Analytics and Data Summit 2018

Learn the fast and way to perform machine learning using SQL Developer 17.2’s Oracle Data Miner drag and drop workflow UI. Learn from multiple experts in the room who will provide 1:1 coaching, guidance and instruction as requested in performing several hands on tutorials. Ask questions, get answers and come away with a comfort level of using Oracle’s machine learning capabilities in your use cases.
Also, try the new Oracle Machine Learning (OML) Zeppelin-based notebook for the Autonomous Data Warehouse Cloud (ADWC). OML extends Oracle’s offerings for data science in the cloud. OML’s collaborative, notebook environment helps data scientists built, share, document and automate data analysis methodologies that run in ADWC. Come learn about how easy it is to use Oracle’s newest SQL notebook technology to access and view data and build and deploy predictive machine learning models.

Oracle Database 12c now includes property graphs - a powerful feature that goes beyond relational, to analyze relationships in big data, such as finding the most influential people in a social network, discovering patterns of fraud within financial transaction data, or recommending products based on what similar customers are buying.
Don’t have the slightest clue on how to get started with and use property graphs? Good news! If you can form a complete sentence (coffee allowed) then you can do this! This hands on lab by Vlamis will show you exactly how to create, load and use property graphs on the Oracle Database. We will cover how to create a property graph using Java or PLSQL, how to load data into a property graph from existing database tables, how to create an in-memory instance of a property graph, how to run algorithms such as page rank against a property graph, how to use PGQL (a sql like language for property graphs) to query a graph and ways to visualize graph data.

In order to complete the hands on lab you will need to bring your own laptop with VNC Viewer installed (https://www.realvnc.com/en/connect/download/viewer/) so you can access the virtual machines we have set up for the lab.

Oracle R Enterprise (ORE), a component of the Oracle Advanced Analytics Option, provides a comprehensive, database-centric environment for end-to-end analytical processes in R, enabling immediate deployment to production environments. Entire R scripts can be operationalized in production applications - eliminating the need to port R code. Using ORE with Oracle Database enables R users to transparently analyze and manipulate data in Oracle Database thereby eliminating memory constraints imposed by client R engines. In this hands-on lab, attendees will work with the latest Oracle R Enterprise software, getting experience with the transparency layer, machine learning algorithms, and embedded R execution. Working with the instructors through scripted scenarios, users will understand how R is used in combination with Oracle Database to analyze big data.

Organizations are capturing data in a variety of data stores including Oracle Database, NoSQL, and Hadoop. Oracle Big Data SQL allows you to use Oracle SQL and security policies to seamlessly query across these stores. This means that existing Oracle Database query applications—without changes—will now be able to take advantage of this new information. This hands-on lab introduces you to how this is achieved: providing access to big data sources, implementing unified security, and delivering analyses that combine and correlate all data.

Meetup.com is a social media platform for people interested in the same subject to gather in groups, also to plan, and organize meetings in order to connect with meetup group members for the purpose of exchanging knowledge, discussing subjects and form new networks. All data related to meetup groups and their members, the events they organize and speakers at events as well as comments regarding events can perfectly representated as a property graph. This gives the opportunity to use graph algorithms to analyze these data. 
The purpose of this presentation is to show how to build a graph from Meetup.com data and how to use Oracle´s Property Graph In-Memory Analytics Framework to analyze the graph in order to (1) find influencers for certain topics in different locations or (2) identify hot topics people are interested in most. The HOL is "tell & show" using BDL VM 4.11 as environment, RStudio for data wrangling and Big Data Spatial and Graph with Oracle NoSQL DB as data layer. Scripts will be uploaded to and available via Github.

Real life Manufacturing Data is not like iris or titanic. We cannot just .fit() and .score(). We need to effectively .transform() the data. In this presentation, we will look at the complexity of manufacturing data and various ways to feature engineer manufacturing data to derive meaningful variables to solve manufacturing problems. Specifically we will look at a. Methods to construct machine learning data set for different type of Manufacturing like Lot Controlled, Serial Controlled, Batch Manufacturing, Discrete Manufacturing b. Transformation method of time series data from Machine Sensor Readings c. Various binning transformations on transaction data