Watch recordings of all presentations made during our Community Day Summit held during October 2023. Find out more about this event and read our blog review of the event.
In addition to these presentations, we also held a Technical Poster Competition for students to present work they have completed on projects leveraging HPCC Systems. See our poster contest wiki to find out more about our 2023 Poster Contest participants and winners.
Resources
Presentation Tracks and Content
Plenary Sessions
Join us as Gavin Halliday, SVP and Head of Platform Engineering, LexisNexis® Risk Solutions, kicks off the 11th annual HPCC Systems Community Virtual Summit.
Welcome & Plenary Keynotes
Gavin Halliday, SVP and Head of Platform Engineering, LexisNexis® Risk Solutions/ Michael Stefanick, Managing Director, EY
Watch as Gavin Halliday kicked off the 11th annual HPCC Systems Community Virtual Summit, as well as hear from our community keynote speaker Michael Stefanick, who presented, AI + Human: How AI will Disrupt Work and Allow Workers to Thrive.
Awards Ceremony & Plenary
Trish McCall, Sr Director Program Management, Hugo Watanuki, Manager Community Tech Programs, George S Foreman, Business Analyst II, Bob Foreman, Software Engineering Lead LexisNexis Risk Solutions
Join us for the long-awaited announcement of the 2024 Community Awards and Poster Competition winners, followed by our afternoon keynote honoring our academic community and engagement.
Platform Evolution
The latest techniques in optimizing the Cloud Native HPCC Systems platform
HPCC Systems Deployment using K3D instance
Aryaman Gautam, Sidharth Ganesan, Srinivasan Kothandam, LexisNexis Risk Solutions ITTC
This integration package enables HPCC Systems trainers and trainees to install the HPCC Systems engine in any standalone system for training and Practice. This will enable the user to work on HPCC Systems components including Thor, Roxie, Dali Storage, and ECL at the localized environment.A Better Understanding of Thor and Roxie Configurations for the Containerized HPCC Systems Platform
Godji Fortil & Krishna Turlapathi, LexisNexis Risk Solutions
In the HPCC Systems Cloud Native Platform, configurations for the Thor and Roxie engines are coded in YAML, stored in a file and passed to the helm deployment. For this presentation, our goal is to go over those settings, recommendations and best practices, then deploy the HPCC Systems Platform using Terraform.How to Enable Azure Log Analytics for the Containerized HPCC Systems Platform Using Terraform
Godji Fortil, LexisNexis Risk Solutions
For log solutions, the HPCC Systems Cloud Native Platform provides a standard log interface to process HPCC Systems logs. During this presentation, we will demonstrate how to enable Azure Log Analytics using our newly developed Terraform module for HPCC Systems logs.Optimizing Business Solutions with Azure Fullstack Products
Mauricio Nunes and Matheus Machado, LexisNexis Risk Solutions
Learn how our LexisNexis Risk Solutions business in Brazil launched two data products using an HPCC Systems implementation on Azure. The speakers will provide an overview of components including a Web portal, Batch and ESP API as delivery channels served from a Roxie and MySQL backend, all on commercial Azure. They will close with a product demo of identity verification and fraud solutions using fictitious data while diving into the architecture, best practices, challenges, and technical experiences shared by the team.
Productivity with HPCC Systems
Leverage the improvements and innovative features in the latest HPCC Systems 9.x release
Roxie Performance: A Deeper Technical Dive into 9.x
Gavin Halliday, LexisNexis Risk Solutions
In this presentation, Gavin will discuss some of the different factors that affect Roxie performance, as well as cover details including some of the recent platform changes which aim to make this job easier. Anyone deploying production Roxie queries or interested in the technical details of how Roxie indexes are implemented can benefit from attending this session.HPCC Systems Landing Zone Security
Russ Whitehead, LexisNexis Risk Solutions
Data is imported to and exported from an HPCC Systems Logical File from a file system location known as a Landing Zone. Landing zone access was previously authorized via the LDAP Security Manager feature permissions, but once a user had access to it they could access the entire Landing Zone directory. Beginning with HPCC Systems Version 9.0, admins can specify Landing Zone scopes, using the same ECL Watch interface as file scopes and workunit scopes. This presentation describes the motivation for the additional security and walks the attendee through the creation of these scopes.ECL Watch: Redefining Progress
Kunal Aswani, LexisNexis Risk Solutions
Delve into ECL Watch and check out the snapshot queries in Roxie, logging, hot spot identification, cost estimation, and metrics for efficient data retrieval.A Threat Detection and Mitigation Framework: HPCC Systems and Azure at the core
Sowmya Myneni, LexisNexis Risk Solutions
While it is fantastic to advertise that we are confirming various security controls (CIS, FedRAMP), how do we actually prove it? This talk is going to explore in-depth the concepts around control effectiveness by showing the audience some example security controls we are implementing at LexisNexis Risk Solutions and how the HPCC Systems data analytics platform is used to collect, explore and analyze the data.
Programming with HPCC Systems
Extend your ECL with powerful language tools
Building a Scalable and Efficient Bitcoin Blockchain Parser on the HPCC Systems Platform / Optimization of Learning Trees using ECL on HPCC Systems
Jyothi Shetty, RVCE / Manonmani S & Sudershan K S, RVCE
This joint session includes two presentations from RV College of Engineering featuring the work from academic collaboration as part of the HPCC Systems Centre of Excellence (CoE) in Cognitive Intelligent Systems for Sustainable Solutions (CISSS).
Building a Scalable and Efficient Bitcoin Blockchain Parser on the HPCC Systems Platform, Jyothi Shetty:
Many commercial applications are interested in analyzing Bitcoin blockchain data, but due to the large volume of transactions the processing of transaction data becomes a challenge. A distributed and parallelized architecture such as HPCC Systems can be a solution to efficiently analyse such massive amounts of data. The work proposes to build a C++-based Bitcoin parsing algorithm embedded within ECL to create a robust and efficient blockchain parser. The proposed approach takes raw blockchain data in the form of blk.dat files as input and processes the headers and transaction data within each block. Furthermore, it maps input addresses to output addresses of previous transactions, constructing a connected network of blocks that accurately resembles the blockchain. By iteratively traversing this chain of blocks, the parsed contents are then written to a CSV file. The suggested implementation offers flexibility as it does not rely on external library dependencies and can be executed as a single ECL file. This is a work in progress study. An initial functional implementation of the parser code has been deployed successfully on hThor and the next step is to expand its capability to Thor so the parser can fully take advantage of HPCC Systems distributed and parallel processing capabilities. As the size of the blockchain data continues to grow, having a scalable and reliable ECL Bitcoin parser that enables retrieval of blockchain data with minimal overhead is quite beneficial.
Optimization of Learning Trees using ECL on HPCC Systems, Manonmani S & Sudershan KS
Some core ECL functions on HPCC Systems, like the ones leveraged in the LearningTrees Machine Learning Bundle are recursive in nature and hence tend to demand higher computational effort under certain conditions. This talk presents an alternative solution for optimized execution of the LearningTrees algorithms in ECL via embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in execution time was observed with no impact on the accuracy of the models: while the embedding technique provided approximately the same accuracy levels as the current existing ML library functions, the execution time was reduced by 4-5 times making it an extremely fast and efficient technique. The target audience who will benefit from this talk includes industry professionals who wish to explore the resources for solving big data problems, technology enthusiasts willing to learn latest trends and technologies, and researchers working in big data and high-performance computing domains.
Building Trustworthy and Auditable Digital Human Readers Superior to Humans with NLP++
David de Hilster & Ashton Williamson, LexisNexis Risk Solutions & Clemson University
Machine Learning, Neural Networks and Large Language Models are statistical and opaque, human memory is fallible, and NLP++ is now stepping in to fix these problems and build trustworthy systems in areas such as law enforcement, healthcare, and sentiment analysis. Not only can NLP++ perform better than humans, but it can build systems that eventually can be better than human experts. Unlike the statistical methods of ML, NN, and LLM which are opaque and not auditable, NLP++ is a verifiable and auditable technology which is a game-changer in today’s critical systems. NLP++ is a glass box computer programming language and framework that seamlessly integrates with HPCC systems to provide sophisticated text processing that until now, have been only achievable by humans. We will look at work being done at Clemson to help solve the medical coding task that is currently done by human readers and has a 50% failure rate.Digital Human Readers Making History with NLP++ and HPCC Systems
Charvi Dave, Kruthika Pinnada, Pedro Rodrigues, Dheemonth Kodali, Shyamaa Karthik, David de Hilster LexisNexis Risk Solutions, RVCE, USP Brazil, St Andrews School
From resume processing, to sentiment analysis, to digital dictionaries, NLP++ and HPCC Systems are making history. Searching for resumes is precarious when using simple keyword search and new work using NLP++ is making resume searching an exact science. Sentiment analysis till now has been done with statistical and keyword methods and have been too generic to used as real-world systems. Two sentiment analyzers break that mold, one involving soccer teams in Brazil, and the other cricket teams in India. NLP digital dictionaries have either been stuck in time for decades or are non-existent. Two Wiktionary projects use NLP++ to parse linguistic information from Wiktionary pages, processing hundreds of thousands and millions of pages using HPCC Systems, and creating dictionaries that can be used in future NLP systems. The result will be the most comprehensive English dictionary ever created for NLP and the first digital dictionary with linguistic information ever created for the Tamil language.Parquet Support for ECL
Jack Del Vecchio, LexisNexis Risk Solutions
Introducing the Parquet Plugin, an interface between ECL and Apache Arrow that gives the ECL programmer the capability to interact with the parquet file format. This talk will demonstrate how ECL programmers can efficiently read and write parquet files with ease. With this interface, ECL programmers can partition datasets, read any partitioned or non-partitioned dataset, and write to a parquet file. In the demo all the functions of the plugin will be shown. One of the key highlights of the plugin is its capability to handle datasets larger than memory. By leveraging streaming techniques, we ensure efficient processing of large-scale datasets without sacrificing performance or data integrity. Attendees will gain insights into the integration of parquet and how the Apache Arrow library was leveraged to give the ECL programmer efficient access to the parquet file format. A variety of demos to include usage examples will be shown as well as opportunities to ask questions and learn more about the plugin.
HPCC Systems in Action
Real World Use Cases with HPCC Systems
An Understanding of HPCC Systems Platform Metrics
Ken Rowland, LexisNexis Risk Solutions
HPCC Systems provides a metric framework for collecting low level platform specific metrics. This presentation covers the framework, available metrics, component instrumentation, and metric collection. It also covers the new ESP method execution profiling. All of it is tied together with examples from a running cluster.Robots, Drones and Generative AI: Exploring cutting edge technologies with HPCC Systems in a school campus environment
Tai Donovan, Carlos Caceres, Nick Schwartz, American Heritage School
School safety security, and wellbeing have been a driving force for our most recent academic partnership with HPCC Systems. The first phase of our security project began in 2020 with an autonomous mobile sentry designed, built and programmed with facial recognition software developed by our students. This project was very effective, and we wanted to enhance the capabilities by increasing response times for any security concern with the use of drones. This latest version uses autonomous drone technology to enhance the security features of our school by detecting unauthorized personnel on campus, assisting security in response to active shooter situations, gathering information during lockdowns and to track the student/staff evacuation process if initiated. In parallel to these latest efforts, a recent HPCC Systems summer internship project investigated the possibility on whether generative AI could be used to generate human-like responses in our autonomous security robot robots. During the internship, an interface through HPCC Systems was constructed to interface interact with GPT and ChatGPT. Therefore, we expect to be able to use a standard neural network to identify different emotions given video of a person and these these emotions would then be processed by the interface to create a call to OpenAI’s API from which an appropriate response would be generated. This response would be designed to change based on the emotions classified by the neural network and most importantly due to the use of generative AI would be different even with the same emotional classification. Even though this is all still in the prototype phase, our projects have proven to be a great proof of concept developed by high school students to deal with the reality of our times.Tombolo - Opensource Tools for Interacting with HPCC Systems Clusters
Matt Fancher & Yadhap Dahal, LexisNexis Risk Solutions
Introducing Tombolo version 2.0, an innovative open-source project initiated by LexisNexis Risk Solutions, aimed at providing data catalog capabilities for HPCC Systems clusters. A web application that caters to both technical and non-technical users, facilitating seamless interaction with HPCC Systems clusters. Join us in this enlightening session as we demonstrate the impressive capabilities of Tombolo. Discover how users can effortlessly create workflows by leveraging assets from HPCC Systems clusters, enhanced with our newly introduced versioning tool. This tool empowers users with increased confidence when editing existing workflows. Witness the application's exceptional ability to monitor assets and proactively send timely notifications via different medias, ensuring crucial data management tasks are completed promptly. Moreover, we'll showcase Tombolo's intuitive dashboard and its extensive range of API endpoints, offering a comprehensive overview of asset performance. During the presentation, our team will also share insights into the future development goals of Tombolo, providing a glimpse into the exciting features currently in development and planned for future additions.Vulnerable Victim Monitoring Connecting the Dots to Find Missing Children Quickly
Pasqua Ruggiero, LexisNexis Risk Solutions
According to Child Find of America, an estimated 2,300 children go missing each day. That equates to over half a million missing children per year. Most of these children are runaways, a population that is quite vulnerable to trafficking. Trafficked victims can be exploited for their PII in relation to account take over, stolen or synthetic identity fraud. LexisNexis Risk Solutions Inquiries are comprised of searches for fraud and credit seeking consumers which are stored in HPCC Systems. These hundreds of billions of inquiry data can be searched retroactively and monitored proactively for matches on missing children within a reasonable geographic area. Additional topics of discussion will include data visualization capabilities in Python with data obtained from both the National Center for Missing & Exploited Children’s public RSS feed (current missing cases) as well as all missing cases from the past 20 years (obtained from the ADAM Program). We will also discuss proposals for additional future studies utilizing the power of HPCC Systems to extract other types of big data and provide further insights and information to provide law enforcement and social services to better aid them in the search and prevention of human trafficking. *We would like to issue a content warning for this presentation for topics like human (and especially child) trafficking. We understand the difficulty of presenting information on this sensitive topic and appreciate your patience and understanding as we present our research. All data will be anonymized to not reveal any personal information.
ECL Training Workshops
The HPCC Systems Training and Support team has been very busy this year visiting universities and trade show events to promote HPCC Systems and ECL through Code Days, Hackathons, and Workshops. This year’s Community Summit workshop presents an in-depth look at these events in three 1-hour sessions:
Part 1: The “Music is Life!” Workshop
Bob Foreman, Software Engineering Lead, LexisNexis Risk Solutions
Who doesn’t love music? Take a break from your daily routine datasets and join us in this first hour. We break down a popular open-source music dataset, explore normalizing the dataset and its effects, and look at a variety of data evaluation and query techniques.Part 2: The “Find Your Paradise!” Hackathon
Bob Foreman, Software Engineering Lead, LexisNexis Risk Solutions
Have you ever thought about building an application that can help people find places to live that maximize their quality of life and happiness? The goal of this challenge is to analyze different datasets across different categories and correlate them using the HPCC Systems platform. After analyzing, the participants will be asked to design an interface to query this data and assign it a scoring system, then deliver it to the user via ROXIE and show the user where they should most likely want to live. Users should be given choices in an easy-to-use form that when submitted will generate a unique set of scores based on locations.Part 3: Better Customer Insights Through Relational Dataset Queries
Bob Foreman, Software Engineering Lead, LexisNexis Risk Solutions
In this final hour, we look at the power of the open data model, transforming normalized data into a denormalized relational dataset, and use the implicit relationality power to analyze relationships to provide better customer insights.