Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Schedule: Time Series and Graphs sessions

12:0512:45 Wednesday, 23 May 2018
Ira Cohen (Anodot)
The mobile world has so many moving parts that a simple change to one element can cause havoc somewhere else, resulting in issues that annoy users and cause revenue leaks. Ira Cohen outlines ways to use anomaly detection to track everything mobile, from the service and roaming to specific apps, to fully optimize your mobile offerings. Read more.
14:0514:45 Wednesday, 23 May 2018
Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Télécom ParisTech)
Average rating: ***..
(3.00, 1 rating)
Heitor Murilo Gomes and Albert Bifet offer an overview of StreamDM, a real-time analytics open source software library built on top of Spark Streaming, developed at Huawei's Noah’s Ark Lab and Télécom ParisTech. Read more.
14:0514:45 Wednesday, 23 May 2018
Data engineering and architecture, Expo Hall
Location: Expo Hall Level: Intermediate
Tags: us
Patrick McFadin (DataStax)
Average rating: *****
(5.00, 2 ratings)
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.
14:5515:35 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 12 Level: Intermediate
Mikio Braun (Zalando)
Average rating: ****.
(4.40, 15 ratings)
Time series data has many applications in industry, in particular predicting the future based on historical data. Mikio Braun offers an overview of time series analysis with a focus on modern machine learning approaches and practical considerations, including recommendations for what works and what doesn't. Read more.
16:3517:15 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 12 Level: Intermediate
Arun Kejariwal (Independent), Francois Orsini (MZ)
Average rating: ***..
(3.14, 7 ratings)
The rate of growth of data volume and velocity has been accelerating along with increases in the variety of data sources. This poses a significant challenge to extracting actionable insights in a timely fashion. Arun Kejariwal and Francois Orsini explain how marrying correlation analysis with anomaly detection can help and share techniques to guide effective decision making. Read more.
17:2518:05 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 12 Level: Intermediate
Fabian Yamaguchi (ShiftLeft)
Average rating: ****.
(4.33, 3 ratings)
Fabian Yamaguchi offers an overview of Code Property Graph (CPG), a unique approach that allows the functional elements of code to be represented in an interconnected graph of data and control flows, which enables semantic information about code to be stored scalably on distributed graph databases over the web while allowing them to be rapidly accessed. Read more.
11:1511:55 Thursday, 24 May 2018
Data science and machine learning, Expo Hall
Location: Expo Hall Level: Beginner
Jared Lander (Lander Analytics)
Average rating: ****.
(4.00, 2 ratings)
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.
12:0512:45 Thursday, 24 May 2018
Erik Nordström (Timescale)
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.
14:0514:45 Thursday, 24 May 2018
Tony Xing (Microsoft), Bixiong Xu (Microsoft)
Average rating: **...
(2.00, 1 rating)
Tony Xing and Bixiong Xu offer an overview of Project Kensho, Microsoft's one-stop shop for business incident monitoring and automated insights. Tony and Bixiong cover the technology's evolution, the architecture, the algorithms, and the benefits and the trade-offs. Along the way, they share a case study on Bing ads key metrics monitoring and automated diagnostic insights. Read more.
14:5515:35 Thursday, 24 May 2018
Data engineering and architecture
Location: S11A Level: Intermediate
Jim Webber (Neo4j)
Average rating: *****
(5.00, 3 ratings)
Jim Webber details how Neo4j mixes the strongly consistent Raft protocol with async log shipping and provides a strong consistency guarantee: causal, which means you can always at least read your writes even in very large multidata center clusters. Read more.
14:5515:35 Thursday, 24 May 2018
Data science and machine learning
Location: Capital Suite 13 Level: Intermediate
Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
Average rating: ****.
(4.00, 2 ratings)
Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud. Read more.