Big-Data Processing Task

A Big-Data Processing Task is a data processing task that accepts a big-data dataset (a very-large complex).

• Context:
  • It can be solved by Big Data Processing System.
  • …
• Example(s):
  • a High Data-Velocity Task.
  • Big Data Search.
  • Big Data Analysis.
  • Big Data Storage.
  • Big NLP.
  • Big Data Visualiation.
  • Web Crawling Task.
  • Web Search Keyword Ad Auction.
  • …
• Counter-Example(s):
  • a Small Data Task.
  • Small Data Processing.
• See: Exabyte, Relational Database Management, Data Sharing, Data Visualization.

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References

2016

• (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/big_data Retrieved:2016-6-1.
  • Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.

    Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research. Data sets are growing rapidly in part because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; , every day 2.5 exabytes (2.5×10¹⁸) of data is created. One question for large enterprises is determining who should own big data initiatives that affect the entire organization. Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers". What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."

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