Database Reference
In-Depth Information
demonstrated the potential of mechanistic or causal movement models that can
find wide applications in many different research fields. Concepts like ran-
dom walks and diffusion, for example, aim to connect individual and collective
patterns.
Web
The social web has changed the way people create and use information. Particu-
larly relevant to this topic is the explosion of services based on geosocial content.
So-called volunteered geographic information (VGI) allows people visiting real
and virtual places to leave footprints of their movement in the social web,
with their movements being recorded and analyzed. The amount of generated
detailed location and event tags is huge and covers not only popular landmarks,
but also obscure places, thus providing broad and wide coverage of locations
in unprecedented scales. This large amount of information, often unstructured,
opens a research avenue in the field of real-time analysis of data flow in the
Semantic Web. Such interplay of mobility and geosocial networks research is
very promising. There is indeed a strong relationship between the social and the
mobility behaviors of humans. People move, they move with friends or relatives,
and they use the social web to share experiences, propagating information about
new places to friends.
The trajectory reconstruction in the social network opens such challenging
issues as, for example, data acquisition, given that such data can be discontinuous
in time, or geographically uncertain, because each point of the trajectory can
have a different scale (sometimes coordinates of a specific place, sometimes a
bounding box area).
Despite the large interest and the large amount of data produced, there is a
clear disproportion between the results obtained so far and the vast quantity and
diversity of issues that are still open. We believe that the issues and peculiarities
related to the data (availability, privacy, granularity, and so on) and the rapid
explosion of the availability of new services and trends are two clear reasons
why it is still hard for the research in this direction to take off and to produce
large and strong analytical results. The preliminary work conducted by many
researchers so far is very promising, and it seems clear that we are facing the
start of a new era in the research on societal and individual human behaviors.
Large Scale and Streams
Nowadays we live in a world overloaded by information. The information at
our disposal is so large and complex that traditional data processing tools and
paradigms are no longer capable to cope with it. This phenomenon has been
dubbed “Big Data.” New computing paradigms have been proposed as a solution
to this new state of affairs, MapReduce being themost prominent of all. Their aim
is to enable massive parallelization of data processing in order to speed up this
