Miriam Fernandez, Knowledge Media Institute, Open University (UK)
Introduction to Mining Social Media Data
The Social Web has become one of the largest human information and communication systems in history, impacting the lives of billions of people around the world. This social phenomenon is transforming the world in ways that were never imagined, shaping how we communicate and share information, how we do business or how we elect governments. In this tutorial we will provide an introduction to different types of social media data (Twitter, Facebook, Wikis, Forums) and to some of the models, tools and methods to represent, enrich and analyse them. We will discuss how, by mining this unprecedented resource, we can help targeting societal challenges, such as policy making, climate change, or disaster management, among others. Note that presentations will be combined with hands-on sessions, so don't forget to bring your laptops with you!
Dr Miriam Fernandez is a Research Fellow at the Knowledge Media Institute (KMi), Open University, and a senior member of the Social Semantics and Web Science group. Before joining KMi, she was research associate at Universidad Autonoma de Madrid, Spain and software engineer (internship) at Google Zurich, Switzerland. Her research is at the intersection of the Web Science (WS) and Semantic Web communities (SW), where she has contributed with more than 100 peer-reviewed articles in various leading conferences and journals. She has extensive expertise in leading EU and national projects. She frequently participates in organising committees and editorial boards of the top SW and WS conferences, recently being program co-chair of the International Semantic Web conference in 2017, and serving as editor for the Journal of Web Semantics.
Fei Chiang, McMaster University (CA)
Introduction to Data Quality (co-presented with Mostafa Milani)
Poor data quality is a challenging and costly problem leading to inaccurate and untrusted data analytics results. Data cleaning involves enriching and structuring the raw data into a format that can be used in latter data management tasks. In this tutorial, we discuss qualitative and quantitative approaches in data cleaning, and survey commonly used data quality rules for error detection, and their application in data repair techniques. We also discuss contextual data quality, and present a multidimensional ontology that provides contextual data quality assessment by extending the multidimensional data model commonly found in OLAP and data warehousing applications.
Fei Chiang is an Assistant Professor in Computer Science at McMaster University in Canada. She is a Faculty Fellow at the IBM Centre for Advanced Studies, and has over 15 years experience in data management spanning academic and industry roles, including serving as Associate Director of McMaster’s MacData Institute. She received her M.Math from the University of Waterloo, and B.Sc and PhD degrees from the University of Toronto, all in Computer Science. Her research interests are in data quality, data cleaning, and dependency discovery. Her research is supported by NSERC, CFI, MRIS, OCE and IBM.
Denis Parra, PUC Chile (CL)
A Tutorial on Information Visualization and Visual Analytics
This tutorial introduces fundamental aspects in information visualization such as marks, channels, idioms, and principles from
cognitive science which allow to design effective visualizations. In order to analyze existent visualizations and implement new ones
in a systematic way, we will introduce the framework by Munzner (2014) which addresses the problem with nested levels: Domain/situation (what),
Data/task abstraction (why), visual encoding/interaction idiom (How), and algorithms. Several examples of existent visualizations are analyzed
within this framework in order to understand its usage, and finally rules of thumb are synthesized for preventing common mistakes when
designing new visualizations. Finally, examples which combine data mining methods with visualizations are explained in detail in
order to understand and eventually implement models for visual analytics.
Denis Parra is Assistant Professor at the Department of Computer Science, in the School of Engineering at PUC Chile. He holds a Ph.D. in Information Science from University of Pittsburgh, USA. His main research interests are Recommender Systems, Intelligent User Interfaces and Information Visualization. He teaches courses on Information Visualization, Recommender Systems and Data Mining at PUC Chile. He has published in important conferences in the area such as ACM RecSys, ACM IUI, UMAP, ACM Hypertext and ECIR, as well as in journals such as ACM TiiS, PloS One, EPJ Data Science, IJHCS, and ESWA. He has earned a best paper award at UMAP conference 2011 and a best reviewer award at WWW conference 2017. He currently leads the SocVis Research Group at PUC Chile.
Martín Ugarte, Free University of Brussels (BE)
Understanding the Bitcoin Protocol
In the last five years, Bitcoin has gained a good deal of attention, reaching in December 2017 a market capitalization close to three hundred billion (US) dollars and a daily trading volume of hundreds of millions of dollars. Although Bitcoin is considered mostly as a financial asset, the protocol published in 2008 by the anonymous Satoshi Nakamoto goes far beyond: it achieves consensus in a decentralized and trust-less network. In this tutorial we will discuss in detail how this consensus is achieved, from the underlying cryptography to the specifics of the protocol and the so-called Blockchain data structure. We will also discuss the incentives of the different actors in the Bitcoin network, and how these incentives complement the underlying cryptography to enforce the security of the digital currency.
Martín Ugarte is a postdoctoral researcher at the Free University of Brussels (ULB) in Belgium, where he has been working for the last two years under the Laboratory for Web & Information Technology. He obtained a PhD from the Catholic University of Chile on the topic of logics and query languages for the Semantic Web. His current research interests include query answering over dynamic datasets and information extraction, amongst other aspects of data management. He has recently published and participated on the main database conferences and journals. Martin is also an advocate of Cryptocurrencies, and has given tutorials on several universities and institutions on the foundational aspects of Cryptocurrencies and their applications to Smart Contracts.