Top 18 Open Source and Commercial Stream Analytics Platforms - Predictive Analytics Today

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Stream Analytics helps to develop and deploy solutions to gain real time insights from devices, sensors, and applications by real time stream processing in the cloud. Stream Analytics enables to perform real time analytics for Internet of Things solutions, stream millions of events per second, provide mission critical reliability and performance, also deliver real time dashboards and alerts over data from devices and applications, correlate across multiple streams of data and use SQL based language for development. Stream Analytics customers deploy and monitor streaming jobs. Applications of stream analytics includes personalized, real-time stock-trading analysis and alerts offered by financial services companies, real-time fraud detection; data and identity protection services, analysis of data generated by sensors and actuators, web clickstream analytics, customer relationship management (CRM) alerts, supply chain alerts, transportation alerts. Apache Flink is an open source platform for distributed stream and batch data processing.


Big Data: Applying Machine Learning to Event Processing - RTInsights

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How do you combine historical Big Data with machine learning for real-time analytics? TIBCO outlines an approach, use cases, and tools of the trade. "Big Data" has gained a lot of momentum recently. Vast amounts of operational data are collected and stored in Hadoop and other platforms on which historical analysis is conducted. Business intelligence tools and distributed statistical computing are used to find new patterns in this data and gain new insights and knowledge for a variety of use cases: promotions, up- and cross-sell campaigns, improved customer experience, or fraud detection.


A methodology for solving problems with DataScience for Internet of Things - Part One

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Real-time systems differ in the way they perform analytics. Specifically, Real-time systems perform analytics on short time windows for Data Streams. Hence, the scope of Real Time analytics is a'window' which typically comprises of the last few time slots. Making Predictions on Real Time Data streams involves building an Offline model and applying it to a stream. Models incorporate one or more machine learning algorithms which are trained using the training Data.


A methodology for solving problems with DataScience for Internet of Things - Part One

@machinelearnbot

Real-time systems differ in the way they perform analytics. Specifically, Real-time systems perform analytics on short time windows for Data Streams. Hence, the scope of Real Time analytics is a'window' which typically comprises of the last few time slots. Making Predictions on Real Time Data streams involves building an Offline model and applying it to a stream. Models incorporate one or more machine learning algorithms which are trained using the training Data.


A methodology for solving problems with DataScience for Internet of Things - Part One

@machinelearnbot

Real-time systems differ in the way they perform analytics. Specifically, Real-time systems perform analytics on short time windows for Data Streams. Hence, the scope of Real Time analytics is a'window' which typically comprises of the last few time slots. Making Predictions on Real Time Data streams involves building an Offline model and applying it to a stream. Models incorporate one or more machine learning algorithms which are trained using the training Data.