Service Composition
5 papers with code • 0 benchmarks • 0 datasets
Let T be the task that the service composition needs to accomplish. The task T can be granulated to T 1 , T 2 , T 3 , T 4 , … , T n . i.e. T = {T 1 , T 2 , T 3 , T 4 , … , T n } . For each task T i , a set of service S i = S i 1 , S i 2 , S i 3 , … , S i m is discovered during the service discovery process such that all services in a set S i perform the same function and have the same input and output parameters (See Figure 2). S 1 = {S 11 , S 12 , S 13 , … , S 1m } , S 2 = {S 21 , S 22 , S 23 , … , S 2m } , S 3 = {S 31 , S 32 , S 33 , … , S 3m } , … , S n = {S n 1 , S n 2 , S n 3 , … , S n m } We need to select one service from each set S i in order to compose the big service such that the overall QoS attributes of the big service are optimal. The total number of the possible distinct service composition is n m . Let k be the the number of QoS attributes. Then the total num- ber of comparisons required are kn m . We need at least kn m comparisons to find whether the solution is optimal, thus making the problem as NP-Hard.
Benchmarks
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Most implemented papers
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations.
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment.
Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment.
QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data.
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front.