Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

verfasst von : Xueyuan Gong, Simon Fong, Yain-Whar Si, Robert P. Biuk-Aghai, Raymond K. Wong, Athanasios V. Vasilakos

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Biofeedback signals are important elements in critical care applications, such as monitoring ECG data of a patient, discovering patterns from large amount of ECG data sets, detecting outliers from ECG data, etc. Because the signal data update continuously and the sampling rates may be different, time-series data stream is harder to be dealt with compared to traditional historical time-series data. For the pattern discovery problem on time-series streams, Toyoda proposed the CrossMatch (CM) approach to discover the patterns between two time-series data streams (sequences), which requires only O(n) time per data update, where n is the length of one sequence. CM, however, does not support normalization, which is required for some kinds of sequences (e.g. EEG data, ECG data). Therefore, we propose a normalized-CrossMatch approach (NCM) that extends CM to enforce normalization while maintaining the same performance capabilities.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Image Segmentation with Superpixel Based Covariance Descriptor
Nächstes Kapitel Event Prediction in Healthcare Analytics: Beyond Prediction Accuracy
Literatur
1.
Aach, J., Church, G.M.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001)CrossRef
2.
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)CrossRef
3.
Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: Proceedings of the 16th Conference on Information and Knowledge Management (CIKM), pp. 811–820 (2007)
4.
Bu, Y., Chen, L., Fu, A.W.-C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 159–168 (2009)
5.
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 493–498 (2003)
6.
Gong, X., Si, Y.-W., Fong, S., Mohammed, S.: NSPRING: normalization-supported SPRING for subsequence matching on time series streams. In: IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 373–378 (2014)
7.
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23, 67–72 (1975)CrossRef
8.
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Discov. 7, 349–371 (2003)MathSciNetCrossRef
9.
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005)CrossRef
10.
Keogh, E., Wei, L., Xi, X., Vlachos, M., Lee, S.-H., Protopapas, P.: Supporting exact indexing of arbitrarily rotated shapes and periodic time series under euclidean and warping distance measures. Int. J. Very Large Data Bases 18, 611–630 (2009)CrossRef
11.
Li, Y., Leong Hou, U., Yiu, M.L., Gong, Z.: Quick-motif: an efficient and scalable framework for exact motif discovery. In: Proceedings of the International Conference on Data Engineering (ICDE) (2014)
12.
Mueen, A.: Enumeration of time series motifs of all lengths. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 547–556 (2013)
13.
Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, M.B.: Exact discovery of time series motifs. In: SDM, pp. 473–484 (2009)
14.
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 262–270 (2012)
15.
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)CrossRefMATH
16.
Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream monitoring under the time warping distance. In: IEEE 23rd International Conference on Data Engineering (ICDE), pp. 1046–1055 (2007)
17.
Toyoda, M., Sakurai, Y.: Discovery of cross-similarity in data streams. In: IEEE 26th International Conference on Data Engineering (ICDE), pp. 101–104 (2010)
18.
Toyoda, M., Sakurai, Y., Ichikawa, T.: Identifying similar subsequences in data streams. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 210–224. Springer, Heidelberg (2008)CrossRef
19.
Toyoda, M., Sakurai, Y., Ishikawa, Y.: Pattern discovery in data streams under the time warping distance. VLDB J. 22, 295–318 (2013)CrossRef
20.
Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings of the 14th International Conference on Data Engineering (ICDE), pp. 201–208 (1998)
Metadaten
Titel
Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals
verfasst von
Xueyuan Gong
Simon Fong
Yain-Whar Si
Robert P. Biuk-Aghai
Raymond K. Wong
Athanasios V. Vasilakos
Copyright-Jahr
2016
Buch
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42996-0_14

Premium Partner

    Bildnachweise