Accelerating the Industrial Internet of Things
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Apama

Software AG
Apama
Apama
Apama
IoT Application
Installed
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Imagine how responsive your enterprise could be if you could glean real-time insights from all that big fast data—data streaming in from global markets, mobile devices, the Internet of Things (IoT), internal transactional systems and a myriad of other sources. You can be that event-driven enterprise by using Apama. Software AG's Apama Streaming Analytics—supporting predictive analytics—is the world’s #1 platform for streaming analytics and intelligent automated action on fast-moving big data. With Apama, you can analyze and act on high-volume business operations in real time.
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Apama Streaming Analytics is built on an in-memory architecture that enables real-time processing of extremely fast, large data volumes—orders of magnitude larger than traditional database-based IT architectures.

Data science teams build predictive models in whatever data mining tools they prefer to use, then use Predictive Analytics for Apama to load the models in the PMML format. This step takes a fraction of the time that model deployment ordinarily requires, and eliminates the need for manual coding, cross-checking and error correction. Apama allows for ingesting these models rapidly, making them instantly organic to the business process that they support, as defined by various Apama applications.

Apama then probes incoming event data from any device, social media stream or business system with extremely low latency against the imported predictive models for real-time scoring. Predictive Analytics for Apama analyzes this streaming data, which can also be enriched with historic and contextual data-at-rest where necessary, to identify business patterns that have happened or are likely to happen.

The platform’s visualizations and visual analytics for business users support both human-oriented and automated intelligent actions, alerts and notifications.
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Number of Case Studies1
PREDICTIVE MAINTENANCE
PREDICTIVE MAINTENANCE
Manufacturers rightly focus on improving profit margins and growing revenue. Attracting new customers, selling more products and lean practices can help. However, as equipment sophistication increases, so does the ability to monitor equipment. Manufacturers can now develop revenue from maintenance services. Preventive maintenance has its advantages but to really drive uptime and maintain service levels, predictive maintenance is needed. Seamless IoT and machine sensor data integration is critical as well as a low-latency messaging backbone for scalable, fast and reliable transport. Delivering potentially large quantities of data at sub-second speeds is key to downstream activities. webMethods Integration, featuring Universal Messaging, addresses this need with an enterprise-grade service bus for connectivity, messaging, transformation and security of machine data for advanced real-time analytics.
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