Database Reference
In-Depth Information
Chapter 3
Big Data Generation and Acquisition
Abstract We have introduced several key technologies related to big data, i.e.,
cloud computing, IoT, data center, and Hadoop. Next, we will focus on the value
chain of big data, which can be generally divided into four phases: data generation,
data acquisition, data storage, and data analysis. If we take data as a raw material,
data generation and data acquisition are exploitation process, data storage is a
storage process, and data analysis is a production process that utilizes the raw
material to create new value.
3.1
Big Data Generation
Data generation is the first step of big data. Specifically, it is large-scale, highly
diverse, and complex datasets generated through longitudinal and distributed data
sources. Such data sources include sensors, videos, click streams, and/or all other
available data sources. At present, main sources of big data are the operation
and trading information in enterprises, logistic and sensing information in the
IoT, human interaction information and position information in the Internet world,
and data generated in scientific research, etc. The information far surpasses the
capacities of IT architectures and infrastructures of existing enterprises, while its
real-time requirement also greatly stresses the existing computing capacity.
3.1.1
Enterprise Data
In 2013, IBM issued a reported titled “Analytics: The Real-world Use of Big Data,”
which indicates that the internal data of enterprises are the main sources of big
data. The internal data of enterprises mainly consists of online trading data and
online analysis data, most of which are historically static data and are managed by
RDBMSs in a structured manner. In addition, production data, inventory data, sales
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