How to Apply Machine Learning to Event Processing
How do you combine historical Big Data with machine learning for real-time analytics? An approach is outlined with different software vendors, business use …
The ability to continually calculate statistical analytics within constant streams of multiple data coming from devices, sensors, websites, social media, and other applications.
How do you combine historical Big Data with machine learning for real-time analytics? An approach is outlined with different software vendors, business use …
How do you perform analytics on data streaming from edge devices? IBM discusses their latest project, Quarks, now an Apache incubator
Apache Spark offers fast speeds, integration with a variety of programming languages, and flexibility. But Spark vs. Hadoop MapReduce is not an either-or
Current approaches to complex event recognition have various tradeoffs. In this white paper, the benefits and limits of various CER approaches are discussed.
How an online retailer of stock media turned to a system to manage Big Data and enable real-time analytics on hardware, network traffic, and transactions.
A giant telescope is undertaking a 10-year survey of the universe in a hunt for dark matter. The experiment's success depends on real-time management of data …
The tiniest imperfection in a piece of copper wire can render it useless. To prevent such manufacturing errors, Schwering & Hasse hooked up sensors to its …
Getting a handle on Big Data using stream processing often involves getting data from disparate sources, then picking and choosing what you want to see. One …
For their annual conferences, Oracle needed to choose an integrated, end-to-end people management solution from among hundreds of disparate Internet of Things …
The 9th annual ACM International Conference on Distributed Event-Based Systems took place in Oslo, Norway from June 29 to July 3, 2015. Here, RTInsights …