—Spatial databases manage, store, and query data with a location element. Oracle Database is a multimodel database that supports simple geometries such as points, lines, and polygons, and complex structures such as 3D objects, topological coverages, linear networks, and raster and gridded data, with scalability, security, and performance. Oracle technologies, including a new self-service map-based tool, make it easy to include location in analytics, mobile, asset management, and service applications. Built-in SQL, REST, Java, and JavaScript APIs with JSON let you analyze addresses, GPS coordinates, coverage areas, territories and other geographic information. Learn all about Oracle’s rich set of spatial data management and development features in this session.
Speakers: Siva Ravada and David Lapp, Oracle [TRN4756] (PDF 4.74MB)

—Node.js provides a highly scalable JavaScript runtime that is well suited for the development of event-driven applications and RESTful services. With Oracle Spatial and Graph, Oracle Database provides a high-performance platform for performing proximity, containment, tracking, geofencing, and many more location-based operations. To streamline the integration of Node.js with basic as well as advanced features of Oracle Database, Oracle maintains the open source node-oracledb driver. This session provides details and demonstrates the integration Oracle Spatial and Graph with the Node.js environment, using the node-oracledb driver to create event-driven spatial applications and RESTful location-based services.
Speakers: Siva Ravada and David Lapp, Oracle [DEV3108] (PDF 2.06MB)

—Python has become an extremely popular scripting language for working with geospatial data. A rich ecosystem of Python geospatial libraries provides algorithms to answer questions such as “How are outcomes correlated with location?” “What areas are the most or least similar?” and “Where are the hotspots?” Using a dataset of nationwide traffic accident data, this presentation shows you how to address these questions in a Jupyter Notebook by combining the spatial features of popular Python libraries and Oracle Database. The open source cx_Oracle Python module will be highlighted as it greatly simplifies the Python/Oracle integration.
Speaker: David Lapp, Oracle [DEV6406] (PDF 1.73MB)

—See how the new visual Spatial Studio tool lets you quickly and easily do spatial analysis and create maps - without coding or in depth spatial knowledge. We'll also show features of interest to developers.
Speaker: David Lapp, Oracle [MTE6768] (PDF 2.56MB)

—As one of the premier outdoor and out-of-home media organizations in the world, OUTFRONT Media serves America’s largest companies with complex marketing and media planning needs. Learn how the OUTFRONT’s analytics team migrated from Oracle Business Intelligence Cloud Service to Oracle Analytics Cloud with an Oracle Autonomous Data Warehouse backend. Hear what lessons were learned and what best practices to follow for Oracle Autonomous Data Warehouse and Oracle Analytics Cloud integration and see how sales analytics and location analysis, using the new Spatial Studio tool, are leading the organization to new analytics insights. Learn how a small team produced gigantic results in a short time.
Speakers: Tim Vlamis and Dan Vlamis, Vlamis Software Solutions; Derek Hayden and Scott Searcy, OUTFRONT Media [CAS3783] (PDF 2.14MB)

—Graph databases and graph analysis are powerful new tools that employ advanced algorithms to explore and discover relationships in social networks, IoT, big data, data warehouses, and complex transaction data for applications such as fraud detection in banking, customer 360, public safety, and manufacturing. Using a data model designed to represent linked and connected data, graphs simplify the detection of anomalies, the identification of communities, the understanding of who or what is the most connected, and where there are common or unnatural patterns in data. In this session learn about Oracle’s graph database and analytic technologies for Oracle Cloud, Oracle Database, and big data including new visualization tools, PGX analytics, and query language.
Speakers: Jean Ihm and Melliyal Annamalai, Oracle [TRN4755] (PDF 2.75MB)

—Graph is an emerging data model for analyzing data. Graphs enable navigation of large and complex data warehouses and intuitive detection of complex relationships for new insights into your data. Powerful algorithms for Graph models such as ranking, centrality, community identification, and path-finding routines support fraud detection, recommendation engines, social network analysis, and more. In this session, learn how to load a graph; insert nodes and edges with Graph APIs; and traverse a graph to find connections and do high-performance Graph analysis with PGQL, a SQL-like graph query language. Also learn how to use visualization tools to work with Graph data.
Speaker: Melliyal Annamalai, Oracle [TUT4328] (PDF 4.19MB)

—This session is aimed at the nonexpert: somebody who wants to know how it works so they can ask the technical experts to apply it in new ways to generate new kinds of value for the business. Look behind the curtain to see how graph analytics works. Learn how it enables use cases, from giving directions in your car, to telling the tax authorities if your business partner’s first cousin is conspiring to cheat on payments.
Speaker: Peter Jeffcock, Oracle [CON5503] (PDF 31.99MB)

—Paysafe provides simple and secure payment solutions to businesses of all sizes around the world, processing billions of payment dollars a year. This, combined with the focus of flawless customer experience and real-time money transfer, makes it a candidate for the “dark side” of the payments industry: fraudsters, money launderers, etc. With traditional data storage techniques such as relational technologies, it is almost impossible to see beyond individual accounts to the connections between them. In this session see how Paysafe implemented the property graph technologies in Oracle Spatial and Graph and Oracle Database, including its fast, built-in, in-memory graph analytics, to perform fast graph queries that identify patterns of fraud.
Speakers: Yavor Ivanov, Stanka Dalekova, and Dobroslav Hristov, Paysafe [CAS2710] (PDF 1.59MB)

—Despite having access to volumes of big data, most of the time investigators fail to uncover hidden relationships and patterns between entities. Financial institutions are now taking note of graph technology, which allows a natural and expressive representation of a case. Graph representation of financial crime data allows investigators to navigate and explore case data visually and intuitively. Combining deep learning (AI) and graph analytics boosts investigator productivity even further with automated case decision-making. Attend this session to learn more.
Speaker: Allen Sellars, Oracle [CON6222] (PDF 2.62MB)

—Oracle Graph Cloud is a new service that offers low-code, highly automated services for data scientists and developers to simplify the creation and management of graphs and allow for powerful graph analysis of database, data warehouse, and data lake content. Unlike other offerings, it includes a comprehensive library of graph analytic algorithms, workflow templates, and notebooks in addition to rich visualization and powerful graph query and traversal capabilities. This session uses healthcare, finance, and other datasets to explore how to access database schema, use automated tools to create graph models, and perform a range of graph analyses to reveal relationships and perform queries using Property Graph Query Language (PGQL) and PGX graph analytics.
Speakers: Jayant Sharma and Korbi Schmid, Oracle [TRN4754] (PDF 2.65MB)

—PGQL and Gremlin are two open source languages for querying and traversing property graphs. Although the two languages look very different on the surface, there are many parallels. This session introduces both languages, shows how common queries can be expressed in either language, and shows how to use both languages with Oracle Spatial and Graph. Finally, it shows how users can compile many of their existing Gremlin queries into PGQL queries.
Speakers: Sungpack Hong, Martin Sevenich and Oscar Van Rest, Oracle [DEV4084] (PDF 2.99MB)

—A key challenge in graph querying and pattern matching is processing increasingly large graphs and queries that don’t fit in a single machine’s memory. This session presents Oracle Labs’ novel technology PGX.D/Async, which can be used with Oracle Big Data Spatial and Graph to perform blazing-fast scalable distributed pattern matching for property graphs. In the session’s first part, developers will learn PGQL, a SQL-like graph query language, and the secret behind how top performance is achieved with asynchronous almost-depth-first traversal, allowing for high parallelism and precise control over memory consumption. The second part illustrates a performance analysis showing how PGX.D/Async performs 100x as fast as Spark and handles cases that Spark cannot.
Speaker: Sungpack Hong, Oracle [DEV3712] (PDF 1.26MB)