Mining Big Data and Streams

Mining Big Data and Streams

Hoda Ahmed Abdelhafez (Suez Canal University, Egypt)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/978-1-5225-2255-3.ch036


Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.
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Challenges Of Big Data Systems

Big data has five key elements: Volume, Velocity, Variety, Veracity and value. These 5 V’s are considered challenges of Big Data systems (Yin & Kaynak, 2015; Ishwarappa & Anuradha, 2015; Marr, 2015).

Volume refers to the huge amount of data. Many companies have large archived data in the form of logs but do not have the capacity to manipulate and analyze that data using traditional database technology. Now big data technology can help store and use these datasets in order to gain benefits from them.

Key Terms in this Chapter

Data Stream: A continuous flow of data.

Mining Data Stream: The ability to extract valuable information from large data streams.

Big Data: The datasets that are of large size and have a greater complexity.

Data Mining: The process of searching large volumes of data automatically for patterns.

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