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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 ...
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...
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 ...
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 ...
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.
As a consequence, data in CMS Pipelines is handled in record mode. For text files, a record holds one line of text. In general, CMS Pipelines does not buffer the data but passes records of data in a lock-step fashion from one program to the next. This ensures a deterministic flow of data through a network of interconnected pipelines.
Pipelining cannot decrease the processing time required for a single task. The advantage of pipelining is that it increases the throughput of the system when processing a stream of tasks. Applying too many pipelined functions can lead to increased latency - that is, the time required for a single task to propagate through the full pipe is ...
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.