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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 ...
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.
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.
Data streaming is also modular, because systems components may be separated and recombined mainly for flexibility and variety. Data streaming works in different application versions and systems such as iOS. It is also possible to change the speed of data streaming. [9] A consequence of modularity is the creation of platforms.
In computing, a pipeline or data pipeline [1] is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines ...
For example, if you need to load data into two databases, you can run the loads in parallel (instead of loading into the first – and then replicating into the second). Sometimes processing must take place sequentially. For example, dimensional (reference) data are needed before one can get and validate the rows for main "fact" tables.
In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming instructions into a series of sequential steps (the eponymous "pipeline") performed by different processor units with different parts of instructions ...