Oracle OpenWorld and Code One Sessions
Speakers: Daniel Geringer and Siva Ravada, Oracle [TRN4095] (PDF 1.06MB)
—In this session learn how to build location analytics and map visualization into applications on the Oracle Cloud using popular developer tools and frameworks with the powerful spatial features of Oracle Database and big data technologies. Go through short examples that let you use sensor feeds, social media data, place names, and GPS coordinates to uncover location patterns, analyze relationships to regions of interest and exclusion zones, track moving objects, and build other useful location-related applications. These typify spatial usage in mainstream cloud development with RAD (REST, Oracle Application Express, Oracle Database), big data (Spark, Hadoop, NoSQL) and popular cloud scripting tools.
Speakers: Siva Ravada, Oracle [TRN4089] (PDF 3.23MB)
—Tracking K nearest neighbors from a large dataset to a given location is an expensive task. This session shows how to use Oracle Big Data Spatial and Graph’s Vector API to build a real-time Spark streaming application that constantly calculates the K nearest moving vehicles for every element from a set of static stations, based on the distance and user-defined business rules. The vehicle location data is continuously received from a stream that sends updated vehicle locations every few seconds so the nearest vehicles for each station are constantly updated and written to HDFS. The presentation also shows how to build a map visualization application to view this real-time tracking with the Oracle Maps API.
Speakers: Siva Ravada and Roberto Mercado Mariscal, Oracle [DEV5355] (PDF 3.52MB)
—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 by combining the geospatial features of popular Python libraries and Oracle Database.
Speaker: Jayant Sharma, Oracle [DEV5185] (YouTube Video 8 Minutes)
—Apache Kafka is a key component in enterprise data architecture today. Kafka manages streaming data sources such as IoT device feeds, clickstream data, and social media feeds. For applications that need to query streaming data and react in real time, Oracle SQL access to Kafka Streams gives SQL developers a simple and powerful way to query Kafka Streams and integrate streaming data with database data. This session walks through use cases involving Oracle Cloud services and shows step-by-step how to use Oracle SQL to query Kafka Streams, join Kafka Streams with database tables, and load high-speed streaming data into the database.
Speaker: Melliyal Annamalai, Oracle [TUT5454] (PDF 1.35MB)
—Graph analysis employs powerful algorithms to discover relationships in social networks, IoT, big data, data warehouses, and complex transaction data. The low-code, highly automated services for data scientists and developers in Oracle Graph Cloud Service will simplify the creation and management of graphs and allow for powerful graph analysis of database, data warehouse, and data lake content. In this session see an overview and demos of Oracle Graph Cloud Service as well as the other graph technologies for Oracle Cloud, Oracle Database, NoSQL, Spark, and Hadoop including PGX analytics and PGQL property graph query language.
Speakers: Jean Ihm, Korbi Schmid and Hans Viehmann, Oracle [TRN4098] (PDF 3.00MB)
—In this session gain new insights into your analytics data using graph analysis and database technologies. See a pipeline used to explore industry-specific datasets and analyze it with the power of graphs. Using retail, telco, and airline datasets, learn how to access database schema, use automated tools to create a graph model, and perform a range of graph analyses to reveal relationships and perform connectivity-based queries. Learn how to use Property Graph Query Language (PGQL) to traverse and query the graph to discover relationships and impacts; invoke PGX graph analytics to discover anomalies, influencers, and communities in data; and visualize the results as a graph in an interactive notebook environment.
Speakers: Jayant Sharma and Korbi Schmid, Oracle [TRN4099] (PDF 1.61MB)
—Graph is an emerging data model that enables fast and intuitive navigation of large and complex data via a graph query language. This session introduces applications that get benefits from graph query, such as finding out how two accounts are connected to each other in a big network of transaction data. Because there is no standard in graph query, the presentation discusses several existing graph query languages, including Oracle's Property Graph Query Language (PGQL), comparing and contrasting their syntax, semantics, and expressive powers. Also, it covers the execution performance and scalability of graph engines that support these queries.
Speaker: Oskar van Rest, Vlad Haprian and Sungpack Hong, Oracle [DEV5447] (PDF 1.83MB)
—Graph models and machine learning techniques are becoming popular for discovering relationships, classifying information, identifying patterns and anomalies in data, and improving understanding of information. By combining ML techniques such as sequenced-based learning with relationships in graphs, we can answer questions such as “How did other investigators approach similar cases?” “Does this malware have the same characteristics as prior attacks?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Using medical and financial data, this session shows how graphs and ML together can enable new insights that could not be attained previously.
Speakers: Sungpack Hong, Jinha Kim and Rhicheek Patra, Oracle [DEV5420] (PDF 2.58MB)
—Finding unexpected patterns and results in data is a critical factor in discovering fraud, adverse reactions, and other suspicious behaviors. Graph analysis is an effective data analysis methodology that considers fine-grained relationships among data entities. This session explains how to use graph analysis algorithms to discover anomalous results. During a live demo in this session, you’ll see anomaly detection in action within medical transactions from the United States Center for Medicare and Medicaid Services (CMS) for the year 2012. You’ll learn how to create a graph, apply graph algorithms (such as personalized PageRank), and discover anomalies and outliers by computing distances between records among the same or different categories.
Speakers: Sungpack Hong and Francisco Morales, Oracle [DEV5397] (PDF 2.77MB)
—This session covers how to use the Spring Framework, containers, serverless technologies, and the Tom Sawyer Perspectives SDK to rapidly develop and deploy autoscalable web applications. You will learn how to build an enterprise application to visualize and analyze big data, relational and NoSQL databases, and Oracle Big Data Spatial and Graph data. The discussion includes core Java APIs and technology for handling mission-critical workloads in the cloud or on premises.
Speaker: Kevin Madden, Tom Sawyer Software [DEV5479] (PDF 2.57MB)
—Blockchain technology and Bitcoin will potentially revolutionize business transactions. Blockchain offers a robust, decentralized platform for privacy and trust. It underlies the digital cryptocurrency Bitcoin, which can be exchanged freely and anonymously, without a central authority. Bitcoin transactions, recorded publicly, provide an invaluable dataset for gaining insight into the behavior of digital currency and the underlying blockchain technology. Blocks, transactions with potential multiple inputs and outputs, and the flow of bitcoins between addresses form a sophisticated real-time graph. This session details a pipeline used to gather Bitcoin transaction data and analyze it with powerful graph cloud database technologies.
Speakers: Julia Kindelsberger and Hans Viehmann, Oracle [DEV4835] (PDF 2.22MB)
—High-performing machine learning algorithms come packaged in Oracle Big Data Cloud Service and Oracle Big Data Cloud at Customer. In this session learn how to interface with data lakes and large datasets using the Oracle Big Data manager UI, which contains a zeppelin notebook interface. Discover how this allows for the use of SQL queries and the R language to manipulate data from an object store and HDFS, HIVE, and IMPALA tables, and it can also be used to create machine learning algorithms using Spark and graph computations using Oracle Big Data Spatial and Graph—all from one simple interface.
Speaker: Marcos Arancibia Coddou, Oracle [PRO4054] (PDF 4.62MB)