Analytics and Data Summit 2020

Graphs enable intuitive detection of complex relationships and navigation of connections for new insights into your data. Powerful algorithms for ranking, centrality, community detection and path finding are available to analyze data with graphs, supporting a variety of use cases such as fraud detection, recommendation engines, dependencies in manufacturing, and more. In this session, learn how to model a graph, load a graph, insert nodes and edges using graph APIs, query a graph using PGQL (Property Graph Query Language), analyze a graph using 50+ pre-built algorithms available in the Java API, and visualize a graph using a native web-based tool. We will walk through an example that provides an end-to-end view of how to build an application that uses graph analytics.

Want to learn how to easily create interactive maps and perform spatial analysis on your data? In this session, we’ll show you how Spatial Studio lets you quickly and easily do spatial analytics and create maps, even if you don’t have GIS or Spatial knowledge. Business users and non-GIS developers have a simple user interface to access the spatial features in Oracle Database. Spatial Studio simplifies the development of web applications, SQL analysis, and REST services using the full spectrum of spatial operators and functions in Oracle Spatial and Graph. We will show you how you can prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results. You will also learn how you can access spatial analyses results via REST and incorporate in applications and workflows.

Our client, one of the biggest private banks in Turkey, was encountering difficulties in analyzing Customer Monetary Relation between each other with conventional data analysis tools. Although they have almost all kind of data analysis tools, analyzing connected data like money transfers, cheque payments for both inbound and outbound directions was a big problem to see who is directly or indirectly connected to whom, is there any cyclic money transfers that can cause a part of fraud activity, or any other money transfers that can be a part of finance of terrorism activities. And their Data Management Division discovered that these are questions in the scope of Graph technologies. Since they are an important and long time partner of Oracle, they preferred to perform these analyses with Oracle Spatial & Graph in RDBMS 12c Exadata Cloud environment. The main reasons in choosing Oracle S&G included: In Memory Graph queries, algorithm execution advantages with PGX with PGQL, and less data transfer costs.

With the growing need for volumes of data required by ML and Knowledge Bases, copying/duplicating potentially Petabytes of data is a real problem. Working with data 'in situ' is fast becoming the only viable pattern for enterprises. Additionally, heterogenous data silos are a given for big enterprises and aren't going away. Finally, incorporating 3rd party taxonomies/ontologies/datasets for enrichment are yet another example of datasources that need to be incorporated and orchestrated. This suggests the need for a hybrid approach for Knowledge Management. With careful architecture, this is where Semantic Knowledge Graphs can really shine, and in particular Oracle's implementation of Semantic Graph technology. Further, moving to the cloud offers real long-term advantages.

Oracle Spatial Studio is a self-service web application that makes it easy for business users, analysts, and developers to create interactive maps and perform spatial analysis on their business data, from Oracle Database or other data sources. It provides a simple user interface to access the spatial features in Oracle Database. In this hands on lab session, we will walk through using Spatial Studio to prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results. You will learn how to access spatial analyses results via REST and incorporate in applications and workflows. Also, we’ll cover advanced topics for developers, such as managing complex 3D and GeoRaster data sets; visualizing 3D scenes, clusters and heat maps; and exploring data sets with huge data volumes in a high performant and highly interactive manner.

Learn the fast and easy way to do machine learning with Oracle SQL Developer 19c’ Oracle Data Miner drag-and-drop workflow UI. In this introductory hands-on lab, learn from several experts who will provide one-on-one coaching, guidance, and instruction as requested in various hands-on tutorials. Ask questions, get answers, and come away with a comfort level for using Oracle’s machine learning capabilities in your use cases.

We see a proliferation of data streams being generated by applications, devices, web, processes, etc. which need to be processed, stored, analyzed securely and at scale. There are many technologies to address these event streams, but the key is to recognize that event-driven action is the end goal of these event streams. Once can think of event-driven workflows in ERP, CRM, HCM that have event flows, traditionally using JMS messaging or streaming news, sensor data, social media posts to a varied set of subscribers with Apache Kafka.

Often there are tough architecture choices for setting up the infrastructure, operating it, tuning it, upgrading it, scaling it. In this talk we present the simplicity of setting up Transactional Event Queues in the Oracle database which brings the best of JMS messaging and Kafka produce/consume with transactions that combine data and events delivered with notifications, ideal to build scalable microservices with a multi-model, multi-tenant architecture

Python is hugely popular for data analysis, including geospatial data. The ecosystem of specialized geospatial Python libraries continues to grow and evolve, providing a powerful toolkit for developers to supplement the spatial features of Oracle Spatial and Graph. Spatial-temporal analyses uses specialized algorithms to answer questions such as 'Is the change over time in traffic accidents for an area related to the change over time in nearby areas?', and 'Are historical traffic accidents clustered in both location and time?'. Spatial-temporal analyses then uses these results to predict the likelihood of potential future outcomes. This session will cover the basics of using Python with Oracle Spatial and then demonstrate a spatial-temporal use case based on a historical dataset of nationwide traffic accidents.