{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T17:40:08Z","timestamp":1749318008447,"version":"3.41.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031937057","type":"print"},{"value":"9783031937064","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-93706-4_12","type":"book-chapter","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T17:22:41Z","timestamp":1749316961000},"page":"201-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Querying Labeled Time Series Data with\u00a0Scenario Programs"],"prefix":"10.1007","author":[{"given":"Edward","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devan","family":"Shanker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Bharadwaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbeen","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinkyu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hazem","family":"Torfah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel J.","family":"Fremont","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjit A.","family":"Seshia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Dreossi, T., Donze, A., Seshia, S.A.: Compositional falsification of cyber-physical systems with machine learning components. In: Proceedings of the NASA Formal Methods Conference (NFM), pp. 357\u2013372 (2017)","DOI":"10.1007\/978-3-319-57288-8_26"},{"key":"12_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-030-25540-4_25","volume-title":"Computer Aided Verification","author":"T Dreossi","year":"2019","unstructured":"Dreossi, T., et al.: VerifAI: a toolkit for the formal design and analysis of artificial intelligence-based systems. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 432\u2013442. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_25"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Du, P., Driggs-Campbell, K.: Adaptive failure search using critical states from domain experts. In: International Conference on Robotics and Automation (ICRA) (2021)","DOI":"10.1109\/ICRA48506.2021.9561477"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Advsim: generating safety-critical scenarios for self-driving vehicles. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00978"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Kim, E., Gopinath, D., Pasareanu, C., Seshia, S.: A programmatic and semantic approach to explaining and debugging neural network based object detectors. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11125\u201311134. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01114"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Fremont, D.J., et al.: Formal scenario-based testing of autonomous vehicles: from simulation to the real world. In: 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) (2020)","DOI":"10.1109\/ITSC45102.2020.9294368"},{"issue":"7","key":"12_CR7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1145\/3503914","volume":"65","author":"SA Seshia","year":"2022","unstructured":"Seshia, S.A., Sadigh, D., Sastry, S.S.: Toward verified artificial intelligence. Commun. ACM 65(7), 46\u201355 (2022)","journal-title":"Commun. ACM"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving, arXiv arXiv:1903.11027 (2019)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Fremont, D.J., Dreossi, T., Ghosh, S., Yue, X., Sangiovanni-Vincentelli, A.L., Seshia, S.A.: Scenic: a language for scenario specification and scene generation. In: PLDI, pp. 63\u201378. ACM (2019)","DOI":"10.1145\/3314221.3314633"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Fremont, D.J., et al.: Scenic: a language for scenario specification and data generation. Mach. Learn. (2022). https:\/\/doi.org\/10.1007\/s10994-021-06120-5","DOI":"10.1007\/s10994-021-06120-5"},{"key":"12_CR13","unstructured":"Foretellix: Measurable scenario description language (2020). https:\/\/www.foretellix.com\/wp-content\/uploads\/2020\/07\/M-SDL_LRM_OS.pdf"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Kang, D., Bailis, P., Zaharia, M.: Blazeit: optimizing declarative aggregation and limit queries for neural network-based video analytics. In: Conference on Very Large Database (VLDB) (2019)","DOI":"10.14778\/3372716.3372725"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Haynes, B., Daum, M., Mazumdar, A., Balazinska, M., Cheung, A., Ceze, L.: Visualworlddb: a DBMS for the visual world. In: Conference on Innovative Data Systems Research (2020)","DOI":"10.1145\/3299869.3324955"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Lu, C., Liu, M., Wu, Z.: SVQL: a SQL extended query language for video databases. Int. J. Database Theory Appl. (2015)","DOI":"10.14257\/ijdta.2015.8.3.20"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Moll, O., Bastani, F., Madden, S., Stonebraker, M., Gadepally, V., Kraska, T.: Exsample: efficient searches on video repositories through adaptive sampling. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2956\u20132968 (2022)","DOI":"10.1109\/ICDE53745.2022.00266"},{"key":"12_CR18","unstructured":"van der Lans, R.F.: Introduction to SQL: Mastering the Relational Database Language (4th Edition). Addison-Wesley Professional (2006)"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Kittivorawong, C., Ge, Y., Helal, Y., Cheung, A.: Spatialyze: a geospatial video analytics system with spatial-aware optimizations. Proc. VLDB Endow. 17(9), 2136\u20132148 (2024). https:\/\/www.vldb.org\/pvldb\/vol17\/p2136-kittivorawong.pdf","DOI":"10.14778\/3665844.3665846"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Kim, E., Shenoy, J., Junges, S., Fremont, D.J., Sangiovanni-Vincentelli, A., Seshia, S.A.: Querying labelled data with scenario programs for sim-to-real validation. In: 13th ACM\/IEEE International Conference on Cyber-Physical Systems, ICCPS 2022, Milano, Italy, 4\u20136 May 2022, pp. 34\u201345. IEEE (2022). https:\/\/doi.org\/10.1109\/ICCPS54341.2022.00010","DOI":"10.1109\/ICCPS54341.2022.00010"},{"key":"12_CR21","unstructured":"Bordes, F., et al.: An introduction to vision-language modeling (2024). https:\/\/arxiv.org\/abs\/2405.17247"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Taheri, H., Xia, Z.C.: Slam; definition and evolution. Eng. Appl. Artif. Intell. 97, 104032 (2021). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197620303092","DOI":"10.1016\/j.engappai.2020.104032"},{"issue":"1","key":"12_CR23","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TITS.2020.3012034","volume":"23","author":"S Mozaffari","year":"2022","unstructured":"Mozaffari, S., Al-Jarrah, O.Y., Dianati, M., Jennings, P., Mouzakitis, A.: Deep learning-based vehicle behavior prediction for autonomous driving applications: a review. IEEE Trans. Intell. Transp. Syst. 23(1), 33\u201347 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"12_CR24","unstructured":"Lee, E.A., Seshia, S.A.: Introduction to Embedded Systems: A Cyber-Physical Systems Approach. MIT Press (2016). http:\/\/leeseshia.org"},{"key":"12_CR25","unstructured":"Shanker, D.: Querying labeled time series data with scenario programs, Master\u2019s thesis, EECS Department, University of California, Berkeley (2024). http:\/\/www2.eecs.berkeley.edu\/Pubs\/TechRpts\/2024\/EECS-2024-136.html"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Yannakakis, M.: Hierarchical state machines. In: Proceedings of the International Conference IFIP on Theoretical Computer Science, Exploring New Frontiers of Theoretical Informatics, ser. TCS 2000, pp. 315\u2013330. Springer, Heidelberg (2000)","DOI":"10.1007\/3-540-44929-9_24"},{"key":"12_CR27","unstructured":"Barrett, C., Sebastiani, R., Seshia, S.A., Tinelli, C.: Satisfiability modulo theories. In: Handbook of Satisfiability, ch.\u00a026, pp. 825\u2013885. IOS Press (2009)"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Van Mierlo, S., Vangheluwe, H.: Introduction to statecharts modeling, simulation, testing, and deployment. In: 2019 Winter Simulation Conference (WSC), pp. 1504\u20131518 (2019)","DOI":"10.1109\/WSC40007.2019.9004771"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Holzer, M., Kutrib, M.: Nondeterministic finite automata\u2014recent results on the descriptional and computational complexity. In: Ibarra, O.H., Ravikumar, B. (eds.) Implementation and Applications of Automata, pp. 1\u201316. Springer, Heidelberg (2008)","DOI":"10.1007\/978-3-540-70844-5_1"},{"key":"12_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-030-99524-9_24","volume-title":"Tools and Algorithms for the Construction and Analysis of Systems","author":"H Barbosa","year":"2022","unstructured":"Barbosa, H., et al.: cvc5: a versatile and industrial-strength SMT solver. In: Fisman, D., Rosu, G. (eds.) TACAS 2022. LNCS, vol. 13243, pp. 415\u2013442. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99524-9_24"},{"key":"12_CR31","unstructured":"OpenAI: GPT-4: Openai\u2019s advanced language model (2024). https:\/\/openai.com\/research\/gpt-4. Accessed 16 Dec 2024"},{"key":"12_CR32","unstructured":"Anthropic: Claude: an AI assistant by anthropic (2024). https:\/\/www.anthropic.com. Accessed 16 Dec 2024"},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Manmadhan, S., Kovoor, B.: Visual question answering: a state-of-the-art review. Artif. Intell. Rev. (2020)","DOI":"10.1007\/s10462-020-09832-7"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Moon, S., et al.: Visiontrap: vision-augmented trajectory prediction guided by textual descriptions. In: European Conference on Computer Vision (ECCV) (2024)","DOI":"10.1007\/978-3-031-72658-3_21"},{"key":"12_CR35","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"}],"container-title":["Lecture Notes in Computer Science","NASA Formal Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-93706-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T17:22:48Z","timestamp":1749316968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-93706-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031937057","9783031937064"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-93706-4_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NFM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"NASA Formal Methods Symposium","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hampton Roads, VA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nfm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shemesh.larc.nasa.gov\/nfm2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}