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A deep learning-based CEP rule extraction framework for IoT data

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Abstract

With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined rules mostly created by experts. However, especially in heterogeneous IoT environments, it may become extremely hard to define these rules manually due to complex and dynamic nature of data. Defining rules requires accurate knowledge of relevant events by utilizing temporal dependencies and relations among attributes of events. Therefore, there is a need for an automatic rule extraction system which is capable of generating rules automatically from unlabeled IoT data. This paper proposes a generalized framework for automatic CEP rule extraction with the help of deep learning (DL) methods. The proposed framework contains two main phases which are data labeling and automatic rule extraction phases. We compare several DL methods with each other and regression-based methods to evaluate the proposed framework. In addition, several rule mining methods are employed in the automatic rule extraction phase in a comparative manner. The reconstruction error and prediction success rate of our framework are evaluated using an air quality dataset collected from a smart city application. The results indicate that the proposed framework is able to generate meaningful and accurate rules for unlabeled IoT data.

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References

  1. Starks F, Goebel V, Kristiansen S, Plagemann T (2018) Mobile distributed complex event processing–Ubi Sumus? Quo vadimus?. Springer, pp 147–180

  2. Monnier O (2013) A smarter grid with the internet of things. Texas instruments, pp 1–11

  3. Gökalp MO, Koçyiğit A, Eren PE (2019) A visual programming framework for distributed internet of things centric complex event processing. Comput Electr Eng 74:581–604

    Article  Google Scholar 

  4. Kawashima H, Kitagawa H, Li X (2010) Complex event processing over uncertain data streams. In: International conference on P2P. Parallel, grid, cloud and internet computing. IEEE, pp 521–526

  5. Mahmood N, Pasha MK, Pasha KA (2017) Survey of applications of complex event processing (cep) in health domain. Sukkur IBA J Computi Math Sci 1(2):88–94

    Article  Google Scholar 

  6. Alias C, Rawet VL, Neto HXR, Reymão JdEN (2016) Investigating into the prevalence of complex event processing and predictive analytics in the transportation and logistics sector: initial findings from scientific literature. In: MCIS, p 2

  7. Mijović V, Tomašević N, Janev V, Stanojević M, Vraneš S (2019) Emergency management in critical infrastructures: a complex-event-processing paradigm. J Syst Sci Syst Eng 28(1):37–62

    Article  Google Scholar 

  8. Robins D (2010) Complex event processing. In: 2nd international workshop on education technology and computer science. Wuhan, Citeseer, pp 1–10

  9. Cugola G, Margara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv (CSUR) 44(3):1–62

    Article  Google Scholar 

  10. Bruns R, Dunkel J, Offel N (2019) Learning of complex event processing rules with genetic programming. Expert Syst Appl 129:186–199

    Article  Google Scholar 

  11. Pielmeier J, Braunreuther S, Reinhart G (2018) Approach for defining rules in the context of complex event processing. Procedia CIRP 67:8–12

    Article  Google Scholar 

  12. Ahmad J, Farman H, Jan Z (2019) Deep learning methods and applications. In: Deep learning: convergence to big data analytics. Springer, pp 31–42

  13. Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim KJ (2017) A survey of deep learning-based network anomaly detection. Cluster Comput 1–13

  14. Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954–21961

    Article  Google Scholar 

  15. Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55–75

    Article  Google Scholar 

  16. Kök , Şimşek MU, Özdemir S (2017) A deep learning model for air quality prediction in smart cities. In: 2017 IEEE international conference on big data (big data). IEEE, pp 1983–1990

  17. Consortium T (2016) Citypulse annual report. The CityPulse Consortium

  18. Motlagh NH, Zaidan MA, Fung PL, Li X, Matsumi Y, Petäjä T, Kulmala M, Tarkoma S, Hussein T (2020) Low-cost air quality sensing process: validation by indoor-outdoor measurements. In: 15th IEEE conference on industrial electronics and applications (ICIEA2020). IEEE

  19. Fernandes G, Rodrigues JJ, Carvalho LF, Al-Muhtadi JF, Proença ML (2019) A comprehensive survey on network anomaly detection. Telecommun Syst 70(3):447–489

    Article  Google Scholar 

  20. Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. arXiv preprint arXiv:190103407

  21. Canizo M, Triguero I, Conde A, Onieva E (2019) Multi-head cnn-rnn for multi-time series anomaly detection: an industrial case study. Neurocomputing 363:246–260

    Article  Google Scholar 

  22. Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:160700148

  23. Malhotra P, Vig L, Shroff G, Agarwal P (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings, Presses universitaires de Louvain, vol 89

  24. Chauhan S, Vig L (2015) Anomaly detection in ECG time signals via deep long short-term memory networks. In: 2015 IEEE international conference on data science and advanced analytics (DSAA). IEEE, pp 1–7

  25. Zhang C, Song D, Chen Y, Feng X, Lumezanu C, Cheng W, Ni J, Zong B, Chen H, Chawla NV (2019) A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proc AAAI Conf Artif Intell 33:1409–1416

    Google Scholar 

  26. Munir M, Siddiqui SA, Dengel A, Ahmed S (2018) Deepant: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7:1991–2005

    Article  Google Scholar 

  27. Cugola G, Margara A, Matteucci M, Tamburrelli G (2015) Introducing uncertainty in complex event processing: model, implementation, and validation. Computing 97(2):103–144

    Article  Google Scholar 

  28. Schultz-Møller NP, Migliavacca M, Pietzuch P (2009) Distributed complex event processing with query rewriting. In: Proceedings of the 3rd ACM international conference on distributed event-based systems, pp 1–12

  29. Boubeta-Puig J, Ortiz G, Medina-Bulo I (2014) A model-driven approach for facilitating user-friendly design of complex event patterns. Expert Syst Appl 41(2):445–456

    Article  Google Scholar 

  30. Boubeta-Puig J, Ortiz G, Medina-Bulo I (2015) Model4cep: graphical domain-specific modeling languages for cep domains and event patterns. Expert Syst Appl 42(21):8095–8110

    Article  Google Scholar 

  31. Petersen E, RLICT MAT, Maag S, Yamga T (2018) An unsupervised rule generation approach for online complex event processing. In: 2018 IEEE 17th international symposium on network computing and applications (NCA). IEEE, pp 1–8

  32. Margara A, Cugola G, Tamburrelli G (2014) Learning from the past: automated rule generation for complex event processing. In: Proceedings of the 8th ACM international conference on distributed event-based systems, pp 47–58

  33. Hasan A, Teymourian K, Paschke A (2015) Probabilistic event pattern discovery. In: International symposium on rules and rule markup languages for the semantic web. Springer, pp 241–257

  34. George L (2015) Event pattern mining for smart environments. In: International SDL forum. Springer, pp 42–45

  35. Lee OJ, Jung JE (2017) Sequence clustering-based automated rule generation for adaptive complex event processing. Future Gener Comput Syst 66:100–109

    Article  Google Scholar 

  36. Mutschler C, Philippsen M (2012) Learning event detection rules with noise hidden markov models. In: 2012 NASA/ESA conference on adaptive hardware and systems (AHS). IEEE, pp 159–166

  37. Mehdiyev N, Krumeich J, Enke D, Werth D, Loos P (2015) Determination of rule patterns in complex event processing using machine learning techniques. Procedia Comput Sci 61:395–401

    Article  Google Scholar 

  38. Mousheimish R, Taher Y, Zeitouni K (2017) Automatic learning of predictive CEP rules: bridging the gap between data mining and complex event processing. In: Proceedings of the 11th ACM international conference on distributed and event-based systems, pp 158–169

  39. Frömmgen A, Rehner R, Lehn M, Buchmann A (2015) Fossa: learning ECA rules for adaptive distributed systems. In: 2015 IEEE international conference on autonomic computing. IEEE, pp 207–210

  40. Ban T, Zhang R, Pang S, Sarrafzadeh A, Inoue D (2013) Referential KNN regression for financial time series forecasting. In: International conference on neural information processing. Springer, pp 601–608

  41. Wu H, Cai Y, Wu Y, Zhong R, Li Q, Zheng J, Lin D, Li Y (2017) Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. In: Bioscience trends

  42. Kane MJ, Price N, Scotch M, Rabinowitz P (2014) Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformat 15(1):276

    Article  Google Scholar 

  43. Tang L, Pan H, Yao Y (2018) Pank-a financial time series prediction model integrating principal component analysis, affinity propagation clustering and nested k-nearest neighbor regression. J Interdiscip Math 21(3):717–728

    Article  Google Scholar 

  44. Martínez F, Frías MP, Pérez-Godoy MD, Rivera AJ (2018) Dealing with seasonality by narrowing the training set in time series forecasting with KNN. Expert Syst Appl 103:38–48

    Article  Google Scholar 

  45. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  46. Wang S, Sun S, Li Z, Zhang R, Xu J (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol 13(1):e1005324

    Article  Google Scholar 

  47. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  48. Längkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11–24

    Article  Google Scholar 

  49. Lim B, Zohren S (2020) Time series forecasting with deep learning: a survey. arXiv preprint arXiv:200413408

  50. Zhang YF, Thorburn PJ, Xiang W, Fitch P (2019) SSIMA-deep learning approach for recovering missing time series sensor data. IEEE Internet Things J 6(4):6618–6628

    Article  Google Scholar 

  51. Fry C, Brundage M (2019) The m4 forecasting competition-a practitioner’s view

  52. Salinas D, Flunkert V, Gasthaus J, Januschowski T (2020) Deepar: probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 36(3):1181–1191

    Article  Google Scholar 

  53. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 37–49

  54. Shickel B, Tighe PJ, Bihorac A, Rashidi P (2017) Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Informat 22(5):1589–1604

    Article  Google Scholar 

  55. Druzhkov P, Kustikova V (2016) A survey of deep learning methods and software tools for image classification and object detection. Pattern Recognit Image Anal 26(1):9–15

    Article  Google Scholar 

  56. Li T, Zhang Z, Chen H (2019) Predicting the combustion state of rotary kilns using a convolutional recurrent neural network. J Process Control 84:207–214

    Article  Google Scholar 

  57. Gupta L, McAvoy M (2000) Investigating the prediction capabilities of the simple recurrent neural network on real temporal sequences. Pattern Recogn 33(12):2075–2081

    Article  Google Scholar 

  58. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  59. Bontemps L, McDermott J, Le-Khac NA, et al (2016) Collective anomaly detection based on long short-term memory recurrent neural networks. In: International conference on future data and security engineering. Springer, pp 141–152

  60. Xingjian S, Chen Z, Wang H, Yeung DY, Wong WK, Woo Wc (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp. 802–810

  61. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31(7):2727–2740

    Article  Google Scholar 

  62. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Empirical methods in natural language processing (EMNLP)

  63. Dey R, Salemt FM (2017) Gate-variants of gated recurrent unit (GRU) neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 1597–1600

  64. Zhao R, Wang D, Yan R, Mao K, Shen F, Wang J (2017a) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Ind Electron 65(2):1539–1548

    Article  Google Scholar 

  65. Zhao B, Lu H, Chen S, Liu J, Wu D (2017b) Convolutional neural networks for time series classification. J Syst Eng Electron 28(1):162–169

    Article  Google Scholar 

  66. Mittal S (2018) A survey of FPGA-based accelerators for convolutional neural networks. In: Neural computing and applications, pp 1–31

  67. Pan H, He X, Tang S, Meng F (2018) An improved bearing fault diagnosis method using one-dimensional CNN and LSTM. J Mech Eng 64(7–8):443–452

    Google Scholar 

  68. Masci J, Meier U, Ciresan D, Schmidhuber J, Fricout G (2012) Steel defect classification with max-pooling convolutional neural networks. In: The 2012 international joint conference on neural networks (IJCNN). IEEE, pp 1–6

  69. Mahajan A, Ganpati A (2014) Performance evaluation of rule based classification algorithms. Int J Adv Res Comput Eng Technol (IJARCET) 3(10):3546–3550

    Google Scholar 

  70. MeeraGandhi G, Appavoo K, Srivasta S (2010) Effective network intrusion detection using classifiers decision trees and decision rules. Int J Adv Netw Appl 2

  71. Panda M, Patra MR (2009) Ensembling rule based classifiers for detecting network intrusions. In: 2009 international conference on advances in recent technologies in communication and computing. IEEE, pp 19–22

  72. Veeralakshmi V, Ramyachitra D (2015) Ripple down rule learner (RIDOR) classifier for iris dataset. Issues 1(1):79–85

    Google Scholar 

  73. Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–90

    Article  Google Scholar 

  74. Devasena CL, Sumathi T, Gomathi V, Hemalatha M (2011) Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring Int J Man Mach Interface 1(Special Issue Inaugural Special Issue):05–09

    Google Scholar 

  75. Hühn J, Hüllermeier E (2009) Furia: an algorithm for unordered fuzzy rule induction. Data Min Knowl Disc 19(3):293–319

    Article  MathSciNet  Google Scholar 

  76. Braei M, Wagner S (2020) Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:200400433

  77. Kök I, Özdemir S (2021) Deepmdp: a novel deep-learning-based missing data prediction protocol for IOT. IEEE Internet Things J 8(1):232–243. https://doi.org/10.1109/JIOT.2020.3003922

    Article  Google Scholar 

  78. Kondeti PK, Ravi K, Mutheneni SR, Kadiri MR, Kumaraswamy S, Vadlamani R, Upadhyayula SM (2019) Applications of machine learning techniques to predict filariasis using socio-economic factors. Epidemiol Infect 147

  79. Koklu M, Kahramanli H, Allahverdi N et al (2015) Applications of rule based classification techniques for thoracic surgery. In: Management, knowledge and learning-joint international conference 2015-technology, innovation and industrial management TIIM, pp 1991–1998

  80. Mohamed WNHW, Salleh MNM, Omar AH (2012) A comparative study of reduced error pruning method in decision tree algorithms. In: 2012 IEEE International conference on control system, computing and engineering. IEEE, pp 392–397

  81. Ali MI, Gao F, Mileo A (2015) Citybench: a configurable benchmark to evaluate RSP engines using smart city datasets. In: International semantic web conference. Springer, pp 374–389

  82. Dataset ICP (2020) Download link. http://iot.ee.surrey.ac.uk:8080/. Accessed 3 Dec 2020

  83. An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, vol 2(1)

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  1. Department of Computer Engineering, Gazi University, Maltepe, Ankara, Turkey

    Mehmet Ulvi Simsek & Feyza Yildirim Okay

  2. Department of Computer Engineering, Hacettepe University, Beytepe, Ankara, Turkey

    Suat Ozdemir

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  1. Mehmet Ulvi Simsek
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Simsek, M.U., Yildirim Okay, F. & Ozdemir, S. A deep learning-based CEP rule extraction framework for IoT data. J Supercomput 77, 8563–8592 (2021). https://doi.org/10.1007/s11227-020-03603-5

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