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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 ...
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.
The previous algorithm describes the first attempt to approximate F 0 in the data stream by Flajolet and Martin. Their algorithm picks a random hash function which they assume to uniformly distribute the hash values in hash space. Bar-Yossef et al. in [10] introduced k-minimum value algorithm for determining number of distinct elements in data ...
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 ...
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.
A transaction is different from a stream because a stream only allows read-only operations, and transactions can do both read and write operations. This means in a stream, multiple users can read from the same piece of data, but they cannot both modify it. [4] A database must let only one transaction operate at a time to preserve data ...
Fraud detection & scoring – raw data is used as source data for an anti-fraud algorithm (data analysis techniques for fraud detection). For example, timestamps, cookie occurrences or analysis of data points are used within the scoring system to detect fraud or to make sure that a message receiver is not a bot (so-called Non-Human Traffic [ 5 ] ).
In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a streaming algorithm and the objective is, given a sequence of points, to construct a good clustering of the stream, using a small amount of memory and time.
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