Fun Facts about Fraud: #1 fraud detection method is a tip, 33% of business failures are due to theft and fraud, the median time for fraud detection is 18 months and 49% of victims do not recover their losses.  With such a significant cost, why can’t companies better combat against fraud? Oracle’s machine learning and advanced analytical capabilities transform your Oracle data management platforms into powerful fraud detection and prevention solutions. Using ML algorithms specifically designed to detect flag and predict rare events companies are turning the tide against this scourge and building applications to automate its detection.  Come see Oracle’s machine learning functions and how they can help you fight crime.

Imagine a world that anticipates your every move.  Datafication, smart phones, Twitter, Facebook, GPS and IoT, produce a digital exhaust that tracks your every movement, relationships and activities. Now, companies know everything about you and can anticipate your future.  Peter Tucker, author of “The Naked Future” paints a vivid picture of a present and “very near future” where machine learning and “cognitive” technologies can analyze gigabytes of data and even predict your future. Oracle’s Big Data, Machine Learning, Notebooks, Clouds and “predictive” Applications make this “naked future” more real today than you may realize.  Come hear about a future made possible by Oracle’s Data Management and Machine Learning technologies.

Most data science projects begin with data, “tools” and scripts but fail to get beyond the data scientist. They hit a wall when attempting to “operationalize” the models.  Netflix never implemented the algorithm that won the Netflix $1 Million Challenge.  This dichotomy between enterprise and algorithms is eliminated when algorithms are built into the data management platforms.  By “moving algorithms to the data”, Oracle Database and Big Data Clouds are now data management and advanced analytical platforms.   Developers use SQL, R and Oracle Data Miner UI to build, evaluate and deploy advanced analytical methodologies.  See how to go beyond “tools” to applications.  Several Oracle “predictive” Applications will be shown as examples.

  Developers often need their DBA’s to provide quicker identification and analysis on sub-optimal SQLs during load test cycles and peak production usage of the application. This becomes crucial, especially when the load test cycles are denser and there are relatively huge number of SQL statements in the entire application. Effective performance management and diagnosis…

AWR collects and persists thousands of performance metrics every hour; the problem is: the root causes of performance anomalies are difficult to detect and it is difficult to know which metrics/attributes are important for DBA’s to focus on to inform their root cause analysis and solutions. A full understanding of these thousands of metrics is impossible, thus many DBA’s (and even the off-the shelf tools) monitor only a standard set of well-known metrics. This “small model” approach may cause you to miss important system behavior or configuration that is relevant to the root cause of the performance problem. The presenters approach to this “small model” meta problem is to massively expand and dynamically extract only the relevant performance metrics. This paradigm shift is achieved by normalizing the data and looking across a wide array of AWR performance metrics gathered during the problem interval. Querying across the normalized data and using statistical analysis, this approach flags unusual trends. By targeting the right metrics at the right time, you “bundle” relevant results which results in event focused and actionable intelligence on the performance issue, and a richer insight into possible solutions.

ADDM* regularly runs behind the scenes and persists various performance tuning solutions that often go unnoticed by the DBA. Since DB performance problems can escalate quickly, the use of these persisted solutions can transform the DBA’s ability to skillfully devise a tuning action plan. To leverage these preexisting solutions, the presenter “reverse engineered” the advisor framework and developed some SQL queries that will help the DBA evaluate the solutions and extract the details and/or SQL needed to implement the solutions. This presentation takes a deep dive into the various ADDM tools, and details these custom queries against ADDM base data. Every DBA will leave this presentation with more confidence, new tools and approaches for solving Oracle database application performance problems. *ADDM (Automated Performance Diagnostics and Monitoring) is a key component in Oracle’s implementation of a self-managing database [part of the Diagnostics and Tuning pack license]. Within the persisted ADDM data, one will find solutions from automatic runs of the SQL Tuning Advisor , Segment Tuning Advisor, hourly ADDM runs, and more.

Need to manage the performance of a consolidated Oracle RAC database cluster? Need to get notifications of DB response time abnormalities such as a switched execution plan? Need to manage rank workloads and ensure the most business critical are meeting SLAs? This session is for you as it will present the QoS Management functionality built into Enterprise…

In this session, you will learn how to upgrade your existing OEM environment using a two system out of place strategy.  We will go over the details of how TDS Telecom successfully migrated our existing OEM test and production environments into a single new 13c environment. During the migration our existing 12c environments and the…

Run RAC? Do you have multiple RAC clusters and want to learn what's new with the technology? Do you want to control application resources from your RAC system? This session covers 10 new technologies in the latest version of RAC. Included are updates to how you patch, monitor and even deploy RAC on your servers. The session also covers storage changes to RAC. Learn how the latest generation of RAC includes an Autonomous Health Framework for improving the reliability of RAC clusters.

The Oracle Autonomous Health Framework (AHF) is a major enhancement in ensuring database performance and availability introduced with the Oracle Database 12c Rel. 2. This session will explain and demonstrate how incorporating applied machine learning techniques in Oracle Cluster Health Advisor, Hang Manager and Trace File Analyzer, autonomously and automatically preserves availability SLAs especially in consolidated or cloud database deployments.