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September 13-15, 2022

Analytics+ Demo Replay
Graph Demo Day Replay
Machine Learning Demo Replay

Join us for three days of the best in Analytics, Spatial, Machine Learning, and Data Lakehouse presentations on the web.

September 13-15, 2022

Level up your knowledge and learn from the experts in Analytics, Spatial, and Machine Learning. In September we’re including a special Data Lakehouse Day. See demos and real-world use cases, ask questions, and get real answers, all from the comfort of your desk. Attend one session or all three days! We look forward to seeing you!

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Summer TechCast Days

2022 Sponsor Ui Path

If you wish to attend multiple days, you must register for each day separately.

September 13th
Analytics+ Machine Learning Day

Dan Vlamis, Edelweiss Kammermann, & Abi Giles-Haigh

*Session start times are subject to change.

11:05am–11:50am ET

11:50am–12:30pm ET

Assessing Post-Grad Outcomes Using Oracle Analytics and Autonomous Database


12:35pm–1:05pm ET




2:00pm ET
Closing Remarks

September 14th
Spatial and Machine Learning Day

Roger Cressey & Jean Ihm

*Session start times are subject to change.

11:00am–11:05am ET
Roger Cressey, Qubix; Jean Ihm, Oracle

11:05am-11:45am ET
Charge Me Up! Using Oracle Spatial, ML, Analytics, and APEX for Finding Optimal Charge Points
Jim Czuprynski, Zero Defect Computing, Inc.

11:45am–11:50am ET

11:50am–12:30pm ET
Smart Airports, On-Time Departure Machine Learning Solution on Oracle Cloud
Olivier Dubois, Robin Smith, Igor Vieira, Oscars S.A.

12:30pm-12:40pm ET
UiPath Brief Demo
KJ Fenton, UiPath

12:40pm–12:45pm ET

12:45pm-1:20pm ET
Building an Open Network Connectivity Model to Enable the Clean Energy Transition
Ken Korsmit, Spatial Eye

1:20pm–1:55pm ET
Leverage Location in Machine Learning with Oracle Autonomous Database and Python
David Lapp, Oracle

1:55-2:00pm ET
Closing Remarks
Roger Cressey & Jean Ihm

September 15th
Data Lakehouse Day

Edelweiss Kammermann & Roger Cressey

*Session start times are subject to change.

11:05am–11:50am ET
Oracle Autonomous Database – Built-In Data Tools

11:50am–12:35pm ET
Building Data Lakehouses In The Oracle Cloud

12:35pm-12:45pm ET
Modern Data Platform – “Secure Access To Trustworthy Data, As And When Needed, Enabling The Data Experience”

12:45pm–12:50pm ET

12:50pm-1:20pm ET
Why Are Data Warehouses Evolving To Lakehouses?

1:20pm-2:00pm ET

Analytics+ Machine Learning

Edelweiss Kammerman

Oracle Analysis Cloud includes many out-of-the-box Ml And AI features that make the life of the business analysts easier. These are very intuitive features that bring a lot of value and reduce the time of data cleansing and data analysis. But there is more. In the latest releases, we can find more tight integration with Oracle Machine Learning as well as with OCI AI Services. In this session, you will see all these features and how to integrate with ML and AI in detail and with live demos.

Gayle Fink & Tim Vlamis
Vlamis Software Solutions

How many Bowie State University (BSU) bachelor’s degree graduates proceed to post-grad study within 7 years? How does this vary by field of study? Do social work undergrads proceed from a bachelor’s to a master’s degree? What analytics can BSU use to attract students to its undergraduate programs? These are just some of the questions that BSU wanted to answer. Vlamis Software Solutions combined BSU student data with National Student Clearinghouse data to analyze post-grad outcomes. The data were loaded into tables in Oracle’s Autonomous Data Warehouse using ADW Tools (seen as “Database Actions”) and analyzed using Oracle Analytics Cloud (OAC) as a multi-table dataset. Come see how BSU uses Oracle’s toolset to analyze postgrad outcomes.

Tim German, Qubix

With the 21c release, customers have the option to run the same version of Essbase that runs in the Oracle Cloud – along with all its formerly “cloud only” features – on on-premises servers or those of other cloud infrastructure providers. But there are many subtle and not-so-subtle differences between an “independent deployment” of Essbase 21c and previous on-prem versions, spanning interfaces, integrations, automation, architecture and infrastructure. In this session, we’ll talk about real-world on-premises implementations of both Linux and Windows versions of 21c.

Spatial and Machine Learning

Jim Czuprynski, Zero Defect Computing, Inc.   

Think finding a close parking space is a challenge? Finding the closest charging station for your EV when you’re running short on battery power will be the next nightmare for drivers in Smart Cities. I’ll show how to use existing Oracle tools – including APEX’s Native Map region and built-in spatial analytic functions – to find the closest charge point while driving, as well as determine where it makes the most sense to place charge points to benefit utility customers.

Olivier Dubois, Robin Smith, Igor Vieira, Oscars S.A.

Fourteen million flights are expected by 2035 in Europe alone. According to Eurocontrol (European Organization for the Safety of Air Navigation), unless new solutions are implemented by increasingly capacity-constrained hubs, 1.5 million flights will not be accommodated by 2040, resulting in 160 million passengers stranded or delayed, and more time wasted in the air and on the ground, with extra ecological costs.

In partnership with the European Space Agency, OSCARS provides location-intelligent solutions through its Geo-Intelligent Platform (GIP). With integrated Machine Learning, GIP can successfully forecast spatiotemporal events and thus provide the various airport personas with on-time performance predictions, ensuring that arrival delays are positively transformed into on-time departures. Advanced deep learning architectures delivered on Oracle Cloud, that integrate Oracle Spatial, Stream Analytics, Middleware and Goldengate technologies, make this possible.

Ken Korsmit, Spatial Eye

The global energy transition from fossil fuels to zero-carbon is driving electric transmission and distribution companies to improve their understanding of the state of their energy networks – past, current, and future.

In the past, energy networks changed slowly over time, were not switched often and were feeding downstream only. This is all changing because of the energy transition.

Load flow calculation and network planning, once a yearly activity, is now changing into a monthly, weekly, or even daily activity of mission critical importance, eventually arriving at the digital twin. Calculating peak loads, or analyzing touch safety scenarios cannot happen without accurate data. As usage of network connectivity data increases, the current practice of point-to-point integrations and poor low voltage data quality is no longer acceptable. Therefore, a systematic approach has been developed to bring network connectivity to all areas of the enterprise.

Using examples from recent customer implementations in Australia and the Netherlands, we will discuss how Spatial Eye’s Warehouse application leverages Oracle Spatial to persist utility network connectivity data in an energy network model that reflects changes over time and can be consumed by power flow applications to calculate suitability of local power generation, network safety and outage sensitivity. Other applications include outage compensation, the computation of business KPIs (SAIDI, SAIFI and CAIDI) and network extension design.

David Lapp, Oracle

Spatial information is hugely valuable in the arena of artificial intelligence (AI), for example machine learning (ML) for home value predictions from county-level data, and deep learning for detecting changes in land cover from satellite imagery. Oracle Spatial is able to play a key role by supporting spatial data preparation, management, and analysis in concert with the growing ecosystem of spatial AI libraries. In this session, we will provide an overview of the role of spatial information in AI. We will also provide examples using spatial features of Oracle Autonomous Database and popular Python libraries to make predictions from a historical dataset of nationwide traffic accidents.

Data Lakehouse

Mike Matthews, Oracle

Data Lakehouse implementations can require a lot of software, and a lot of expertise. But did you know you can simplify your architecture, and improve your agility and data governance, by using the tools that are built-in to the Oracle Autonomous Database? The database provides tools for loading data, transforming and preparing data for analytics, finding data sets both in the database and in the wider data lake, and exploring and analyzing your data. Join this webinar to see what these tools can do and to learn about Oracle’s plans for them.

Dr. Holger Friedrich

There Is lots of talk about Data Lakehouses these days. What are the differences but also the similarities between Data Lake- and Warehouses? And how came the new term about to be in the first place? These questions are discussed and answered in this talk. Afterwards, the talk moves on to Oracle’s claim of offering the best product and service stack for implementing Data Lakehouses. An example OCI-based Lakehouse solution, combining different products like Object Storage, Autonomous Database, and Data Catalogue is discussed. Comparisons to competitors are made. This way, the talk will provide an introduction to Data Lakehouses as well as deeper discussion of Oracle’s offering, thus providing helpful insights regarding data strategy, architecture, modelling, and engineering.

Nitin Vengurlekar, Oracle

The goal of this session is to showcase how organizations can achieve faster Mean Time to Analysis and Decision (MTTA/MTTD) as they transition and pivot to embracing digital transformation. We will explore how State & Local agencies used data flow pipelines to harvest and transform Medicare or Criminology data sets, with key focus on how data sets become available/accessible in the Data Lakehouse (Autonomous Data Warehouse) using tight integration between the data catalog, Object Storage and Data Integration, thereby improving overall data ingestion and eliminating the need for data copy contagion. Finally, we will demonstrate AI/ML Speech and Vision capabilities, along with Oracle Analytics out-of-the-box features like Natural Language Query (NLQ), Natural Language Generation (NLG), Auto Insights which provide rich Prescriptive/Descriptive analytics. – ‘Secure access to trustworthy data, as and when needed while enabling participation in data experience.”

Alexey Filanovskiy, Oracle

Lakehouses became a new reality for many customers who wants to maximize business output from their data capital. Oracle proudly offers wide range of products and database features to help customers to implement all-data strategy. At this session we will recap Autonomous Database capabilities for Lakehouses/Data Lakes, talk about upcoming features. Also, Oracle Product Managers will talk about best practices if building Lakehouses on Autonomous Database foundation

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