Analytics and Data Summit 2020

If you have geospatial data in Oracle Database, you also have a powerful development tool to create map visualizations with that data. In this session, we will provide an overview of the various visualization strategies and platforms our customers can use to visualize and explore their Oracle Spatial managed data sets.Learn how to effectively utilize the Spatial and Graph Map Visualization Component (formerly known as MapViewer) to query and serve your Oracle Spatial data in a variety of industry standard formats such as GeoJSON, Image Tiles, and Vector Tiles, as well as OGC standards-based services such as WMS. Examples and demos will show how to use various Open Source HTML5, WebGL mapping clients, and APIs to consume and visualize data services provided in the Map Visualization Component. We will also show how to use the HTML5 Map API provided by the Spatial and Graph Map Visualization component to digitize and modify your geometry data stored in Oracle Database.

Graph-powered Cyber-security intelligence has recently drawn a lot of attention with the emergence of the Cloud era. Cloud applications generate huge volume of logs which are essential for threat detection and investigation. While such massive volume of data enables to accurately detect multiple threats, the data heterogeneity introduced by multiple data sources makes it challenging to analyze data in the tabular data model. Graph data model bridges the limitation by connecting heterogeneous entities via relationships. In this talk, we present how graph technology enables us to build a smarter and deeper cyber-security intelligence. Specifically, we demonstrate how Oracle Labs PGX and Data Studio enable (a) SaaS Cloud Security team to do in-depth analysis of security alerts with visualization and threat hunting from multiple heterogeneous logs, and (b) ODC Moat team to develop an effective Invalid Traffic Detection model by leveraging features extracted from underlying graph.

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.

Learn how to architect a graph analytics application using Oracle graph technologies. In this presentation, we will cover topics such as: how to create graph data from standard database tables, how to load data efficiently into graph stores, when to use in memory versus in-database graph analytics, when to use different graph visualization tools, determining how much memory you need, and more. We will also highlight the latest features, including custom algorithms, partitioned graphs, and new query functions. This intermediate-level session assumes basic familiarity with graph concepts and features. For beginners to graphs, attending the session “Getting Started with Graphs” first is recommended.

PGQL (Property Graph Query Language) is an SQL-like language to query property graphs in Oracle’s graph products. Aligned with the emerging SQL/PGQ standard, PGQL allows for expressing complex graph patterns in an intuitive way. We will start with an introduction, walk through examples that illustrate how complex patterns can be represented in simple PGQL syntax, and show how PGQL queries can be used with built-in graph algorithms like PageRank. We will highlight additions to the language over the past twelve months, such as new syntax to create graphs from database tables.