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Research Topics in Data Stream Processing

Research Topics in Data 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.

   • In real-time, data arrives continuously in a potentially infinite stream, in a time-varying, unpredictable, rapid, heterogeneous, continuous, and unbound nature. Datastream processing systems normally hold fixed storage and computing power, and it processes data streams by a resource-constrained system.

   • Nowadays, data stream processing is inevitable. Several business services use streaming technologies and showing astonishing results.

   • When the stream data arrives from the different sources is growing, making sense of such incoming massive stream data and the historical data present is the main challenge.

   • In contrast to conventional data mining algorithms, mining or analyzing continuously arriving stream data raises numerous challenges.

   • In the real stream, learning tasks is that data typically changes over time. Hence adaptive machine learning models are needed to handle concept drift.

   • The effective use of entire information from the vast stream data is still a bottleneck while stream data processing.

   • It is necessary to work on data stream extraction and loading according to the requirements of the data stream processor and requires a solution for integrating the data from both operational data sources and data stream applications.