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Machine learning - Stream Processing

Data Streaming plays a major role in real-time application due to the generation of data from an infinite number of sources, and stream processing produces responses much faster than the other data processing methods. A data stream is a continuous flow of data from heterogeneous sources. Common examples include activity stream data from a web or mobile application, time-stamped log data, transactional data, and event streams from sensor or device networks. The need for stream processing in data management is that it accelerates decision-making and develops the adaptive application. Stream processing refers to the processing of a continuous stream of data instantly as it is produced. It analyzes streaming data in real-time, and it is used when the data size is unknown and infinite, and continuous. Machine learning at vast amounts of data or to process stream data, there are three potential levels of impact such as reducing the number of incoming data points or dimensions, data handling process, and implementation and modification of learning algorithm. The stock market, e-commerce transactions, manufacturing, retail, finance, and social media are the major application areas of Stream processing. Advanced future scopes of stream data processing are Real-time streaming mobility analytics, Multidimensional skylines over streaming data, Streaming analysis in wireless sensor networks, A self-managing wide-area data streaming service using model-based online control, Streaming big data with self-adjusting computation, and more.