{"url":"/task/service-composition","name":"Service Composition","slug":"service-composition","description_markdown":"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 =\r\n{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\r\nservices 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 =\r\n{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 }\r\nWe 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\r\nare 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-\r\nber 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\r\nNP-Hard.","categories":[{"name":"Miscellaneous","url":"/area/miscellaneous"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":45,"papers_with_code":6,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":6,"of":6,"tagged_in_all":45,"items":[{"url":"/paper/rlhgnn-reinforcement-learning-driven","title":"RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes","date":"2025-07-03","arxiv_id":"2507.02690","repositories_listed":1,"syntology":null},{"url":"/paper/an-ai-chatbot-for-explaining-deep","title":"An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems","date":"2023-09-25","arxiv_id":"2309.14391","repositories_listed":1,"syntology":null},{"url":"/paper/qos-aware-big-service-composition-using","title":"QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm","date":"2021-09-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/190600772","title":"Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing","date":"2019-05-31","arxiv_id":"1906.00772","repositories_listed":1,"syntology":null},{"url":"/paper/cognitively-inspired-agent-based-service","title":"Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing","date":"2019-05-29","arxiv_id":"1905.12630","repositories_listed":1,"syntology":null},{"url":"/paper/190107910","title":"NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings","date":"2019-01-23","arxiv_id":"1901.07910","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}