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数据挖掘推动客户数据驱动车辆的开发进程
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    Abstract:

    Data-driven product development is a key technology for systems engineering especially for consumer-oriented industries such as the automotive industry. The basic prerequisite for all data driven approaches is data itself. Due to the increasing networking capabilities of modern vehicles， automotive manufactures are able to record and store customer data in the form of internal vehicle bus signals. The challenge in using this data is that it is not designed for external use， but for internal communication to ensure the safety and functionality of the vehicle. Therefore， the main question is how to extract customer needs and consumer-relevant information within this data using the process of data mining （DM）. Consequently， in this paper， a literature review on the aforementioned use case is conducted. Based on the literature research， a DM simulation game is conducted to determine the suitability of existing DM processes in the area of requirements elicitation. Finally， a process extension is proposed that helps to systematically focus the DM process on customer-relevant information and thus accelerate the overall process.

    Reference
    [1] MAURER M, WINNER H. Automotive systems engineering[J]. Berlin: Springer, 2013.
    [2] D'AMBROSIO J, SOREMEKUN G. Systems engineering challenges and MBSE opportunities for automotive system design[C]// 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff: IEEE Xplore, 2017: 2075.
    [3] MASCHOTTA R, WICHMANN A, ZIMMERMANN A, et al. Integrated automotive requirements engineering with a SysML-based domain-specific language[C]// 2019 IEEE International Conference on Mechatronics (ICM). Ilmenau, Germany: IEEE Xplore, 2019: 402. DOI: https://doi.org/10.1109/ICMECH.2019.8722951.
    [4] FANK P, BOJA D, ABTHOFF T. Big data driven vehicle development – Technology and potential[C]// 2020 Internationales Stuttgarter Symposium. Wiesbaden: Springer Fachmedien Wiesbaden, 2020: 315. DOI: https://doi.org/10.1007/978-3-658-30995-4_31.
    [5] FRAGNER A, KREIS A, HIRZ M. Virtual tools to support design and production engineering: Early detection of stone chips to optimize production processes[C]// 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). Paris: IEEE, 2020: 399. DOI: https://doi.org/10.1109/ICIEA49774.2020.9102004.
    [6] BACH J, LANGNER J, OTTEN S, et al. Data-driven development, a complementing approach for automotive systems engineering[C]// 2017 IEEE International Systems Engineering Symposium (ISSE). IEEE, 2017: 1. DOI: https://doi.org/10.1109/SysEng.2017.8088295.
    [7] BAJZEK M, FRITZ J, HICK H. Systems engineering principles[C]// HICK H, KüPPER K, SORGER H. Systems Engineering for Automotive Powertrain Development. Cham: Springer International Publishing, 149. DOI: https://doi.org/10.1007/978-3-319-99629-5_7.
    [8] SILLITTO H, MARTIN J, MC KINNEY D,et al. Systems Engineering and System Definitions[Z]. San Diego: INCOSE — International Council on Systems Engineering, 2019.
    [9] VDI. Entwicklung cyber-physischer mechatronischer systeme (CPMS): VDI/VDE 2206[S]. 2020[2021-10-11]. https://www.vdi.de/richtlinien/details/vdivde-2206-entwicklung-cyber-physischer-mechatronischer-systeme-cpms.
    [10] VDI. Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte: VDI 2221[S]. 1993[2021-10-11]. https://www.vdi.de/richtlinien/details/vdi-2221-methodik-zum-entwickeln-und-konstruieren-technischer-systeme-und-produkte.
    [11] BAJZEK M, FRITZ J, HICK H. Systems engineering processes[C]// HICK H, KüPPER K, SORGER H. Systems Engineering for Automotive Powertrain Development. Cham: Springer International Publishing, 2021. 235. DOI: https://doi.org/10.1007/978-3-319-99629-5_9.
    [12] ESCH J, RETTMANN A, MARZINEAK S. A systems engineering approach to electromagnetic compatibility[C]// LIEBL J. Der Antrieb von morgen 2021. Berlin: Springer Berlin Heidelberg, 2021: 167. DOI: https://doi.org/10.1007/978-3-662-63403-5_11.
    [13] International Organization for Standardization. Information technology — Process assessment: ISO/IEC 15504-5: 2012[S]. [2021-10-11]. https://www.iso.org/standard/60555.html.
    [14] FAYYAD U, PIATETSKY-SHAPIRO G, SMYTH P. From data mining to knowledge discovery in databases[J]. AI Magazine, 1996, 17: 37.
    [15] FAYYAD U, PIATETSKY-SHAPIRO G, SMYTH P. The KDD process for extracting useful knowledge from volumes of data[J]. Commun ACM, 1996, 39(11): 27. DOI: https://doi.org/10.1145/240455.240464.
    [16] KURGAN L A, MUSILEK P. A survey of knowledge discovery and data mining process models[J]. The Knowledge Engineering Review, 2006, 21(1): 1. DOI: https://doi.org/10.1017/S0269888906000737.
    [17] MARISCAL G, MARBáN ó, FERNáNDEZ C. A survey of data mining and knowledge discovery process models and methodologies[J]. The Knowledge Engineering Review, 2010, 25(2): 137. DOI: https://doi.org/10.1017/S0269888910000032.
    [18] HAN J W, KAMBER M, PEI J. Data Mining: Concepts and Techniques[M]. [S. l.]: Elsevier professional, 2011.
    [19] ROTONDO A, QUILLIGAN F. Evolution Paths for Knowledge Discovery and Data Mining Process Models[J]. SN Computer Science, 2020, 1(2): 109. DOI: https://doi.org/10.1007/s42979-020-0117-6.
    [20] CIOS K J, KURGAN L A. Trends in Data Mining and Knowledge Discovery[C]// PAL N R, JAIN L. Advanced Techniques in Knowledge Discovery and Data Mining. London: Advanced Information and Knowledge Processing, Springer London, 2005: 1. DOI: https://doi.org/10.1007/1-84628-183-0_1.
    [21] PLOTNIKOVA V, DUMAS M, MILANI F. Adaptations of data mining methodologies: A systematic literature review[J]. PeerJ Computer Science, 2020, 6(2): e267. DOI: https://doi.org/10.7717/peerj-cs.267.
    [22] CHAPMAN P, CLINTON J, KERBER R, et al. CRISP-DM 1.0. Step-by-step data mining guide[A]. [S. l.]: CRISP-DM Consortium, 2000.
    [23] MARBáN ó, MARISCAL G, SEGOVI J. 2009. A data mining & knowledge discovery process model [C]// PONCE J, KARAHOC A. Data Mining and Knowledge Discovery in Real Life Applications.[S. l.]: I-Tech Education and Publishing, 2009. DOI: https://doi.org/10.5772/6438.
    [24] MARTíNEZ-PLUMED F, CONTRERAS-OCHANDO L, FERRI C, et al. CASP-DM context aware standard process for data mining [DB/OL]. (2017-09-19) [2021-05-28]. https://doi.org/10.48550/arXiv.1709.09003.
    [25] PECHENIZKIY M, PUURONEN S, TSYMBAL A. Does relevance matter to data mining research？[C]// KACPRZYK J, LIN T Y, XIE Y, et al. Data Mining: Foundations and Practice: Part of the Studies in Computational Intelligence book series (SCI, volume 118). Berlin: Springer Berlin Heidelberg, 2008: 251. DOI: https://doi.org/10.1007/978-3-540-78488-3_15.
    [26] PLOTNIKOVA V, DUMAS M, MILANI F. Adapting the CRISP-DM data mining process: A case study in the financial services domain[C]// CHERFI S, PERINI A, NURCAN S. Research Challenges in Information Science: Lecture Notes in Business Information Processing. Cham: Springer International Publishing, 2021. 55. DOI: https://doi.org/10.1007/978-3-030-75018-3_4.
    [27] OpenStreetMap.org. Map data [DB/OL]. [2021-05-20]. https://www.openstreetmap.org.
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WEGENER Jan, VAN PUTTEN Sebastiaan, NEUBECK Jens, WAGNER Andreas.数据挖掘推动客户数据驱动车辆的开发进程[J].同济大学学报(自然科学版),2021,49(S1):1~10

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  • Received:November 30,2021
  • Online: February 28,2023
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