From LotsOfCode to NoCode: Runtime-optimized analytics

Anastasia Ailamaki (Professor for Computer and Communication Sciences at the EPFL – École Polytechnique Fédérale de Lausanne) will talk about just-in-time (JIT) systems and how they synthesise a real-time intelligence paradigm to resolve most system performance challenges



Distinguished Lecture Series: What is Data Science?

The ever-increasing demand for diverse real-time analysis on exponentially growing data has brought a series of new system design challenges: First, we can no longer afford to pre-load the data in a database in order to support interactive analytics. Second, with the semiconductor advancement predicted by the end of Dennard scaling, hardware in servers becomes increasingly heterogeneous. Third, the need for throughput is increased as a function of the number of concurrent queries issued by applications and users, but current work sharing techniques do not scale. Fourth, data pipelines are made of heterogeneous tools, each optimized for each processing step, but cross-tool communication introduces high overheads. Finally, we need real-time processing over fresh data (aka Hybrid Transactional Analytical Processing or HTAP), but interference between heterogeneous workloads results in suboptimal performance. The common theme is increasing heterogeneity which is impossible to address efficiently with system design decision made ahead of time, as at design time we know too little too early.  Runtime decisions about both mechanisms and heuristics, on the other hand, always lead to efficient processing because optimal processing depends on the use case properties (data, workload, hardware, concurrency). Prof. Ailamaki will discuss novel just-in-time (JIT) systems which make and actuate decisions at runtime, and explain how the individual JIT solutions synthesise a real-time intelligence paradigm that helps resolve most system performance challenges.



When and where?

Friday, 21 October 2022 @ 13:00–14:30 CEST

BIG Hörsaal lecture hall
University of Vienna
TP Tiefparterre (map)
Universitätsring 1, 1010 Vienna



For organisational reasons, we are thankful for your registration until 17 October 2022 at the latest. 



Anastasia Ailamaki

Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, as well as the co-founder and Chair of the Board of Directors of RAW Labs SA, a Swiss company developing systems to analyze heterogeneous big data from multiple sources efficiently. She earned a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She has received the 2019 ACM SIGMOD Edgar F. Codd Innovations Award and the 2020 VLDB Women in Database Research Award. She is also the recipient of an ERC Consolidator Award (2013), the Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), an NSF CAREER award (2002), twelve best-paper awards in international scientific conferences. She has received the 2018 Nemitsas Prize in Computer Science by the President of Cyprus and the 2021 ARGO Innovation Award by the President of the Hellenic Republic. She is an ACM fellow, an IEEE fellow, a member of the Academia Europaea, and an elected member of the Swiss, the Belgian, the Greek, and the Cypriot National Research Councils.

© Christoph Kaminski