{"status":"public","citation":{"apa":"Ničković, D., Qin, X., Ferrere, T., Mateis, C., &#38; Deshmukh, J. (2019). Shape expressions for specifying and extracting signal features. In <i>19th International Conference on Runtime Verification</i> (Vol. 11757, pp. 292–309). Porto, Portugal: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-32079-9_17\">https://doi.org/10.1007/978-3-030-32079-9_17</a>","short":"D. Ničković, X. Qin, T. Ferrere, C. Mateis, J. Deshmukh, in:, 19th International Conference on Runtime Verification, Springer Nature, 2019, pp. 292–309.","ista":"Ničković D, Qin X, Ferrere T, Mateis C, Deshmukh J. 2019. Shape expressions for specifying and extracting signal features. 19th International Conference on Runtime Verification. RV: Runtime Verification, LNCS, vol. 11757, 292–309.","mla":"Ničković, Dejan, et al. “Shape Expressions for Specifying and Extracting Signal Features.” <i>19th International Conference on Runtime Verification</i>, vol. 11757, Springer Nature, 2019, pp. 292–309, doi:<a href=\"https://doi.org/10.1007/978-3-030-32079-9_17\">10.1007/978-3-030-32079-9_17</a>.","chicago":"Ničković, Dejan, Xin Qin, Thomas Ferrere, Cristinel Mateis, and Jyotirmoy Deshmukh. “Shape Expressions for Specifying and Extracting Signal Features.” In <i>19th International Conference on Runtime Verification</i>, 11757:292–309. Springer Nature, 2019. <a href=\"https://doi.org/10.1007/978-3-030-32079-9_17\">https://doi.org/10.1007/978-3-030-32079-9_17</a>.","ama":"Ničković D, Qin X, Ferrere T, Mateis C, Deshmukh J. Shape expressions for specifying and extracting signal features. In: <i>19th International Conference on Runtime Verification</i>. Vol 11757. Springer Nature; 2019:292-309. doi:<a href=\"https://doi.org/10.1007/978-3-030-32079-9_17\">10.1007/978-3-030-32079-9_17</a>","ieee":"D. Ničković, X. Qin, T. Ferrere, C. Mateis, and J. Deshmukh, “Shape expressions for specifying and extracting signal features,” in <i>19th International Conference on Runtime Verification</i>, Porto, Portugal, 2019, vol. 11757, pp. 292–309."},"quality_controlled":"1","conference":{"start_date":"2019-10-08","end_date":"2019-10-11","location":"Porto, Portugal","name":"RV: Runtime Verification"},"isi":1,"date_published":"2019-10-01T00:00:00Z","date_updated":"2023-09-06T11:24:10Z","oa_version":"None","publication_status":"published","date_created":"2019-12-09T08:47:55Z","year":"2019","page":"292-309","scopus_import":"1","project":[{"call_identifier":"FWF","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211"},{"grant_number":"S11402-N23","call_identifier":"FWF","name":"Rigorous Systems Engineering","_id":"25F2ACDE-B435-11E9-9278-68D0E5697425"}],"intvolume":"     11757","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","external_id":{"isi":["000570006300017"]},"publication_identifier":{"issn":["0302-9743"],"isbn":["9783030320782","9783030320799"]},"alternative_title":["LNCS"],"language":[{"iso":"eng"}],"publisher":"Springer Nature","type":"conference","volume":11757,"month":"10","day":"01","author":[{"last_name":"Ničković","full_name":"Ničković, Dejan","first_name":"Dejan"},{"first_name":"Xin","full_name":"Qin, Xin","last_name":"Qin"},{"orcid":"0000-0001-5199-3143","first_name":"Thomas","last_name":"Ferrere","id":"40960E6E-F248-11E8-B48F-1D18A9856A87","full_name":"Ferrere, Thomas"},{"full_name":"Mateis, Cristinel","last_name":"Mateis","first_name":"Cristinel"},{"first_name":"Jyotirmoy","full_name":"Deshmukh, Jyotirmoy","last_name":"Deshmukh"}],"_id":"7159","doi":"10.1007/978-3-030-32079-9_17","abstract":[{"lang":"eng","text":"Cyber-physical systems (CPS) and the Internet-of-Things (IoT) result in a tremendous amount of generated, measured and recorded time-series data. Extracting temporal segments that encode patterns with useful information out of these huge amounts of data is an extremely difficult problem. We propose shape expressions as a declarative formalism for specifying, querying and extracting sophisticated temporal patterns from possibly noisy data. Shape expressions are regular expressions with arbitrary (linear, exponential, sinusoidal, etc.) shapes with parameters as atomic predicates and additional constraints on these parameters. We equip shape expressions with a novel noisy semantics that combines regular expression matching semantics with statistical regression. We characterize essential properties of the formalism and propose an efficient approximate shape expression matching procedure. We demonstrate the wide applicability of this technique on two case studies. "}],"title":"Shape expressions for specifying and extracting signal features","publication":"19th International Conference on Runtime Verification","article_processing_charge":"No","department":[{"_id":"ToHe"}]}