu.au Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

5 90 E. Chang tics, Ontologies, business intelligence, data quality, etc. Her academic achievement includes 22 Competitive Research Grants including 11 Australian Research Council (ARC) Grants worth over $15 million. She has supervised/co-supervised 41 PhD theses to completion, 21 Maters Theses and 16 Post-docs. She has published 7 authored, 8 edited books, 15 book chapters, 100+ International journal papers and over 300 refereed conference papers with an H-Index of 35 (Google Scholar). She is a Fellow of IEEE, and the Chair for IEEE IES Technical Committee on Industrial Informatics ( ). She provides leadership by working with top researchers from around world and directs the future research in this area in the world. She is also an Associate Editor for IEEE Transactions on Industrial Electronics (since 2007) and Guest Editor on IEEE Transactions on Industrial Informatics (since 2005), Co-editor in chief for International Journal on Engineering Intelligent Systems. She has been General Chair and Technical Chair for over 20 International and IEEE Conferences.

6 Tutorials Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

7 From a Data-Driven Towards a Knowledge-Driven Society: Making Sense of Data Edy Portmann, Ulrich Reimer, and Gwendolin Wilke Introduction In our four-part workshop, we will focus on data as a primary resource for our society and, with methods from soft computing, sense-making of raw data to augment human conditions for example expanding the smart city concept to a concept of cognitive cities that, in a humanistic sense, gears to human-centered computing. With this in mind, in the first part, we focus on deep learning to generate natural language descriptions of data mining results. In the second part, we concentrate on granular computing as a forceful toolbox for representing and reasoning with different types and levels of data abstraction. In part three, we demonstrate, how the previous parts are critical elements to overcome the concept of smart cities towards cognitive cities, in which opposed to many of todays smart city concepts humans are the center of development. In the fourth and last part of our workshop, we will collectively develop solutions to introduced challenges. E. Portmann ( ) University of Bern, Bern, Switzerland edy.portmann@iwi.unibe.ch U. Reimer Department Information Science, University of Applied Sciences, St. Gallen, Switzerland ulrich.reimer@fhsg.ch G. Wilke Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland gwendolin.wilke@hslu.ch Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

8 94 E. Portmann et al. Part 1: Sensor Networks in Mobile Health: Making Sense of Sensor Data Sensors are becoming prevalent in our society. For example, they monitor weather and environmental pollution, they help track goods along supply chains, and they enable more flexible and adaptable manufacturing processes. In the medical domain, wearable sensors and sensors built into smartphones enable the monitoring of activities and vital data of patients. While it is simple to collect tons of sensor data, it remains difficult to make sense out of them. Data mining algorithms are employed to gain insights from the data by determining correlations and hidden patterns in the data. In many situations, however, sensor data cannot directly be fed into data mining algorithms because the data is too low-level and not significant enough to be used as features. Instead, appropriate features need to be derived first. However, in most cases it is not clear beforehand which features would work best, so that determining meaningful features requires a lot of experimentation. Deep learning is an unsupervised feature learning approach that can be used to derive higher-level features from low-level sensor data and thus helps to avoid time-consuming experiments to identify relevant features. Another central building block for making sense out of sensor data is how to communicate the insights gained to the user in a comprehensible way. Visualizations are a powerful form of communication but are in many cases not suited to represent patterns identified in a data stream. For example, correlations between a user s daytime behavior, environmental factors and his or her perceived quality of sleep cannot be properly visualized, but it could be nicely verbalized. Natural language descriptions of data mining results need to make use of fuzzy concepts to appropriately reflect their level of accuracy or vagueness. This part of the workshop will therefore focus on principles underlying deep learning as an unsupervised feature learning approach on sensor data and on the generation of natural language descriptions of data mining results. These principles will be discussed in the context of application scenarios from mobile health and can be transferred to other application areas where sensors play an important role. Instead of presenting ready-to-go recipes we will focus on sharing our experience collected in various projects and on fostering a lively discussion. Part 2: The Potential of Granular Computing and Fuzzy Logic for Automating Human-Centered Computing As introduced in the previous part, the proliferation of ubiquitous computing of recent years brings with it a dramatic increase in data collection, data generation and data processing. Raw and low-level data is manually and automatically processed to be used and reused by different parties in different semantic contexts and with

9 From a Data-Driven Towards a Knowledge-Driven Society: Making Sense of Data 95 different intentions. To this end, data is usually interpreted using aggregation, generalization, simplification or approximation. Within the resulting complex ecosystems of data processing flows it becomes increasingly important to automatically control and/or track the change in meaning of derived data. The ability to automatically differentiate necessary from unnecessary detail in a given context, as well as the ability to assess people s intentions requires that computers are able to process information in a way that emulates humans reasoning and sense-making. One pivotal element of human reasoning in this context is the ability to process information on different abstraction levels in other words, in different granularities. This second part of the workshop will thus provide an opportunity to discuss granular computing and in particular fuzzy logic as a framework, theoretical basis and methodological toolbox for explicitly representing and reasoning with different types and levels of abstraction, aggregation, generalization or approximation. Particularly, we will discuss its potential for mimicking contextual human reasoning and sense-making. Part 3: Enhancing Smart to Cognitive Cities with Fuzzy-Logic Based Human-Centered Computing Cognitive cities are a form of future cities applying elements of smart cities intertwined with elements of cognitive computing such as an ongoing humancomputer interaction. Underlying cognitive computing is an interdisciplinary field encompassing different topics such as collective and computational intelligence, computational thinking, and connected learning and cognition theories. These features improve cities abilities to face technological and civil challenges. Similar to the concept of responsive cities, cognitive cities focus on a human-centered approaches supported by technology. In this third part, thus, focusing on soft computing methods such as granular computing, computational intelligence, fuzzy logic and computing with words, we bring together the previous parts to develop livable future cities in a humanistic sense. Primarily, with the stated goal to balance smart cities with cognitive computing research by adhering to soft computing methods, we concentrate on user experiences. Thereby it comprises the practical, experiential, affective, meaningful and valuable aspects of human computer interaction. It primarily focuses on the citizens perceptions of a city such as utility, ease of use and efficiency. It is subjective in nature to the degree that it is about individual perception and thought with respect to the system and thereby is an ideal field of application of introduced soft computing methods.

10 96 E. Portmann et al. Part 4: Real-World Use Cases In this last part, based on use cases that are related to our previous parts, together we develop new concepts and solutions to challenges of our society. To this end, we introduce a couple of real-world use cases, which we address collectively in groups to make sense of data. Tutorial Instructors Edy Portmann is a researcher, specialist and consultant for semantic search, social media, and soft computing. At present, he is working as an Assistant Professor of information science at the University of Bern, Switzerland. In the past, Edy Portmann studied for a BSc in information systems, for an MSc in business and economics, and for a PhD in computer sciences. He was a Visiting Research Scholar at National University of Singapore (NUS), as well as a Postdoctoral Researcher at University of California at Berkeley, USA. During his studies, Edy Portmann worked several years in a number of organizations in study-related disciplines. He is repeated nominee for Marquis Who s Who, a selected member of the Heidelberg Laureate Forum, co-founder of Mediamatics (i.e., Media and Informatics) think tank, and co-editor of the Springer series Fuzzy Management Methods, as well as author of two popular books in his field. Ulrich Reimer studied computer science and received his doctorate in 1987 at the Information Science Dept. of the University of Konstanz with a thesis on formal

11 From a Data-Driven Towards a Knowledge-Driven Society: Making Sense of Data 97 ontologies for natural language understanding. In 1992 he received his habilitation at the University of Konstanz. For more than 10 years Ulrich Reimer was head of the IT R&D group of Swiss Life, the biggest life insurance company in Switzerland, and responsible for large-scale research projects, some of them funded by the EU. Since 2005 he has been with the Institute of Information and Process Management at the University of Applied Sciences St. Gallen where his main research activities are in the areas of Semantic Technologies, Behavioural Change Support Systems and Model-Driven Development, primarily in the application area of ehealth. Gwendolin Wilke holds a Ph.D. degree in Geoinformatics (2012) from the Vienna University of Technology (Austria), with a Thesis on uncertainty modelling for geodata. On invitation of Prof. Lotfi Zadeh she worked as a visiting researcher at

12 98 E. Portmann et al. UC Berkeley (USA) in 2009 in the field of fuzzy geometry. She extended this work as a visiting researcher at the Knowledge Representation and Reasoning Group at the University of Leeds (UK) where she worked on qualitative geometric reasoning in From 2012 to 2015, Gwen worked as a researcher at the information and knowledge management group at the University of Applied Sciences and Arts Northwestern Switzerland where she was involved in numerous national and international research projects. She regularly publishes research papers on the theory and application of fuzzy logic, soft computing and granular computing. Since 2015, Gwen is a lecturer for Business Intelligence at the School of Information Technology of the Lucerne University of Applied Sciences and Arts. Gwen is a member of the North American Fuzzy Information Processing Society and board member of the special interest group on Knowledge Structures and Metadata of the Swiss Informatics Society. In her current research, Gwen focuses on the theory and application of qualitative and granular computing to business intelligence, and, more generally, to knowledge management, specifically the challenges and opportunities of today s ever more complex data driven society.

13 Linguistic Summaries and Their Quality Miroslav Hudec Description The purpose of this tutorial is to give information related to linguistic summarization from the data. The increasing use of information systems by businesses and governmental agencies has created mountains of data that contain potentially valuable knowledge. Nowadays, mining summarized information from data sets is a topic of interest for researchers and practitioners. Summaries by short quantified sentences of natural language simulate human reasoning, i.e. provide summaries that are not as terse as the numbers. Furthermore, linguistically summarized sentence can be read out by a text-to-speech synthesis system, when the user s visual attention should not be distracted. In many situations data cannot be suitably visualized, but they can be verbally expressed. We can say that the linguistic summary is: a more or less accurate textual description (summary) of a data set This simple definition hides many challenges: construction of fuzzy sets for summarizers, restrictions and quantifiers, selecting appropriate aggregation functions, sufficient coverage of data, simplicity, usefulness and the like. When LS is of a good quality, we can use results for informing managers, generating fuzzy rules, support estimation of missing values, etc. The tutorial is divided into three main parts. M. Hudec ( ) Faculty of Economic Informatics, University of Economics in Bratislava, Bratislava, Slovakia miroslav.hudec@euba.sk Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /

14 100 M. Hudec Classic Prototype Forms (Protoforms) of Linguistic Summaries The main focus is on the classic protoforms, which are divided into basic structures of LS, (express information about particular attributes on the whole database, e.g. most of customers are young) and into structures with restriction (express relational knowledge among attributes on the part of a database delimited by the flexible (or sharp) restriction, for example most of old houses have high gas consumption). In addition, restriction and summarizer may be consisted of compound predicates merged by the and or or connectives. In this direction, we examine suitable t-norm functions for the and connective. Furthermore, we discuss summaries on attributes expressed by short sentences. Finally, other protoforms are outlined. Quality Measures Suitable for Summaries The high truth value (validity) of an examined summary is not always sufficient measure. It especially holds for LSs with restriction. These LSs may be trapped into outliers. Hence, quality measures focused on data coverage, simplicity and outliers should not be neglected. These measures should be properly aggregated. All t-norms meet axiomatic properties but differ in algebraic properties. Different t-norms are suitable for and connective among atomic predicates in summaries and for developing and aggregating quality measures. We also raise question of applying uninorms for aggregating these measures. Finally, illustrative interfaces for managing summaries and quality measures on real data are touched. Mining Summaries form Fuzzy Data Data are usually stored and used as numbers which pretend to be precise. But real data are frequently not available as precise numbers. When we want to keep information about vagueness, then data should be stored in fuzzy databases. However, fuzzy databases lack practical applications due to the complex structure for users. Hence, fuzzy meta model which stores attributes fuzzy values in classical relational database is an option. In this way we are able to build linguistic summaries from fuzzy data by possibility, necessity and similarity measures, among others. Thereof, we can create large varieties of possible summaries consisted of crisp numbers, fuzzy numbers, short sentences, crisp and fuzzy conditions as well as crisp and fuzzy quantifiers.

15 Linguistic Summaries and Their Quality 101 Tutorial Instructor Miroslav Hudec is a researcher and assistant professor at the University of Economics in Bratislava, Faculty of Economic Informatics. Prior to joining University of Economics in 2013, he was a researcher in the Institute of Informatics and Statistics, Bratislava. He received the Master and PhD degrees from the University of Belgrade. His work is mainly focused on fuzzy logic, knowledge discovery, and information systems. M. Hudec is author or co-author of approximately 45 scientific articles and three books. He is a member of program committees of several international conferences and serves as an editorial board member in Applied Soft Computing journal. He was research leader of two working packages of FP7 project focused on the modernization of data collection and analysis in official statistics. He was the representative of Slovakia on UNECE/Eurostat/OECD Meeting on the Management of Statistical Information Systems in years and 2013.

16 Author Index Al-Nahyan, Moza T., 73 Myrach, Thomas, 15 Balkenende, Daria, 47 Berkachy, Rédina, 35 Chang, Elizabeth, 89 Nápoles, Gonzalo, 59 Papageorgiou, Elpiniki, 59 Portmann, Edy, 15, 93 D Onofrio, Sara, 15 Dikopoulou, Zoumpolia, 59 Donzé, Laurent, 35 Radeleczki, Sándor, 1 Reimer, Ulrich, 93 E. Hawas, Yaser, 73 Schüetze, Roland, 47 Hudec, Miroslav, 25, 99 Trillas, Enric, 87 Kaltenrieder, Patrick, 15 Kovács, László, 1 Meier, Andreas, 47 Vanhoof, Koen, 59 Wilke, Gwendolin, 93 Springer International Publishing AG 2017 A. Meier et al. (eds.), The Application of Fuzzy Logic for Managerial Decision Making Processes, Fuzzy Management Methods, DOI /