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Data streaming platforms bring together analysis of information, but more importantly, they are able to integrate data between different sources (Myers, 2016). IBM streams for example is an analytics platform that enables the applications developed by users to gather, analyze and correlate information that comes to them from a variety of ...
Time series data provides a historical context to the analysis typically associated with complex event processing. This can apply to any vertical industry such as finance [14] and cooperatively with other technologies such as BPM. The ideal case for CEP analysis is to view historical time series and real-time streaming data as a single time ...
The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams, one for stock orders, and one for the resulting stock trades.
Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato , New Zealand .
Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
Real-time analysis in social media is an important tool when trying to understand the public's perception of a certain topic as it unfolding to allow for reaction or an immediate change in course. In near real-time analysis, we assume that data is ingested into the tool at a rate that is less than real-time.
One example is to choose the order of two successive join operators. In DBMS this decision is mostly done by certain statistics of the involved databases. But, since the data of a data streams is unknown in advance, there are no such statistics in a DSMS. However, it is possible to observe a data stream for a certain time to obtain some statistics.
Microsoft Azure Stream Analytics is a serverless scalable complex event processing engine by Microsoft that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, web sites, social media, and other applications. [1]
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