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The OTB-Applications package makes available a set of simple software tools . It supports raster and vector data and integrates most of the already existing OTB applications. The architecture takes advantage of the streaming and multi-threading capabilities of the OTB pipeline. It also uses features such as processing on demand and automagic ...
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 ...
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 ...
Pipeline: allowing the simultaneous running of several components on the same data stream, e.g. looking up a value on record 1 at the same time as adding two fields on record 2 Component: The simultaneous running of multiple processes on different data streams in the same job, e.g. sorting one input file while removing duplicates on another file
Pig Latin script describes a directed acyclic graph (DAG) rather than a pipeline. [11] Pig Latin's ability to include user code at any point in the pipeline is useful for pipeline development. If SQL is used, data must first be imported into the database, and then the cleansing and transformation process can begin. [11]
When the pipeline is in the playing state, data buffers flow from the source pad to the sink pad. Pads negotiate the kind of data that will be sent using capabilities. The diagram to the right could exemplify playing an MP3 file using GStreamer. The file source reads an MP3 file from a computer's hard-drive and sends it to the MP3 decoder.
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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.