[TALK]@Telematika

Talks & Presentations Collection

Applications of natural language understanding: Tools and technologies
November 20, 2019

Applications of natural language understanding: Tools and technologies

With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases.

Avoid big data becoming a big problem

Avoid big data becoming a big problem

November 20, 2019

Raghunath Nambiar reviews the big data landscape, reflects on big data lessons learned in enterprise over the last few years, and explores how these organizations avoid their big data environments becoming unmanageable by using simplex management for deployment, administration, monitoring, and reporting no matter how much the environment scales.
Best practices to extract value from Hadoop with predictive analytics

Best practices to extract value from Hadoop with predictive analytics

November 20, 2019

Turning big data into tangible business value can be a struggle even for highly skilled data scientists. Zoltan Prekopcsak outlines the best practices that make life easier, simplify the process, and implement results faster.
BI on Hadoop: What are your options?

BI on Hadoop: What are your options?

November 20, 2019

There are (too?) many options for BI on Hadoop. Some are great at exploration, some are great at OLAP, some are fast, and some are flexible. Understanding the options and how they work with Hadoop systems is a key challenge for many organizations. Tomer Shiran provides a survey of the main options, both traditional (Tableau, Qlik, etc.) and new (Platfora, Datameer, etc.).
Big data at Google: Solving problems at scale

Big data at Google: Solving problems at scale

November 20, 2019

Google is no stranger to big data, pioneering several big data technologies grown and tested internallyincluding MapReduce, BigTable, and most recently Dataflow and TensorFlow, as well as one of the most heavily used tools at Google, BigQueryand making them available to everyone. Jordan Tigani shares what big data means for Google and announces several new BigQuery features.
Breaking Spark: Top five mistakes to avoid when using Apache Spark in production

Breaking Spark: Top five mistakes to avoid when using Apache Spark in production

November 20, 2019

Spark has been growing in deployments for the past year. The increasing amount of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine. Drawing on his experiences across 150+ production deployments, Neelesh Srinivas Salian explores common issues observed in a cluster environment setup with Apache Spark.
Bringing big data and design to policy making

Bringing big data and design to policy making

November 19, 2019

Cat Drew explains how the UK's Policy Lab and GDS data teams are bringing more of a data, digital, and design approach to policy making, showcasing some of the Policy Lab projects that have used ethnography and data science to create fresh insight to change the way we think about policy problems.
Building real-time BI systems with HDFS and Kudu

Building real-time BI systems with HDFS and Kudu

November 19, 2019

Ruhollah Farchtchi explores best practices for building systems that support ad hoc queries over real-time data and offers an overview of Kudu, a new storage layer for Hadoop that is specifically designed for use cases that require fast analytics on rapidly changing data with a simultaneous combination of sequential and random reads and writes.
Data relativism and the rise of context services

Data relativism and the rise of context services

November 19, 2019

The traditional data warehouse of the 1990s was quaintly called the single source of truth. Joe Hellerstein explains why today we take a far more relativistic view: the meaning of data depends on the context in which it is used.
Data wants to be shareable

Data wants to be shareable

November 19, 2019

Data has more potential value when it can be shared. In order to monetize data, it must first be made shareable: shareable data is an asset that can be sold, traded, or used to create new data marketplaces. Mona Vernon outlines a framework to structure thinking about data shareability and monetization and explores these new business opportunities.
Deep learning for web-scale text

Deep learning for web-scale text

November 19, 2019

Piotr Mirowski looks under the hood of recurrent neural networks and explains how they can be applied to speech recognition, machine translation, sentence completion, and image captioning.
Developing a successful big data strategy

Developing a successful big data strategy

November 19, 2019

Many businesses have undertaken big data projects, but for every successful project, there are dozens that have failed or stagnated. Seb Darrington explores the reasons why such projects hit obstacles, typical challenges, and how to overcome them along your own big data journey.
Don't build a data swamp: Hadoop governance case studies for financial services

Don't build a data swamp: Hadoop governance case studies for financial services

November 19, 2019

Mark Donsky and Chang She explore canonical case studies that demonstrate how leading banks, healthcare, and pharmaceutical organizations are tackling Hadoop governance challenges head-on. You'll learn how to ensure data doesn't get lost, help users find and trust the data they need, and protect yourself against a data breachall at Hadoop scale.
Drawing insights from imperfection: A year of Dear Data

Drawing insights from imperfection: A year of Dear Data

November 18, 2019

Stefanie Posavec recently completed a year-long drawing project with Giorgia Lupi called Dear Data, where each week they manually gathered and drew their data on a postcard to send to the other. Stefanie discusses the professional insights she gained spending a year on such an intensive personal data project.
HopsWorks: Multitenant Hadoop as a service

HopsWorks: Multitenant Hadoop as a service

November 18, 2019

Currently, multitenancy in Hadoop is limited to organizations running separate Hadoop clusters, and the secure sharing of resources is achieved using virtualization or containers. Jim Dowling describes how HopsWorks enables organizations to securely share a single Hadoop cluster using projects and a new metadata layer that enables protection domains while still allowing data sharing.
How to date (a film)

How to date (a film)

November 18, 2019

Using data science to help make better business decisions in the film industry.
Introduction to generalized low-rank models and missing values

Introduction to generalized low-rank models and missing values

November 18, 2019

The generalized low-rank model is a new machine-learning approach for reconstructing missing values and identifying important features in heterogeneous data. Through a series of examples, Jo-fai Chow demonstrates how to fit low-rank models in a parallelized framework and how to use these models to make better predictions.
Machine learning for human rights advocacy: Big benefits, serious consequences

Machine learning for human rights advocacy: Big benefits, serious consequences

November 18, 2019

Megan Price demonstrates how machine-learning methods help us determine what we know, and what we don't, about the ongoing conflict in Syria. Megan then explains why these methods can be crucial to better understand patterns of violence, enabling better policy decisions, resource allocation, and ultimately, accountability and justice.
Making sense of exactly-once semantics

Making sense of exactly-once semantics

November 18, 2019

Exactly-once semantics is a highly desirable property for streaming analytics. Ideally, all applications process events once and never twice, but making such guarantees in general either induces significant overhead or introduces other inconveniences, such as stalling. Flavio Junqueira explores what's possible and reasonable for streaming analytics to achieve when targeting exactly-once semantics.
Modern data strategy and CERN

Modern data strategy and CERN

November 17, 2019

Clouderas Mike Olson is joined by Manuel Martin Marquez, a senior research and data scientist at CERN, to discuss Hadoop's role in the modern data strategy and how CERN is using Hadoop and information discovery to help drive operational efficiency for the Large Hadron Collider.
Petascale genomics

Petascale genomics

November 17, 2019

The advent of next-generation DNA sequencing technologies is revolutionizing life sciences research by routinely generating extremely large datasets. Tom White explains how big data tools developed to handle large-scale Internet data (like Hadoop) help scientists effectively manage this new scale of data and also enable addressing a host of questions that were previously out of reach.
Pixels and place: What online experiences can borrow from offline spaces and vice versa

Pixels and place: What online experiences can borrow from offline spaces and vice versa

November 17, 2019

The metaphors used online have always borrowed heavily from the offline world, but as our online and offline worlds converge, the biggest opportunities for innovative experiences will come from blending them intentionally. Kate ONeill examines how the meaning and understanding of place relates to identity, culture, and intent and how we can shape our audiences' experiences more meaningfully.
Prophecies and predictive models: How a 3D approach to data transforms your business

Prophecies and predictive models: How a 3D approach to data transforms your business

November 17, 2019

The famous Oracle at Delphi had a secret: Its prophecies were interpreted by Temple Guides, using a very early version of ethnographic research. With todays near-blind faith in the predictive power of Big Data, its time to take a lesson from the Ancient Greeks.
Protecting individual privacy in a data-driven world

Protecting individual privacy in a data-driven world

November 17, 2019

With the analytic and predictive power of big data comes the responsibility to respect and protect individual privacy. As citizens, we should hold organizations to account; as data practitioners, we must find intelligent ways to analyze data without violating privacy. Jason McFall discusses privacy risks and surveys leading privacy-preserving analysis techniques.
Realizing the value of combining the IoT and big data analytics

Realizing the value of combining the IoT and big data analytics

November 17, 2019

The IoT combined with big data analytics enables organizations to track new patterns and signs and bring data together that previously was not only a challenge to integrate but also way too expensive. Frank Saeuberlich and Eliano Marques explain why data management, data integration, and multigenre analytics are foundational to driving business value from IoT initiatives.
Saving whales with deep learning

Saving whales with deep learning

November 17, 2019

Piotr Niedwied explores how deepsense.io created the worlds best deep learning model for identifying individual right whales using aerial photography for the NOAA (National Oceanic and Atmospheric Administration) and explains what happened when the solution was covered by news media around the globe.
Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra

Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra

November 17, 2019

Which venues have similar visiting patterns? How can we detect when a user is on vacation? Can we predict which venues will be favorited by users by examining their friends' preferences? Natalino Busa explains how these predictive analytics tasks can be accomplished by using Spark SQL, Spark ML, and just a few lines of Scala code.
Simple, fast, and flexible risk aggregation in Hadoop

Simple, fast, and flexible risk aggregation in Hadoop

November 17, 2019

Value at risk (VaR) is a widely used risk measure. VaR is not simply additive, which provides unique challenges to report VaR at any aggregate level, as traditional database aggregation functions don't work. Deenar Toraskar explains how the Hive complex data types and user-defined functions can be used very effectively to provide simple, fast, and flexible VaR aggregation.