data-architecture-a

Fig. 9.2.11 Documenting summarization. Metadata in Big Data While data are the essence of what is stored in big data, it is impo ...

Field name Field length Field type Field identifying characteristics Native metadata are used to identify and describe data that ...

very good reasons for managing metadata differently. In big data, it often makes sense to store the descriptive metadata physica ...

One of the fundamental issues of data is that of how data are linked to each other. This issue is an issue in big data just as i ...

Fig. 9.2.14 Different kinds of linkages. Chapter 9.2: Analyzing Repetitive Data ...

Chapter 9.3 Repetitive Analysis Abstract There are many facets to the analysis of repetitive data. One type of data where repeti ...

Fig. 9.3.1 Repetitive data can come from almost anywhere. Universal Identifiers As data are stored in big data and as textual di ...

has its own quirks. Greenwich mean time (GMT) is the time that occurs at the meridian that runs through Greenwich, England. The ...

Health-care data need to be secure because of privacy reasons. Personal financial data need to be secure because of theft and p ...

Fig. 9.3.4 Encrypting a field of data. There are many issues that relate to encryption. Some of the issues are as follows: How ...

Another interesting aspect of security is looking at who is trying to look at encrypted data. The access and analysis of encrypt ...

As a rule, distillation is done on a project basis or on an irregular unscheduled basis. Fig. 9.3.7 shows the process of distill ...

Archiving Results Much of the analytic processing that is done against repetitive data is of the project variety. And there is a ...

Fig. 9.3.9 Archiving the results of analysis. At the very least, the results created by the project should be gathered and store ...

establish whether a project has met its objectives. The optimal time to outline such metrics is at the very outset of the projec ...

Chapter 10.1 Nonrepetitive Data Abstract Nonrepetitive analytics begins with the contextualization of the nonrepetitive data. Un ...

Fig. 10.1.1 Nonrepetitive data. The nonrepetitive data found in big data are called “nonrepetitive” because each unit of data is ...

Fig. 10.1.3 The only similarities are accidental. As an example of two units of nonrepetitive data being the same, suppose there ...

The mechanics of textual disambiguation are shown in Fig. 10.1.4. Fig. 10.1.4 The mechanics of textual disambiguation. The gener ...

“parsing” implies a straightforward process, and the logic that occurs here is anything but straightforward. The remainder of th ...