The conference fosters in-depth technical-scientific presentations as well as inspiring talks by experts from industry and academia. The following speakers have confirmed their participation in the conference to deliver keynotes and invited talks on big data topics:

Tim Bell (CERN), Erik Elmroth (UmeƄ University), SK Reddy (Hexagon), and others to be confirmed.

You may also be interested in speakers on utility and cloud computing.

Speaker and Bio Topic and Abstract
BDCAT Keynote: Tim Bell, CERN. Tim is responsible for the group at CERN, the European Laboratory for Particle Physics, which manages the compute infrastructure for 13,000 physicists around the world to support fundamental research. He previously worked as a Unix kernel developer at IBM along with managing large scale Unix production deployments and services for Deutsche Bank in Europe. He is an elected member of the OpenStack Foundation board of directors since 2012. Clouds at CERN: a 5 year perspective. Driven by the computing needs of the Large Hadron Collider and other CERN experiments, a new approach to managing compute resources was deployed in 2013 based on a cloud computing infrastructure and agile methodologies. This talk will review the experiences of using cloud computing for scientific research and administrative computing, lessons learnt, the current status and the outlook for the next years.
BDCAT Keynote: Erik Elmroth, UmeƄ University. Erik Elmroth is Professor in Computing Science at UmeƄ University. He has been Head and Deputy Head of the Department of Computing Science for ten years. He is leading the UmeƄ University research on distributed systems, focusing on theory, algorithms, and systems for the autonomous management of ICT resources, spanning from individual servers to large scale cloud datacenters, federated clouds, highly distributed edge clouds, and software-defined infrastructures. Elmroth's research group participates frequently in large-scale national and international collaborations such as eSSENCE (a long-term Swedish strategic eScience collaboration), the EU FP7 and H2020 projects RESERVOIR, OPTIMIS, VISION Cloud, CACTOS, ORBIT, RECAP, and ACTiCLOUD, the EU networks ACROSS and AAPELE, as well as the local industry-focused ICT research within the UMIT research laboratory. Elmroth is leading the UmeƄ University activities in the 1.8 billion SEK Wallenberg Autonomous Systems Program (WASP), partly as member of the WASP Research Strategy Group. He is also leading an 8 postdoc project in autonomous systems funded by the Kempe Foundations. A particular high-light is being the principal investigator for Cloud Control, which is a framework project funded by the Swedish Research Council, taking a control theoretic approach to fundamental problems for autonomous cloud datacenter management systems. What just went wrong, where and why? Or did it? - Autonomous anomaly detection for a connected world. To manage the increasingly larger and more complex applications on equally large and complex clouds, mobile edge clouds, and other emerging infrastructures, we increasingly rely on automation and (semi-)autonomous systems. This is done not only to improve their availability, reliability, and performance but also to reduce cost and manual labor. The operation is largely relying on planning and feedback providing information for the many knobs used to steer the systems, knobs being auto-scalers, service differentiators, schedulers, orchestrators, power managers, etc. A valuable complement to planning, feedback, and steering is the ability to detect when things go wrong, that is, to automatically detect anomalies as well as to identify their-root causes and suitable actions to rectify them. Although anomaly detection is being performed in many completely different areas, the problem is still open and challenging as its solutions require equal amount of machine learning and domain knowledge. In this presentation we will start by setting the scene with some illustrative key management tasks and solutions before turning attention to anomaly detection and resolution, where we will map the field and show some recent progress, in particular addressing performance, security, and functional anomalies.
BDASE Workshop Invited Speaker: SK Reddy, Hexagon. SK Reddy is the Chief Product Officer AI in Hexagon. He is an AI and ML expert and a successful twice startup entrepreneur. He is an AI startup advisor too. Also he is a frequent speaker in conferences and is an AI blogger. His focus of research is in Image processing, NLP and large data processing using ML and DL. Predictive maintenance for industry 4.0. Industry 4.0 is driven by digital revolution and digital twins. With the availability of better data, AI techniques could be used to better predict when the machines needs repair/maintenance and also compute the remaining-life of the machine. Machines or vehicles breaking down without notice is costly for factory management and also dangerous for humans operating near these machines. Hence monitoring the machines for potential breakdown is useful. There are around 5000 known statistical techniques that could be used to detect anomalous behavior of machines or vehicles. k-Nearest Neighbors (k-NN), Local Outlier Factor (LOF), Influenced Outlierness (INFLO), Local Outlier Probability (LoOP), Local Correlation Integral (LOCI), Approximate Local Correlation Integral (aLOCI), Cluster Based Local Outlier Factor (CBLOF) including unweighted-CBLOF (uCBLOF), Local Density Cluster-based Outlier Factor (LDCOF), Clustering-based Multivariate Gaussian Outlier Score (CMGOS), Histogram-based Outlier Score (HBOS), One-class Support Vector Machines (ocSVM), Robust Principal Component Analysis (rPCA), etc. are some of them. Deep Learning techniques, independently and in conjunction with machine learning techniques, are further improving the quality of predictions. I will discuss the practical solutions of predicting maintenance needs of machines using machine learning and deep learning.

More information on the speakers, the content and the scheduling will be provided in the coming days.

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