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Increasing adoption of Big Data in business environments have driven the needs of stream joining in realtime fashion. Multi-stream joining is an important stream processing type in today's Internet companies, and it has been used to generate higher-quality data in business pipelines. Multi-stream joining can be performed in two models: (1) All-In-One (AIO) Joining and (2) Step-By-Step (SBS) Joining...
As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often...
Data quality is essential in big data paradigm as poor data can have serious consequences when dealing with large volumes of data. While it is trivial to spot poor data for small-scale and offline use cases, it is challenging to detect and fix data inconsistency in large-scale and online (real-time or near-real time) big data context. An example of such scenario is spotting and fixing poor data using...
Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each...
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