  Various types of services, such as web APIs, IoT services, O2O services, and
many others, have flooded on the Internet. Interconnections among these
services have resulted in a new phenomenon called "Internet of Services" (IoS).
By IoS,people don't need to request multiple services by themselves to fulfill
their daily requirements, but it is an IoS platform that is responsible for
constructing integrated solutions for them. Since user requirements (URs) are
usually coarse-grained and transboundary, IoS platforms have to integrate
services from multiple domains to fulfill the requirements. Considering there
are too many available services in IoS, a big challenge is how to look for a
tradeoff between the construction efficiency and the precision of final
solutions. For this challenge, we introduce a framework and a platform for
transboundary user requirement oriented solution design in IoS. The main idea
is to make use of domain priori knowledge derived from the commonness and
similarities among massive historical URs and among historical integrated
service solutions(ISSs). Priori knowledge is classified into three types:
requirement patterns (RPs), service patterns (SPs), and probabilistic matching
matrix (PMM) between RPs and SPs. A UR is modeled in the form of an intention
tree (ITree) along with a set of constraints on intention nodes, and then
optimal RPs are selected to cover the I-Tree as much as possible. By taking
advantage of the PMM, a set of SPs are filtered out and composed together to
form the final ISS. Finally, the design of a platform supporting the above
process is introduced.
