{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T21:51:49Z","timestamp":1719179509625},"reference-count":7,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Real-time processing of data streams emanating from sensors is becoming a common task in industrial scenarios. An increasing number of processing jobs executed over such platforms are requiring reasoning mechanisms. The key implementation goal is thus to efficiently handle massive incoming data streams and support reasoning, data analytic services. Moreover, in an on-going industrial project on anomaly detection in large potable water networks, we are facing the effect of dynamically changing data and work characteristics in stream processing. The Strider system addresses these research and implementation challenges by considering scalability, fault-tolerance, high throughput and acceptable latency properties. We will demonstrate the benefits of Strider on an Internet of Things-based real world and industrial setting.<\/jats:p>","DOI":"10.14778\/3137765.3137805","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T13:35:53Z","timestamp":1504791353000},"page":"1905-1908","source":"Crossref","is-referenced-by-count":9,"title":["Strider"],"prefix":"10.14778","volume":"10","author":[{"given":"Xiangnan","family":"Ren","sequence":"first","affiliation":[{"name":"ATOS, Bezons, France and UPEM LIGM, Marne-la-Vall\u00e9e, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Cur\u00e9","sequence":"additional","affiliation":[{"name":"UPEM LIGM, Marne-la-Vall\u00e9e, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Ke","sequence":"additional","affiliation":[{"name":"ATOS, Bezons, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy","family":"Lhez","sequence":"additional","affiliation":[{"name":"UPEM LIGM, Marne-la-Vall\u00e9e, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Badre","family":"Belabbess","sequence":"additional","affiliation":[{"name":"ATOS, Bezons, France and UPEM LIGM, Marne-la-Vall\u00e9e, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tendry","family":"Randriamalala","sequence":"additional","affiliation":[{"name":"ATOS, Bezons, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufan","family":"Zheng","sequence":"additional","affiliation":[{"name":"ATOS, Bezons, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel","family":"Kepeklian","sequence":"additional","affiliation":[{"name":"ATOS, Bezons, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526856"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363955"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1331939.1331940"},{"key":"e_1_2_1_4_1","volume-title":"SSWS","author":"Fischer","year":"2013","unstructured":"L. Fischer and al. Scalable linked data stream processing via network-aware workload scheduling . In SSWS , 2013 . L. Fischer and al. Scalable linked data stream processing via network-aware workload scheduling. In SSWS, 2013."},{"key":"e_1_2_1_5_1","volume-title":"ISWC","author":"Phuoc","year":"2013","unstructured":"D. L. Phuoc and al. Elastic and scalable processing of linked stream data in the cloud . In ISWC 2013 . D. L. Phuoc and al. Elastic and scalable processing of linked stream data in the cloud. In ISWC 2013."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367578"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2247596.2247635"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3137765.3137805","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:06:35Z","timestamp":1672221995000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3137765.3137805"}},"subtitle":["an adaptive, inference-enabled distributed RDF stream processing engine"],"short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":7,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["10.14778\/3137765.3137805"],"URL":"https:\/\/doi.org\/10.14778\/3137765.3137805","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2017,8]]}}}