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SIGMOD 2018: Keynote Talks

Keynote Speaker 1: Eric Brewer (Google, UC Berkeley)

Kubernetes and the New Cloud


We are in the midst of shifting the notion of "Cloud" to a higher level of abstraction than virtual machines - one based on services, processes and APIs. Kubernetes epitomizes this shift and has rapidly become the de facto way to manage this new era of container-based applications. It aims to simplify the deployment and management of services, including the construction of applications as sets of interacting but independent services. We explain some of the key concepts in Kubernetes and Istio and show how they work together to simplify evolution, scaling and operations.


Eric Brewer is a VP of Infrastructure at Google and faculty at UC Berkeley. He pioneered the use of clusters of commodity servers for Internet services, and built the first "giant scale" systems. His "CAP Theorem" covers basic trade offs required in the design of distributed systems and followed from his work on a wide variety of real-world distributed systems. He is a member of the National Academy of Engineering, and winner of the ACM Prize in Computing for his work on large-scale services. At Berkeley he leads work on technology for developing regions including telemedicine systems that have helped to restore vision for thousands in India, and energy systems in Kenya and India that provide affordable electricity.

Keynote Speaker 2: Pedro Domingos (University of Washington)

Machine Learning for Data Management: Problems and Solutions


Machine learning has made great strides in recent years, and its applications are spreading rapidly. Unfortunately, the standard machine learning formulation does not match well with data management problems. For example, most learning algorithms assume that the data is contained in a single table, and consists of i.i.d. (independent and identically distributed) samples. This leads to a proliferation of ad hoc solutions, slow development, and suboptimal results. Fortunately, a body of machine learning theory and practice is being developed that dispenses with such assumptions, and promises to make machine learning for data management much easier and more effective. In particular, representations like Markov logic, which includes many types of deep networks as special cases, allow us to define very rich probability distributions over non-i.i.d., multi-relational data. Despite their generality, learning the parameters of these models is still a convex optimization problem, allowing for efficient solution. Learning structure - in the case of Markov logic, a set of formulas in first-order logic - is intractable, as in more traditional representations, but can be done effectively using inductive logic programming techniques. Inference is performed using probabilistic generalizations of theorem proving, and takes linear time and space in tractable Markov logic, an object-oriented specialization of Markov logic. These techniques have led to state-of-the-art, principled solutions to problems like entity resolution, schema matching, ontology alignment, and information extraction. Using tractable Markov logic, we have extracted from the Web a probabilistic knowledge base with millions of objects and billions of parameters, which can be queried exactly in subsecond times using an RDBMS backend. With these foundations in place, we expect the pace of machine learning applications in data management to continue to accelerate in coming years.


Pedro Domingos is a professor of computer science at the University of Washington and the author of The Master Algorithm, a bestselling introduction to machine learning for a broad audience. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation's CAREER Award, and numerous best paper awards. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning.

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