Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Temporal data and time-series sessions

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13:3017:00 Tuesday, 30 April 2019
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio)
Many industry segments have been grappling with fast data (high-volume, high-velocity data). In this tutorial we shall lead the audience through a journey of the landscape of state-of-the-art systems for each stage of an end-to-end data processing pipeline - messaging, compute and storage - for real-time data and algorithms to extract insights - e.g., heavy-hitters, quantiles - from data streams. Read more.
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13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15
Francesca Lazzeri (Microsoft), Aashish Bhateja (Microsoft)
Time series modeling and forecasting has fundamental importance to various practical domains and, during the past few decades, machine learning model-based forecasting has become very popular in the private and the public decision-making process. In this tutorial, we will walk you through the core steps for using Azure Machine Learning to build and deploy your time series forecasting models. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Sami Niemi (Barclays)
Predicting transaction fraud of debit and credit card payments in real-time is an important challenge, which state-of-art supervised machine learning models can help to solve. Barclays has been developing and testing different solutions and will show how well different models perform in variety of situations like card present and card not present debit and credit card transactions. Read more.
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12:0512:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Recently, Sequence-2-Sequence has also been used for applications based on time series data. In this talk, we first overview S2S and the early use cases of S2S. Subsequently, we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Jian Chang (Alibaba Group), Sanjian Chen (Alibaba Group)
We would like to share the architecture design and many detailed technology innovations of Alibaba TSDB, a state-of-the-art database for IoT data management, from years of development and continuous improvement. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Alun Biffin (Van Lanschot Kempen), David Dogon (Van Lanschot Kempen)
In this talk we describe how machine learning revolutionized the stock picking process for portfolio managers at Kempen Capital Management by filtering the vast small-cap, investment universe down to a handful of optimal stocks. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Guoqiong Song (Intel)
Collecting and processing massive time series data (e.g., logs, sensor readings, etc.), and detecting the anomalies in real time is critical for many emerging smart systems, such as industrial, manufacturing, AIOps, IoT, etc. This talk will share how to detect anomalies of time series data using Analytics Zoo and BigDL at scale on a standard Spark cluster. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Shivnath Babu (Unravel Data Systems | Duke University), Alkis Simitsis (Micro Focus)
Cost and resource provisioning are critical components of the big data stack. A magic 8-ball for the big data stack would give an enterprise a glimpse into its future needs and would enable effective and cost-efficient project and operational planning. This talk covers how to build that magic 8-ball, a decomposable time-series model, for optimal cost and resource allocation for the big data stack. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Christian Hidber (bSquare)
Reinforcement learning (RL) learns complex processes autonomously like walking, beating the world champion in go or flying a helicopter. No big data sets with the “right” answers are needed: the algorithms learn by experimenting. We show “how” and “why” RL works in an intuitive fashion & highlight how to apply it to an industrial, hydraulics application with 7000 clients in 42 countries. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 7
Erik Nordström (Timescale)
Requirements of time-series databases include ingesting high volumes of structured data; answering complex, performant queries for both recent & historical time intervals; & performing specialized time-centric analysis & data management. I explain how one can avoid these operational problems by re-engineering Postgres to serve as a general data platform, including high-volume time-series workloads Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Christopher Hooi (Land Transport Authority of Singapore)
The Fusion Analytics for Public Transport Event Response (FASTER) system provides a real-time advanced analytics solution for early warning of potential train incidents. Using novel fusion analytics of multiple data sources, FASTER harnesses the use of engineering and commuter-centric IoT data sources to activate contingency plans at the earliest possible time and reduce impact to commuters. Read more.