Not Acid, Not Base, but Salt - A Transaction Processing Perspective on Blockchains
Stefan Tai, TU Berlin, Germany
Big Data, Smart Data and Imbalanced Classification - Preprocessing, Models and Challenges
Francisco Herrera, University of Granada, Spain
Challenges on Big data based Clouds Health-Care for Risk Predictions based on Ensemble Classifiers and Subjective Analysis
Hamido Fujita, Iwate Prefectural University, Japan
Not Acid, Not Base, but Salt - A Transaction Processing Perspective on Blockchains
Stefan Tai
TU Berlin
Germany
Brief Bio
Stefan Tai is Full Professor and Head of Chair Information Systems Engineering at TU Berlin, Germany (2014-present). Prior to that, he was a Full Professor at the Karlsruhe Institute of Technology, Germany (2007-2014) and a Research Staff Member at the IBM Thomas J. Watson Research Center in New York, USA (1999-2007). Overall, Stefan has over 20 years of experience in cutting-edge IT research and development in both academia and in industry. Stefan works on creating next-generation IT software systems that provide a necessary foundation to address larger societal challenges. His research interests include distributed information systems, cloud services and systems, and the Web as a platform for technology, social, and business innovation. Currently, Stefan works passionately on blockchain-based solutions for a diversity of application domains. Stefan was involved in a number of industrial developments (mostly with IBM), holds several patents in the area of enterprise middleware and distributed systems, and has published over 100 peer-reviewed research articles. A new book on “Cloud Service Benchmarking” will appear in 2017, to be published by Springer. Stefan serves regularly as PC member of key conferences, as member of four editorial boards of relevant journals in the field, and as expert evaluator for scientific programs worldwide. He also acts as advisor to global IT companies and as mentor to start-up companies.
Abstract
Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. In this keynote paper, we present SALT as a model to explain blockchains and their use in application architecture. We take both, a transaction and a transaction processing systems perspective on the SALT model. From a transactions perspective, SALT is about Sequential, Agreed-on, Ledgered, and Tamper-resistant transaction processing. From a systems perspec-tive, SALT is about decentralized transaction processing systems being Symmetric, Admin-free, Ledgered and Time-consensual. We discuss the importance of these dual perspectives, both, when comparing SALT with ACID and BASE, and when engineering blockchain-based applications. We expect the next-generation of decentralized transactional applications to leverage combinations of all three transaction models.
Big Data, Smart Data and Imbalanced Classification - Preprocessing, Models and Challenges
Francisco Herrera
University of Granada
Spain
http://decsai.ugr.es/~herrera
Brief Bio
Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has been the supervisor of 42 Ph.D. students. He has published more than 400 journal papers that have received more than 62000 citations (Scholar Google, H-index 125). He is coauthor of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015), "The 2-tuple Linguistic Model. Computing with Words in Decision Making" (Springer, 2015), "Multilabel Classification. Problem analysis, metrics and techniques" (Springer, 2016), "Multiple Instance Learning. Foundations and Algorithms" (Springer, 2016). He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as editorial member of a dozen of journals. He received the following honors and awards: ECCAI Fellow 2009, IFSA Fellow 2013, 2010 Spanish National Award on Computer Science ARITMEL to the "Spanish Engineer on Computer Science", International Cajastur "Mamdani" Prize for Soft Computing (Fourth Edition, 2010), IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015 respectively), 2011 Lotfi A. Zadeh Prize Best paper Award of the International Fuzzy Systems Association, 2013 AEPIA Award to a scientific career in Artificial Intelligence, and 2014 XV Andalucía Research Prize Maimónides (by the regional government of Andalucía), 2017 Security Forum I+D+I Prize, and 2017 Andalucía Medal (by the regional government of Andalucía). He has been selected as a Highly Cited Researcher http://highlycited.com/ (in the fields of Computer Science and Engineering, respectively, 2014 to present, Clarivate Analytics). His current research interests include among others, soft computing (including fuzzy modeling, evolutionary algorithms and deep learning), computing with words, information fusion and decision making, and data science (including data preprocessing, prediction and big data).
Abstract
Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. To overcome this issue, the MapReduce framework has arisen as a"de facto" solution. Basically, it carries out a "divide-and-conquer" distributed procedure in a fault-tolerant way to adapt for commodity hardware.
Learning with imbalanced data refers to the scenario in which the amounts of instances that represent the concepts in a given problem follow a different distribution. The main issue when addressing such a learning problem is when the accuracy achieved for each class is also different. This situation occurs since the learning process of most classification algorithm is often biased towards the majority class examples, so that minorities ones are not well modeled into the final system. Being a very common scenario in real life applications, the interest of researchers and practitioners on the topic has grown significantly during these years. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts are accentuated during the data partitioning to fit the MapReduce programming style.
In this talk we will pay attention to the imbalanced big data classification problem, we will analyze the current research state of this are, the behavior of standard preprocessing techniques in this particular framework toward, and we will carry out a discussion on the challenges and future directions for the topic.
Challenges on Big data based Clouds Health-Care for Risk Predictions based on Ensemble Classifiers and Subjective Analysis
Hamido Fujita
Iwate Prefectural University
Japan
http://www.fujita.soft.iwate-pu.ac.jp
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Brief Bio
Dr. Hamido Fujita, is a professor at Iwate Prefectural University(IPU), Iwate, Japan.
He is the director of Intelligent Software Laboratory.
He worked at Tohoku University as visiting Professor on late eighties, and then joined University of Tokyo, RCAST as Associate Professor, and then he moved to Canada, as visiting Professor at the University of Montreal.
Then after he joined Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan, as professor and head of Information System Division. He is directing at IPU two laboratories, Intelligent Software Laboratory and Cognitive Systems Laboratory.
Also, he is the founder of SOMET organization.
He has supervised Ph.D students jointly with University of Laval, University Technology, Syndey(UTS), He is also Professor at the University of Laval, Quebec, Canada supervising Graduate Studies students, he was a visiting Professor at the University of Paris_1, Sorbonne, 2003~2004. He worked as opponent for Stockholm University, Sweden co-supervised students. He also worked with UTS Sydney, Australia, co-supervised Ph.D students. He published books in IOS press. He guest edited several special issues on International Journal of Knowledge based systems, Elsevier where he is editor in this journal.
He is currently heading a Virtual Medical Doctor project supported by Ministry of Interior and communication of Japan, and a project related to Mental Cloning as an intelligent user interface between human user and computers, supported by MEXT (Ministry of Education, Culture, Sports, Science and Technology), of Japan.
Abstract
Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making.
The challenges in big data analytics are the high dimensionality and complexity in data representation. Granular computing and feature selection are among the challenge to deal with big data analytics that is used for Decision making. We will discuss these challenges in this talk and provide new projection on ensemble learning for health care risk prediction. In decision making most approaches are taking into account objective criteria, however the subjective correlation among different ensembles provided as preference utility is necessary to be presented to provide confidence preference additive among it reducing ambiguity and produce better utility preferences measurement for good quality predictions. Most models in Decision support systems are assuming criteria as independent. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analytics due to preprocessing and normalization processes which are expensive and difficult when data sets are raw and imbalanced. We will highlight these issues though project applied to health-care for elderly, by merging heterogeneous metrics for providing health care predictions for elderly at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams that collected from multi-sensing devices.
Subjectivity (i.e., service personalization) would be examined based on correlations between different contextual structures that are reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations. We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in granular computing and decision support systems research domains. In this talk I will present the current state of art and focus it on health care risk analysis with examples from our experiments.