Predictive Analytics is one of the widely used flavours of Analytics. Nowadays, most of the customers want to leverage machine learning(ML) techniques to identify the likelihood of future outcomes based on historical data. To predict the future KPIs appropriate Machine learning Models require to be developed and used for predictive analytics. This blog is primarily focusing on how to implement machine learning with Oracle analytics to predict future KPIs and then perform analytics in Oracle Analytics Cloud(OAC) or Oracle Analytics Server(OAS). "Please do not use this blog to refer and validate Machine Learning concepts" We can implement ML either in Oracle Analytics Cloud/Oracle Analytics Server or in Oracle Database.

Taking aim at competitors including Amazon Aurora and Snowflake, Oracle has enhanced the MySQL HeatWave in-memory query accelerator in the Oracle Cloud's MySQL Database Service by leveraging advanced machine learning. But Oracle insists the improvements do not mean the MySQL Database Service is encroaching on its flagship Oracle Database. The company on August 10 rolled out MySQL Autopilot, a component of HeatWave that uses advanced machine learning techniques to accelerate query performance and scalability. MySQL HeatWave works with the MySQL Database Service in Oracle Cloud Infrastructure (OCI) to accelerate performance for analytics and mixed OLTP (online transaction processing) and OLAP (online analytical processing) workloads. Included with HeatWave at no extra charge, Autopilot automates aspects of achieving high query performance at scale including provisioning, data loading, query execution, and failure handling.

Oracle Database 21c, the new release of the longtime industry leading RDBMS, is currently available in the Oracle Cloud, where it can be deployed as a Virtual Machine DB System (for clusters and single instance) or a Bare Metal DB System (single instance). It will be available more broadly later this year, including on-premises. While Oracle Database 20c was scheduled for release in 2020, that release was cancelled because of the COVID-19 pandemic and its effect on customer IT departments. All of the features planned for 20c were rolled into 21c. There are several notable points to be made about Oracle Database 21c.

If you need a primer on the concept of what a'Converged Database' is, I reccommend the following: I know, it should be TL/DW; (too long, didn't watch). But just in case you're scanning this versus deep-reading, let me sum up. Instead of implementing multiple database technologies, one for each feature required (json documents, spatial, relational, media, text, etc.), you can instead take advantage of a single database management system that supports all of those, AND ALSO provides a single SQL interface. A critical component for making a converged database viable, is having a single set of libraries, drivers, and APIs across all of the different types of data and workloads. In addition to a single SQL engine, there is another another critical interface required: HTTP(S), or more specifically, REST via HTTP(S).

According to McKinsey's study "Visualizing the uses and potential impact of AI and other analytics", 2018, the estimated impact of artificial intelligence and other analytics on all industries regarding anomaly detection is between $1.0T and $1.4T. Anomaly detection is the critical success factor in predictive maintenance, which tries to anticipate when maintenance is required. This differs from the classical preventive approach, in which activities are planned on a regularly scheduled basis, or condition-based maintenance activities, in which assets are monitored through IoT sensors. Applying anomaly detection algorithms based on machine learning, it's possible to perform prognostics to estimate the condition of a system or a component and its remaining useful life (RUL), in order to predict an incoming failure. One of the most famous algorithms is the MSET-SPRT, well-described with a use case in this blog post: "Machine Learning Use Case: Real-Time Support for Engineered Systems."

In keeping with Oracle's mission to help people see data in new ways, discover insights, unlock endless possibilities, customers wishing to utilize the Machine Learning, Spatial and Graph features of Oracle Database are no longer required to purchase additional licenses. As of December 5, 2019, the Machine Learning (formerly known as Advanced Analytics), Spatial and Graph features of Oracle Database may be used for development and deployment purposes with all on-prem editions and Oracle Cloud Database Services. See the Oracle Database Licensing Information Manual (pdf) for more details. This latest announcement further enhances the benefits of Oracle's multi-model converged architecture by supporting multiple data types, data models (e.g. Developers and data scientists can use standard SQL interfaces and/or APIs with Oracle's Machine Learning functions, Graph analytics and Spatial operators to develop their models and applications.

Ever notice in soccer when a midfielder runs his or her legs off to get the ball to a striker who then taps it into the net and gets all the glory? Charlie Berger wants to help database administrators--the indefatigable midfielders of enterprise IT--get a little of that data science glory for themselves. They can do that, Berger says, by first realizing how important their data wrangling abilities and their SQL knowledge can be to the data science process. Then they can take it a step further by learning the basic data science practices and understanding how to wield the ample library of machine learning algorithms available to them right in the Oracle Database. "In my travels, I've discovered that it's easier to take the person who knows and likes SQL and teach them how to begin doing real machine learning in the database, than it is to take folks who do Python or R, and who know algorithms, and teach them to do machine learning inside the database," although both can benefit from the practice, says Berger, a senior director of product management for machine learning, AI and Cognitive Analytics at Oracle.

Talk Abstract: Oracle Database has different graph features: property and RDF graphs. And the RDF graph feature can be used either with JENA or with RDF4J. In this TechCast we will introduce the RDF4J Oracle Adapter and focus on the SPARQL query language API used in RDF4J. We will present some pitfalls encountered while developing the adapter. And we will end with a use case in which the SPARQL RDF4J on Oracle Database is used as part of the GraphSearch PoolParty Semantic Suite component.

If you can ask Apple Siri or Amazon Alexa to search databases for restaurants and obscure bands, why can't you ask them to search or update the databases of your enterprise resource planning (ERP) or human resources (HR) applications? This question, or something like it, crossed the minds of Jorge Rimblas and Christoph Ruepprich--both of whom are database developers and Oracle ACEs. They each took the initiative to answer the question and learned lessons along the way. "It's a great time to be a database developer," says Rimblas, who has worked with Oracle Database since 1995. "But you have to keep learning," he says.

Autonomous is quickly becoming one of the most talked about words in tech. A concept popularized by the automotive industry is quickly gaining traction in other areas, including datacenters. We're beginning to see the advent of autonomous databases that leverage machine learning to eliminate human labor and human error. In this new world, the database automatically patches, tunes, backs up and upgrades itself without intervention – all while the system remains up and running. The idea of a self-healing, evolving and autonomous software taking on human tasks – such as operating machinery – may be unsettling to some Sci-Fi enthusiasts, but others see the vast potential it will have when their jobs are freed from the mundane.