The DEBS 2019 Summer School welcomes participation from graduate students, researchers and professionals who are working on topics that are related to Machine Learning for Real-time Analytics. The event aims at graduate students, researchers and practitioners starting or being active in the field of Machine Learning for Real-time Data Analytics. There will be keynotes by distinguished speakers. Furthermore, there will be plenty of opportunities to exchange ideas and to discuss the topics with other participants and speakers. The summer school will be followed by the traditional doctoral symposium to which Ph.D. candidates are invited to submit and discuss their research work.
Machine learning (ML) is a key discipline and set of tools for the modern data analytics. It allows engineers to build systems that learn by themselves from data. The topics of the summer school include (but are not limited to) Machine Learning techniques for CEP, Deep Learning, real-time analytics, complex-event detection, data stream processing, big/fast data analysis, event processing for AI/ML, AI/ML for event processing.
Technology has been the key enabler of the current Big Data movement. Without open-source tools like R and Spark, as well as the advent of cheap, abundant computing and storage in the cloud, the trend toward datafication of almost every field in research and industry could never have happened. However, the current Big Data tool set is ill-suited for interactive data analytics to better involve the human-in-the-loop which makes the knowledge discovery a major bottleneck in our data-driven society. In this talk, I will present an overview of our current research efforts to revisit the current Big Data stack from the user interface to the underlying hardware to enable interactive data analytics and machine learning on large data sets.
Carsten Binnig is a Full Professor in the Computer Science department at at TU Darmstadt and an Adjunct Associate Professor in the Computer Science department at Brown University. Carsten received his PhD at the University of Heidelberg in 2008. Afterwards, he spent time as a postdoctoral researcher in the Systems Group at ETH Zurich and at SAP working on in-memory databases. Currently, his research focus is on the design of data management systems for modern hardware as well as modern workloads such as interactive data exploration and machine learning. His work has been awarded with a Google Faculty Award, as well as multiple best paper and best demo awards for his research.
Continuous, dynamic and short-term learning is an effective learning strategy when operating in very fast and dynamic environments, where concept drift constantly occurs. In an on-line, stream learning model, data arrives as a stream of sequentially ordered samples, and older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. Learning takes place by processing a sample at a time, inspecting it only once, and as such, using a limited amount of memory; stream approaches work in a limited amount of time, and have the advantage to perform a prediction at any point in time during the stream. In this talk I focus on a particularly challenging problem, that of continually learning detection models capable to recognize cyber-attacks and system intrusions in a highly dynamic environment such as the Internet. I consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using a dynamic, variable length system memory which automatically adapts to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, I show that adaptive learning algorithms can continuously realize high detection accuracy over dynamic network data streams. I would additionally present other approaches leading to the continual acquisition of knowledge, by extending traditional active learning schemes to the streaming domain, through the application of reinforcement learning concepts. Taking a broader look into the application of AI for Networking (Ai4NETS), I will conclude my talk by elaborating on some of the major showstoppers hindering a natural application of machine learning in the networking applied field.
Dr. Pedro Casas is Scientist in ICT Security and Information Management at the AIT Austrian Institute of Technology in Vienna. He received an Electrical Engineering degree from Universidad de la República, Uruguay in 2005, and a Ph.D. degree in Computer Science from Institut Mines-Télécom, Télécom Bretagne, France in 2010. He was Postdoctoral Research Fellow at the LAAS-CNRS lab in Toulouse between 2010 and 2011, and Senior Researcher at the Telecommunications Research Center Vienna (FTW) between 2011 and 2015. He works as project manager and technical work leader in different networking-related initiatives, including research projects and commercial solutions. His work focuses on machine-learning and data mining based approaches for Networking, big data analytics and platforms, Internet network measurements, network security and anomaly detection, as well as QoE modeling, assessment and monitoring. He has published more than 135 Networking research papers in major international conferences and journals, received 12 awards for his work - including 7 best paper awards, and he is general chair for different conferences, workshops and leading actions in network measurement and analysis, including the IEEE ComSoc ITC Special Interest Group on Network Measurements and Analytics. He is leading the Big-DAMA project on Big Data analytics for network traffic monitoring and analysis.
|Abstract submission for research track||
|Research and Industry paper submission||
|Tutorial proposal submission||
|Grand challenge solution submission|
|Author notification research and industry track|
|Poster, demo & doctoral symposium submission||April 22nd, 2019|