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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 [11] introduced k-minimum value algorithm for determining number of distinct elements in data ...
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 ...
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.
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 ...
Pipeline Pilot is a software tool designed for data manipulation and analysis. It provides a graphical user interface for users to construct workflows that integrate and process data from multiple sources, including CSV files, text files, and databases. The software is commonly used in extract, transform, and load (ETL) tasks.
Flow-based programming defines applications using the metaphor of a "data factory". It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time until it is finished, but as a network of asynchronous processes communicating by means of streams of structured data chunks, called "information packets" (IPs).
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
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]