Aims

BigDat 2016 will be a research training event for graduates and postgraduates in the first steps of their academic career. With a global scope, it aims at updating them about the most recent advances in the critical and fast developing area of big data, which covers a large spectrum of current exciting research and industrial innovation with an extraordinary potential for a huge impact on scientific discoveries, medicine, engineering, business models, and society itself. Renowned academics and industry pioneers will lecture and share their views with the audience.

Most big data subareas will be displayed, namely: foundations, infrastructure, management, search and mining, security and privacy, and applications. Main challenges of analytics, management and storage of big data will be identified through 4 keynote lectures, 20 six-hour courses, and one round table, which will tackle the most active and promising topics. The organizers believe outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes.

Addressed to

Graduate and postgraduates from around the world. There are no formal pre-requisites in terms of academic degrees. However, since there will be differences in the course levels, specific knowledge background may be assumed for some of them. BigDat 2016 is also appropriate for more senior people who want to keep themselves updated on recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators.

Regime

In addition to keynotes, at least 2 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they will be willing to attend as well as to move from one to another.

Venue

BigDat 2016 will take place in Bilbao, the capital of the Basque Country region, famous for its gastronomy and the seat of the Guggenheim Museum. The venue will be:

DeustoTech, School of Engineering
University of Deusto
Avda. Universidades, 24
48014 Bilbao, Spain

Keynote Speakers

• Nektarios Benekos (European Organization for Nuclear Research), Role of Computing and Software in Particle Physics
• Chih-Jen Lin (National Taiwan University), When and When Not to Use Distributed Machine Learning
• Jeffrey Ullman (Stanford University), Theory of MapReduce Algorithms
• Alexandre Vaniachine (Argonne National Laboratory), Big Data Technologies and Data Science Methods in the Higgs Boson Discovery

Professors and Courses

• Nektarios Benekos (European Organization for Nuclear Research), [introductory/intermediate] Exploring the Mysteries of our Cosmos: the Big Deal between Big Data and Big Science
• Hendrik Blockeel (KU Leuven), [intermediate] Decision Trees for Big Data Analytics
• Edward Y. Chang (HTC Health, Taipei), [introductory/intermediate] Big Data Analytics for Healthcare: Scalable Algorithms and Applications
• Nello Cristianini (University of Bristol), [introductory] THINKBIG: Towards Large Scale Computational Social Sciences, History and Digital Humanities
• Ernesto Damiani (University of Milan), [introductory/intermediate] Architectures, Models and Tools for Big-Data-as-a-Service
• Francisco Herrera (University of Granada), [introductory] Big Data Preprocessing
• Chih-Jen Lin (National Taiwan University), [introductory/intermediate] Large-scale Linear Classification
• George Karypis (University of Minnesota), [intermediate/advanced] Scaling Up Recommender Systems
• Geoff McLachlan (University of Queensland), [intermediate/advanced] Big Data Extensions of Some Methods of Classification and Clustering
• Wladek Minor (University of Virginia), [introductory/intermediate] Big Data and Structural Biology and Chemistry
• Raymond Ng (University of British Columbia), [introductory/intermediate] Mining and Summarizing Text Conversations
• Sankar K. Pal (Indian Statistical Institute), [introductory/advanced] Machine Intelligence and Granular Mining: Relevance to Big Data
• Erhard Rahm (University of Leipzig), [introductory/intermediate] Scalable and Privacy-preserving Data Integration
• Hanan Samet (University of Maryland), [introductory/intermediate] Sorting in Space: Multidimensional, Spatial, and Metric Data Structures for Applications in Spatial Databases, Geographic Information Systems (GIS), and Location-based Services
• Jaideep Srivastava (Qatar ComputingResearch Institute), [intermediary] Social Computing: Computing as an Integral Tool to Understanding Human Behavior and Solving Problems of Social Relevance
• Jeffrey Ullman (Stanford University), [introductory] Big Data Algorithms that Aren’t Machine Learning
• Alexandre Vaniachine (Argonne National Laboratory), [introductory/advanced] Big Data: Comparison with Computational Models
• Xiaowei Xu (University of Arkansas, Little Rock), [introductory/advanced] Big Data Analytics for Social Networks
• Fuli Yu (Baylor College of Medicine), [introductory/intermediate] Overview of Large-scale Genomics and Variant Analysis
• Mohammed J. Zaki (Rensselaer Polytechnic Institute), [introductory/advanced] Large Scale Graph Analytics and Mining