The new stage in the evolution of the Web (the Live Web or Evented Web) puts lots of social
datastreams at the service of users, who no longer browse static webpages but interact with
applications that present them contextual and relevant experiences. Given that each user
is a potential source of events, a typical user often gets overwhelmed. To deal with that
huge amount of data, multiple automation tools have emerged, covering from simple social
media managers or notification aggregators to complex CRMs or smart-home Hub/Apps.
As a downside, they cannot tailor to the needs of every single user.
As a natural response to this downside, Task Automation Services broke in the Internet.
They may be seen as a new model of mashup technology for combining social streams,
services and connected devices from an end-user perspective: end-users are empowered to
connect those stream however they want, designing the automations they need. The numbers
of those platforms that appeared early on shot up, and as a consequence the amount of
platforms following this approach is growing fast.
Being a novel field, this thesis aims to shed light on it, presenting and exemplifying
the main characteristics of Task Automation Services, describing their components, and
identifying several dimensions to classify them. This thesis coins the term Task Automation
Services (TAS) by providing a formal definition of them, their components (called channels),
as well a TAS reference architecture.
There is also a lack of tools for describing automation services and automations rules.
In this regard, this thesis proposes a theoretical common model of TAS and formalizes it as
the EWE ontology This model enables to compare channels and automations from different
TASs, which has a high impact in interoperability; and enhances automations providing a
mechanism to reason over external sources such as Linked Open Data.
Based on this model, a dataset of components of TAS was built, harvesting data from the
web sites of actual TASs. Going a step further towards this common model, an algorithm
for categorizing them was designed, enabling their discovery across different TAS.
Thus, the main contributions of the thesis are: i) surveying the state of the art on task
automation and coining the term Task Automation Service; ii) providing a semantic common
model for describing TAS components and automations; iii) populating a categorized dataset
of TAS components, used to learn ontologies of particular domains from the TAS perspective;
and iv) designing a agent architecture for assisting users in setting up automations, that is
aware of their context and acts in consequence.