Expo Hall sessions

In the era of 24-hour news and online newspapers, editors in the newsroom must quickly and efficiently make sense of the enormous amounts of data that they encounter and make decisions about their content. Daniel Gilbert and Jonathan Leslie discuss an ongoing partnership between News UK and Pivigo in which a team of data science trainees helped develop an AI platform to assist in this task. Read more.

Konstantinos Georgatzis and Martha Imprialou explain how to interpret the predictions given by your black-box model and how machine learning is helping to drive decision making today. Read more.

Graph databases are becoming mainstream. Patrick McFadin explains how to use the knowledge you have gained from your years of working with relational databases in this brave new world. There are many similarities but also some significant differences that can open up completely new use cases. If you're deciding whether to take the plunge into graph databases, this is the talk for you. Read more.

Most machine learning algorithms are designed to work on stationary data, but real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Emre Velipasaoglu reviews monitoring methods, focusing on their applicability in fast data and streaming applications. Read more.

The BMW Group IT team drives the usage of data-driven technologies and forms the nucleus of a data-centric culture inside of the organization. Tobias Bürger and Joshua Görner discuss the E-to-E relationship of data and models and share best practices for scaling applications in real-world environments. Read more.

Temporal data is being produced in ever-greater quantity, but fortunately our time series capabilities are keeping pace. Jared Lander explores techniques for modeling time series, from traditional methods such as ARMA to more modern tools such as Prophet and machine learning models like XGBoost and neural nets. Along the way, Jared shares theory and code for training these models. Read more.

Erik Nordström explains how and why to use PostgreSQL as a Prometheus backend to support complex questions (and get a proper SQL interface), offers an overview of pg_prometheus, a custom Prometheus datatype, and prometheus-postgresql-adapter, a remote storage adaptor for PostgreSQL, and shares his experience with TimescaleDB, which enables PostgreSQL to scale for classic monitoring volumes. Read more.

Spark NLP natively extends Spark ML to provide natural language understanding capabilities with performance and scale that was not possible to date. David Talby, Saif Addin Ellafi, and Paul Parau explain how Spark NLP was used to augment the Recognos smart data extraction platform in order to automatically infer fuzzy, implied, and complex facts from long financial documents. Read more.

Switzerland-based startup WinJi capitalizes on two current megatrends: big data and renewable energy. Stamatis Stefanakos offers an overview of WinJi's TruePower Asset Management Platform, covering the overall architecture and the motivation behind it, the physics behind the data, and the business case. Read more.