Database Reference
In-Depth Information
store characteristics of the support calls as typical structured data, with attributes
such as time stamps, machine type, problem type, and operating system. In
addition, the system will likely have unstructured, quasi- or semi-structured data,
such as free-form call log information taken from an e-mail ticket of the problem,
customer chat history, or transcript of a phone call describing the technical
problem and the solution or audio file of the phone call conversation. Many
insights could be extracted from the unstructured, quasi- or semi-structured data
in the call center data.
Figure 1.3
Big Data Growth is increasingly unstructured
Although analyzing structured data tends to be the most familiar technique, a
different technique is required to meet the challenges to analyze semi-structured
data (shown as XML), quasi-structured (shown as a clickstream), and unstructured
data.
Here are examples of how each of the four main types of data structures may look.
•
Structured data:
Data containing a defined data type, format, and
structure (that is, transaction data, online analytical processing [OLAP]
data cubes, traditional RDBMS, CSV files, and even simple spreadsheets).
See
Figure 1.4
.
