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We devise a complete set of methods for learning relevant events, for building the event-order graph stream from the original data stream, for embedding and prediction, and for theoretically bounding the complexity. We evaluate our approach with four real world stream datasets and find that our method results in high precision and recall values for event timing prediction, ranging between 0.7 and nearly 1, significantly outperforming baseline approaches. Moreover, due to our choice of efficient translation-based embedding, the overall throughput that the stream system can handle, including continuous graph building, training, and event predictions, is over one thousand to sixty thousand tuples per second even on a personal computer---which is especially important in resource constrained environments, including edge computing.<\/jats:p>","DOI":"10.14778\/3401960.3401973","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T19:15:14Z","timestamp":1615403714000},"page":"1779-1792","source":"Crossref","is-referenced-by-count":13,"title":["Data stream event prediction based on timing knowledge and state transitions"],"prefix":"10.14778","volume":"13","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[{"name":"University of Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingjian","family":"Ge","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cindy","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"An Oracle White Paper","year":"2009","unstructured":"Oracle complex event processing: Lightweight modular application event stream processing in the real world . 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