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Towards Adjusting Mobile Devices to User’s Behaviour

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6904)

Abstract

Mobile devices are a special class of resource-constrained embedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained resources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Time-consuming operations have to be started early on. For instance, loading files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system’s operation. Our goal is to adjust such strategies on the basis of logged system data. Optimization is then achieved by predicting an application’s behavior based on facts learned from earlier runs on the same system. In this paper, we analyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices.

Keywords

  • Mining system calls
  • ubiquitous knowledge discovery

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References

  1. Bockermann, C., Apel, M., Meier, M.: Learning sql for database intrusion detection using context-sensitive modelling. In: Proc. 6th Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 196–205. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  2. Bovet, D., Cesati, M.: Understanding the Linux Kernel, 3rd edn. O’Reilly & Associates, Inc., Sebastopol (2005)

    Google Scholar 

  3. Bucy, J.S., Schindler, J., Schlosser, S.W., Ganger, G.R.: The disksim simulation environment version 4.0 reference manual. Tech. Rep. CMU-PDL-08-101, Carnegie Mellon University (May 2008)

    Google Scholar 

  4. Cantrill, B.M., Shapiro, M.W., Leventhal, A.H.: Dynamic instrumentation of production systems. In: Proc. of USENIX ATEC 2004. USENIX, Berkeley (2004)

    Google Scholar 

  5. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Finding hierarchical heavy hitters in data streams. In: VLDB 2003: Proceedings of the 29th International Conference on Very Large Data Bases, pp. 464–475. VLDB Endowment (2003)

    Google Scholar 

  6. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Diamond in the rough: finding hierarchical heavy hitters in multi-dimensional data. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 155–166. ACM, New York (2004)

    CrossRef  Google Scholar 

  7. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Finding hierarchical heavy hitters in streaming data. ACM Trans. Knowl. Discov. Data 1(4), 1–48 (2008)

    CrossRef  Google Scholar 

  8. Domingos, P., Pazzani, M.: Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: Machine Learning, pp. 105–112. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  9. Eigler, F., Hat, R.: Problem solving with systemtap. In: Proceedings of the Ottawa Linux Symposium, vol. 2006 (2006)

    Google Scholar 

  10. Frank, A., Asuncion, A.: UCI machine learning repository (2010)

    Google Scholar 

  11. Gupta, K., Nath, B., Ramamohanarao, K.: Conditional random fields for intrusion detection. In: 21st Intl. Conf. on Adv. Information Netw. and Appl., pp. 203–208 (2007)

    Google Scholar 

  12. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning, corrected edn. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  13. Huang, J., Lu, J., Ling, L.C.X.: Comparing naive bayes, decision trees, and svm with auc and accuracy. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 553–556. IEEE Computer Society, Los Alamitos (2003)

    Google Scholar 

  14. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, pp. 282–289 (2001)

    Google Scholar 

  15. Lohmann, D., Hofer, W., Schröder-Preikschat, W., Streicher, J., Spinczyk, O.: CiAO: An aspect-oriented operating-system family for resource-constrained embedded systems. In: Proc. of USENIX ATEC. USENIX, Berkeley (2009)

    Google Scholar 

  16. Malouf, R.: A comparison of algorithms for maximum entropy parameter estimation. In: COLING-02: Proceedings of the 6th Conference on Natural Language Learning, pp. 1–7. Association for Computational Linguistics, Morristown (2002)

    Google Scholar 

  17. Nocedal, J.: Updating quasi-newton matrices with limited storage. Mathematics of Computation 35(151), 773–782 (1980)

    MathSciNet  CrossRef  MATH  Google Scholar 

  18. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)

    CrossRef  Google Scholar 

  19. Schraudolph, N.N., Graepel, T.: Conjugate directions for stochastic gradient descent. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1351–1358. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  20. Schraudolph, N.N., Yu, J., Günter, S.: A stochastic quasi-Newton method for online convex optimization. In: Meila, M., Shen, X. (eds.) Proc. 11th Intl. Conf. Artificial Intelligence and Statistics (AIstats). Workshop and Conference Proceedings, jmlr, San Juan, Puerto Rico, vol. 2, pp. 436–443 (2007)

    Google Scholar 

  21. Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: NAACL 2003: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 134–141. Association for Computational Linguistics, Morristown (2003)

    Google Scholar 

  22. Silberschatz, A., Galvin, P.B., Gagne, G.: Operating System Concepts. Wiley Publishing, Chichester (2010)

    MATH  Google Scholar 

  23. Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields for Relational Learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, MIT Press, Cambridge (2007)

    Google Scholar 

  24. Tartler, R., Lohmann, D., Schröder-Preikschat, W., Spinczyk, O.: Dynamic AspectC++: Generic advice at any time. In: The 8th Int. Conf. on Software Methodologies, Tools and Techniques, IOS Press, Prague (2009) (to appear)

    Google Scholar 

  25. Tian, S., Mu, S., Yin, C.: Sequence-similarity kernels for SVMs to detect anomalies in system calls. Neurocomput. 70(4-6), 859–866 (2007)

    CrossRef  Google Scholar 

  26. Timm, C., Gelenberg, A., Weichert, F., Marwedel, P.: Reducing the Energy Consumption of Embedded Systems by Integrating General Purpose GPUs. Tech. Rep. 829, Technische Universität Dortmund, Fakultät für Informatik (2010)

    Google Scholar 

  27. Vishwanathan, S.V.N., Schraudolph, N.N., Schmidt, M.W., Murphy, K.P.: Accelerated training of conditional random fields with stochastic gradient methods. In: ICML 2006: Proceedings of the 23rd International Conference on Machine Learning, pp. 969–976. ACM, New York (2006)

    Google Scholar 

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Fricke, P. et al. (2011). Towards Adjusting Mobile Devices to User’s Behaviour. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds) Analysis of Social Media and Ubiquitous Data. MUSE MSM 2010 2010. Lecture Notes in Computer Science(), vol 6904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23599-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-23599-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23598-6

  • Online ISBN: 978-3-642-23599-3

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