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The Web of Smart Entities 129

Smart substitutions. The use of AI technologies and the use of ontologies
such as used in the context of the semantic web enable smart substitutions.
We see examples of this substitution when, based on dietary restrictions,
alternate meals may be suggested, or when certain kinds of exercises are
recommended based on availability or opportunity.

7.5.4 Web of Smart Entities

Consider Google’s Nest thermostat; in addition to processing data from its
internal sensors, it can process data about the weather communicated to it by
a weather app. We see Google’s Nest as highlighting the beginnings of a
richly interwoven fabric of applications that are directly or indirectly
informed by sensor data.
Definition 4. WSE consists of a highly connected web of software
applications that manage and automate routine behavior.
A few representative tasks for these smart applications are listed in the
following section.
Balancing. If an application that manages a person’s exercise activities
interacts with an application that manages a person’s dietary intake, physical
fitness can be balanced to specifications. If we empower the fitness model to
make the relevant decisions, we can dynamically adjust a person’s fitness. For
example, the fitness model may encourage a walk or bike ride rather than the
use of a car or public transportation. Perhaps together they recommend a
dish that lowers a person’s caloric intake at a restaurant within walking
distance.
Seamlessness. Given the proliferation of data, it is likely that models will
gather data about particular activities in different contexts. For example,
food preferences will likely be gathered not just from meals prepared at
home, but also from meals ordered at restaurants or consumed in other set-
tings. This way an overarching and more informed model can be built.
Seamlessness comes about when an overarching model is applied in different
contexts. If the model learned that someone likes their coffee black, then this
is how it should be prepared, whether at home, at work, or by a coffee shop.
Recommendations. Models of a person’s behavior can be used to make rec-
ommendations based on matching to like models. For example, diet prefer-
ences, just as preferences that Netflix and Amazon gather about their
customers, can be used to match to similar models and, based on those
matches, recommendations may be made.