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Save the Date: February 15-17, 2022

Fall Analytics+ Day Replay
Fall Graph Day Replay
Fall Machine Learning Day Replay

Join us for three days of the best analytics and data presentations on the web.

Feb 15-17, 2022

Join us for three days of dynamic demo presentations. Level up your knowledge. Watch the leaders in analytics and data work their magic on Analytics, Machine Learning, and Graph. Ask questions, get real answers, and watch the solutions, all from the comfort of your desk. We’re doing demos! Attend one session or all three days, we look forward to seeing you.

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Fall TechCast Demo Days

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

October 12th
Analytics+ Day

Dan Vlamis & Edelweiss Kammermann

*Session start times are subject to change.

11am ET
Using OAC for a Marketing Analytics Engine
Amsive and Vlamis

11:40am ET – Break

11:45am ET
Fusion Analytics Warehouse in a Nutshell
Edelweiss Kammermann

12:30pm ET – Break

12:30pm ET
Reporting and Analytics Decision Tree for Oracle Cloud Applications
Shyam Nath

1:15pm ET – Break

1:20pm ET
ADW & Snowflake, which is more suitable for analytics?
Holger Friedrich

October 13th
Graph Day

Melli Annamalai, Oracle
Roger Cressey, Qubix

*Session start times are subject to change.

11am ET
Graphing Grifters: Identify & Display Patterns of Corruption With Oracle Graph
Jim Czuprynski, Zero Defect Computing Inc.

11:20am ET
Use Graph Analytics for Product Recommendations with Oracle Autonomous Database
Melli Annamalai, Oracle

11:45am ET
Dependency Analysis of Legacy Applications with Oracle Graph
Stephan La Rocca, PITSS GmbH

12:15pm ET – Break

12:20pm ET
Ways to the Bacon: SQL vs PL/SQL vs PGQL with Graph Studio
Kim Berg Hansen

12:30pm ET
Modeling and Querying Complex Product Bills of Materials Using Graphs
Florian Siepe, Viessmann IT Service

1:00pm ET – Break

1:05pm ET
Integrating Data Silos with Linked Data and Oracle RDF Graph
Martien Vos, Redforce

1:35pm ET
Geographical Graphs: Graph, Meet Map!
Albert Godfrind, Oracle

October 14th
Machine Learning Day

Abi Giles-Haigh, Roger Cressey

*Session start times are subject to change.

Automating the machine learning modeling process
Mark Hornick


You have a Model, now what? Deploying ML solutions on Autonomous Database
Marcos Arancibia


Is it all about automation? Augmentation vs Automation within the Analytical Journey
Abi Giles-Haigh, Roger Cressey

Analytics+ Abstracts

“Amsive, its predecessor being DX Marketing, has a long history of providing marketing services to hundreds of clients through data-driven campaigns and advanced analytics.  Amsive and Oracle partner Vlamis Software Solutions are developing a next generation of a Marketing Analytics Engine (MAE), based on Autonomous Database Cloud Service (ADW), Oracle Data Integrator (ODI) and Oracle Analytics Cloud (OAC). As an early adopter of cloud-based data and analytics platforms, Come hear about their journey as they update their offerings to capitalize on the unique and newest capabilities of ADW. They will cover how they load and match data from multi-source enterprise datasets and leverage OAC for visualizations, including demonstrations of the modeling and machine learning capabilities possible in this enhanced environment.”

Oracle offers Fusion Analytics Warehouse as an analytic platform for Oracle Cloud ERP, HCM, SCM, and CX Cloud. Do you want to know how it works, how much prebuilt content is included, and if it is the right solution for you? Maybe you are wondering if you can customize the content or add new sources.

Have you moved from on-premises Enterprise Resource Planning (ERP) applications like eBusiness Suite or PeopleSoft to Oracle Cloud ERP? Or directly adopted Cloud Applications? Wondering how to handle the variety of your reporting needs including operational and analytical analysis. What about complex reports that need near real-time data or customer facing reports with logos and localizations, or pixel-perfect reports? Finally, what about adhoc analysis needs for the business analysts in the company.

Oracle Autonomous Data Warehouse and Snowflake are major contenders in the area of fully managed Cloud Data Warehouse services. They share many similarities regarding ease of use, low maintenance and their SQL API. But they also significantly different. Differences range from the fundamental architecture, with Snowflake running on immutable object storage and containers and ADW on Exadata, over available APIs, engines and features up to the Cloud platforms they are available on. In this presentation we are going to look at a number of requirements and important aspects that a Cloud DWH service has to provide, particularly for analytical workloads. We will discuss the abilities and maturity of the two services w.r.t. those analytic workload requirements. This will allow the attendees to evaluate which service fulfils their needs best and thus to choose between them. Live demos will illustrate and clarify specific points.

Graph Abstracts

Ever wonder how your bank knows when to text you when a potentially fraudulent transaction appears on its radar? It’s simple if you know how to apply the right algorithms to financial transactions so that outliers become readily apparent. These techniques can be applied to just about any anomalous behavior, and property graph technology helps make outliers stand out dramatically.

Through presentations and online demonstrations, this session will:

  1. Show how to use appropriate Oracle Graph algorithms already built into Oracle Database to uncover anomalous patterns
  2. Demonstrate Oracle Graph Studio’s capabilities to identify and display outliers more clearly
  3. Introduce the basic concepts of PGQL (Property Graph Query Language) to look at tabular data in completely new ways

Businesses and services often want to make better product recommendations to customers, such as which movies to watch, for a movie streaming service. Graph analysis is a powerful tool here, to increase customer stickiness and satisfaction with their service. This can now be done easily and quickly by database developers or analysts, even without expertise in graphs.

In this demo session, we will show step-by-step how to do this using the Graph Studio feature of Autonomous Database. Starting with database tables for customers and their movie watching histories, we will create a graph. Since a customer in a cluster is likely to enjoy movies liked by other customers in the same cluster, we will cluster users based on movies they have watched, using the WhomToFollow algorithm. We will also show how to start a notebook, and run graph queries from the notebook.

Speaker Bio:
Melli Annamalai is a distinguished product manager at Oracle. She has 20+ years experience in working with unstructured and semi-structured data in the database, including multimedia data, data in Apache Hadoop platforms, property graphs, and RDF knowledge graphs. Her current focus area is graph database and analytics, in particular on ways to make it easy for developers to use graphs in any application. She enjoys working with developers as they develop and deploy solutions to solve business problems, so that she can better tailor product enhancements to what they need. She is a regular presenter at industry events, Oracle User Group meetings and techcasts, and Oracle Developer events. Melli has a Ph.D. in Computer Science from Purdue University.

Modernizing existing legacy applications is a very complex technical undertaking. Existing fragments of software need to be analyzed, and implicit dependencies between objects, such as those from references in Oracle Database objects, need to be taken into account. You also need to consider dependencies related to implementation, such as those in external scripts, user interfaces, client software, etc., as well as dependencies from business processes and user interaction with the system. This results in a complex, very large, dynamic network of dependencies.

Graph technologies are well suited to modeling such networks, and performing analysis on them. We digitize all this information into our own application datacube. Using the graph features of Oracle Database, we can then analyze, cluster, and reassign components from the legacy application into groups, based on a design pattern called “bounded context”, in order to rearchitect the legacy application into new software frameworks. In this session, we will cover the principles, concepts, ideas and results of this approach.

Speaker Bio
Stephan La Rocca is Director Business Development for PITSS GmbH, and has been working for over 20 years in the Oracle Software Development and Business Intelligence ecosystem. He has been a speaker at various conferences (DOAG, SiOUG) and webcasts. Together with the University of Ulm, he is responsible for a founded Project of the German Government focusing on retrograde analysis of Business Processes in Legacy Applications.

Six Degrees of Kevin Bacon is about linking actors by movies where they have worked together. The Bacon Number of an actor is the number of links in the shortest path from the actor to Kevin Bacon.

Calculation of the Bacon number is an interesting exercise (like given as a lab at Oberlin College) and of course we can do it in Oracle. But simple implementations might easily become excessively resource consuming for the database – we might need to do things like early pruning to get acceptable performance on larger datasets.

This talk investigates different Bacon calculation methods with SQL and PL/SQL as well as graph methods (remember that Graph option is now no-cost in supported database versions.) These methods can be useful for other real sets of data besides movie data.

Speaker Bio

Kim Berg Hansen is a senior developer at Trivadis Denmark A/S, using Oracle SQL and PL/SQL since 2000. He specializes in doing it all in a single SQL statement wherever possible, utilizing the database to the max, and authoring SQL quizzes on Oracle DevGym. Kim has presented at ODTUG Kscope, UKOUG, DOAG, and OUGN conferences. Kim is Oracle ACE and Oracle OCE in SQL.

For product manufacturers, it is crucial in B2B e-commerce settings to give customers more insight into your products. Especially if you have a complex product catalog, with long-lasting products such as industrial durable goods, which can be composed of other products, enabling business customers to find the right information within a few clicks is a key factor for customer satisfaction.

Graphs are a useful way to model the complex relationships and dependencies among many different products and parts. To illustrate this, we construct a graph in a multi-catalog environment to model which “Part of”-relationships can be found in bills of materials (BoMs). The type of item is included in the graph: whether they are wear- or spare-parts, or just regular products. We can also perform useful queries such as finding nested spare- or wear-parts, and how a certain product or part has been used. In this example, we use Oracle Database Graph features to store, model, and analyze the BoM data set. We will also show how we use PGQL, a natural and powerful query language, to easily perform various analyses on the graph structures. Here PGQL – the query language used in PGX – here shows its advantages for handling these structures.

Speaker Bio
Florian Siepe, Software Engineer, Viessmann IT Service

Florian is a software engineer at the Viessmann IT Service. After finishing his bachelor’s degree in 2021, he is currently pursuing his Master studies at the University of Marburg (Germany).

His professional areas currently include full-stack development in a B2B e-commerce setting, while focusing on Java-based technologies such as Spring Boot or Quarkus, as well as Angular on the client side. During his bachelor studies, he gained familiarity with NoSQL database solutions and graph analytics technologies such as PGX.

Many organizations face the frustration that several existing data silos, each serving departmental operations, are needed to create a 360 view of their target object. Using data warehouse technology for such federated queries can be difficult. Most data are fetched from the source without ever being queried. Other challenges include uncontrollable data structure drift on the source side.

Linked Data can address this challenge, using the W3C standard technology of RDF Graph. Different data sources, which can contain different data types, such as asset descriptions or geographical information, can be linked in the same graph. Bringing in multiple data sources, different views on this data can be provided – depending on which application is accessing your data.

This session will feature a demo of this in action, using sewage management data from the Netherlands, developed by Redforce, a leading Dutch IT consultancy firm. They will show how Oracle technologies, including their mature RDF Graph Database features, were used in the solution.

Speaker Bio
Martien Vos is currently data architect at Redforce in the Netherlands. Martien has over 25 years experience in GIS, data migration and data integration. During the past 7 years Martien has been working intensively with Oracle RDF Graph, with a focus on RDF with the OGC GeoSPARQL standard. During the past years, Martien has developed a system for manipulating native RDF and Spatial data. This was done using Oracle APEX and Oracle JET in combination with OpenLayers.

The powerful algorithms of Oracle Property Graph combined with the flexibility of the PGQL language allow you to rapidly examine the relationships in your data. But as we all know, “a picture is worth a thousand words”. So viewing the results in a graphical way is important. This is provided by the GraphViz feature for on-premises development, and by the Graph Studio feature of Autonomous Database.

One little known capability of graph visualization is the ability to show a graph on a map. This applies well to graphs with a geographical location perspective: customers, banks, stores, airports, … It means you can highlight flows of money or goods, or flight routes on a global map.

This presentation will show how you can use this capability: enriching your data with locations, mapping locations in tables, and using GraphViz to display and interact with geographical graphs.

Speaker Bio
The session will be covered by Albert Godfrind from Oracle Corporation. Albert has over 25 years of experience in designing, developing, and deploying IT applications. His interest and enthusiasm for spatial information and geographical information systems started at Oracle when he started using the spatial extensions of the Oracle database in 1998. Ever since, Albert has been evangelizing the use of spatial information to GIS and BI communities across Europe, consulting with partners and customers, speaking at conferences, and designing and delivering in-depth technical training.

Albert is one of the authors of the first book on Oracle Spatial, “Pro Oracle Spatial – The essential guide to developing spatially enabled business applications”.

Machine Learning Abstracts

Using data to gain insights, make predictions, and identify patterns has become a priority for most enterprises. Machine learning is a key technology, but it often requires specific skill sets. Even for experts, the machine learning process can be highly iterative and involve significant trial and error. To help make machine learning more accessible to a broader set of users and enhance data scientist productivity, Oracle introduced automated machine learning, or AutoML, both from a Python API and a no-code user interface. In this session we introduce the machine learning process and how AutoML plays a key role in the modeling process. We’ll include a demonstration of the no-code OML AutoML UI and the OML4Py AutoML features.

One of the hardest problems to solve when working with machine learning applications is the deployment. Most failures of ML projects happen because of challenges on pushing models to production. Join us as we show you how Autonomous Database makes it simple build and deploy ML Applications. We will see several options for adding AI and ML into your projects when using Oracle Machine Learning that is available inside the Oracle Database. You will also learn about interfaces with SQL, PL/SQL, Python and REST APIs, and see live demos of the different components, as well as see a live demos of Cognitive Text capabilities.

Oracle are now providing new techniques and tools built within the Autonomous Data Warehouse to automate tasks, but what are they and how can they help us in our Analytical journey? In this session, we will demonstration Database data insights, with a use case and how it adds to our data understanding. Then moving into AutoML, how is it done and what does it map to in the traditional Oracle Machine Learning (OML) world. Finally, we will show how AutoML can complement our traditional Data Mining to boost our analytical journey.

Register Early for Fall 2021 TechCast Days

Oct 12th–14th

Oct 12th – Analytics+ Day

Oct 13th – Graph Day

Oct 14th – ML Day

Past TechCast Days