Accelerating the Industrial Internet of Things
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Oracle Integrated Cloud

Oracle Integrated Cloud
Device Management Platform
January, 2014 (5 years ago)
January, 2014 (5 years ago)
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Maximize the value of your investments in SaaS and on-premises applications through a simple and powerful integration platform in the cloud.

Auto-Associated SaaS connections

An Environment pre-loaded with connections to all Oracle SaaS applications you have subscriptions to

Native SaaS Adaptors

Best-in-class SaaS adaptors to accelerate integration with your cloud assets

Secure On-Premises Integration

Secure and seamless integration with enterprise applications and systems in your data-center

Open Adapter SDK

Simple and extensible architecture for creation of new adapters


Intuitive Visual Designer – Mobile Ready

Rich Point and click browser-based designer to build integrations anywhere - on your computer or your favorite tablet

Oracle Recommends™

Smart Data Mapper provides accurate recommendations using Oracle Recommends™

Business User Friendly

Business friendly terminology combined with videos and tutorials for ease of use


Publish your integrations as an API for external consumption
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Number of Case Studies1
Oracle's ML (Machine Learning) Applied Toward Big Data Processing
Oracle's ML (Machine Learning) Applied Toward Big Data Processing
Cloud, competition, big data analytics and next-generation “predictive” applications are driving companies towards achieving new goals of delivering improved “actionable insights” and better outcomes. Traditional BI & Analytics approaches don’t deliver these detailed predictive insights and simply can’t satisfy the emerging customer expectations in this new world order created by big data and the cloud.Unfortunately, with big data, as the data grows and expands in the three V’s; velocity, volume andvariety (data types), new problems emerge. Data volumes grow and data becomes unmanageable and immovable. Scalability, security, and information latency become new issues. Dealing with unstructured data, sensor data and spatial data all introduce new data type complexities.Traditional data analysis typically starts with a representative sample or subset of the data that is exported to separate analytical servers and tools (SAS, R, Python, SPSS, etc.) that have been especially designed for statisticians and data scientists to analyze data. The analytics they perform range from simple descriptive statistical analysis to advanced, predictive and prescriptive analytics.
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