Big Data Processing & Analytics - HPCC Systems (LexisNexis)
IMPORTANT: Please register at the Webinar link here:
Join us as we continue this series of webinars specifically designed for the community by the community with the goal to share knowledge, spark innovation, and further build and link the relationships within our HPCC Systems community. Featured speakers and topics include:
Jeremy Meier and David Noh, both Undergraduate Students at Clemson University - An Investigation into Time Series Analysis
Over the past several months, our team has worked closely with a dataset having roughly 16,000 total observations, recording both the date and balance in financial data. Focusing on individual accounts with a size of around 400 observations, our first goal was to compare statistical metrics and techniques used commonly in time series analysis on the given data sets. We dove deep into two major industry standard methods for understanding and predicting on a dataset. Using insights learned from these observations, we hope to better predict future balances in the dataset, as well as find any anomalies or misbehavior in the data in order to provide business value.
Jeremy is a senior undergraduate student, majoring in Computer Science at Clemson University. He is originally from Greenville, South Carolina, and he is conducting research with Dr. Apon’s group with a focus on time series analysis. In the past, he has worked with HPCC Systems in the development of text analysis libraries. His other interests include bioengineering and animation.
David is a senior undergraduate student, majoring in Computer Science at Clemson University. He is working on research with a focus on machine learning algorithms and time series analysis. His interests include machine learning algorithms and high performance computing.
Roger Dev, Sr Architect, LexisNexis Risk Solutions - TextVectors - Machine Learning for Textual Data
Text Vectorization allows for the mathematical treatment of textual information. Words, phrases, sentences, and paragraphs can be organized as points in high-dimensional space such that closeness in space implies closeness of meaning. HPCC Systems' new TextVectors module supports vectorization for words, phrases, or sentences in a parallelized, high-performance, and user-friendly package.
Roger is a Senior Architect working on the Machine Learning team at LexisNexis Risk Solutions. Roger has been involved in the implementation and utilization of machine learning and AI techniques for many years, and he has over 20 patents in diverse areas of software technology.
Allan Wrobel, Consulting Software Engineer, LexisNexis Risk Solutions - ECL Tip: Leveraging the power of HPCC Systems. Using AGGREGATE.
The ECL built-in function AGGREGATE has been seen by many in the community as ‘complex’ and as such has been underused. However, in using AGGREGATE you can be sure you’re playing to the strengths of HPCC Systems.
Allan has worked with LexisNexis Risk Solutions since 2011 and the inception of LexisNexis Risk Solutions in the UK. Initially working with
Data Operations, Allan now serves as an ECL developer on both Thor and ROXIE.
Submit a talk for an upcoming episode!
It’s easy! All you need to do is submit a talk title and brief abstract to email@example.com. If chosen, you will be asked to present remotely for an upcoming 20-minute tech talk. Learn more about our Tech Talks: https://wiki.hpccsyst...
Alpharetta, GA - USA
Thursday, April 25 at 11:00 AM