Contemporary Computing Technologies for Processing Big Spatiotemporal Data - Space-Time Integration in Geography and GIScience - page 325

18.2.6 Privacy

Privacy is another critical concern, and one that becomes increasingly important
as many big data collected daily are personal related (which sensitively reflects
the personal information, activities and behaviors in both physical and virtual
world). Therefore, there is increasing public concern regarding the inappropriate
use of personal data, particularly through converging of data from multiple sources.
Managing privacy is both a technical and a sociological problem, which must be
addressed jointly from both perspectives to achieve the promise of big data.

18.2.7 Massive Collaboration

Many analytics for big data will not only involve the computational processes
automatically conducted by machines, but will also involve human interventions.
For example, in web x.0 era, end users are involved as the sensors or data providers.
Many data are collected and contributed by the users. In big data processing
procedures, it is important to support the input and collaboration from multiple
human experts in different domains to have better understanding and wise decisions.
A systematic solution for big data should not only tackle the physical and
technical challenges but also consider and coordinate the policies and social aspects.
This chapter will focus on the computing technology.

18.3 Big Data Management

The first challenge about big data is discoverability and accessibility. This section
describes with an example how metadata, catalogs, index, and database are used to
facilitate big data discovery and access using database archived metadata and index
for component, service and datasets (Fig. 18.1 ).

18.3.1 Metadata and GEOSS Clearinghouse

Metadata is structured information that describes, explains, locates, and makes it
easier to retrieve, use, or manage data or an information resource. It is often defined
as data about data. In general, there are two kind of metadata: one is descriptive data,
which describes the content and serves as a brief abstract of the data; and another is
structural metadata, which describes the design and specification of data structures.
Metadata is critical for big data management and discovery. Well designed and
described metadata mechanism could better support management and discovery