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VRKSHA: a novel tree structure for time-profiled temporal association mining

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Abstract

Mining association patterns from a time-stamped temporal database distributed over finite time slots is implicitly associated with task of scanning the input database. Finding supports of itemsets requires scanning the input database. Database scan can be performed in either snapshot or lattice-based approach. Sequential and SPAMINE methods for similarity-profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar are based on the snapshot database scan and lattice scan, respectively. Snapshot database scan involves scanning multi-time slot database time slot by time slot. The major limitation of Sequential method is the requirement to retain original temporal database in the disk for finding itemset support computations. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in the memory and also eliminates the need to retain database in memory. The basic idea is to generate a compressed time-stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference w.r.t each time slot and validating whether the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed Sequential approach is from the fact that it is a single database scan approach excluding the initial database scan performed for computing true supports of singleton items. VRKSHA overcomes the major limitation of retaining database in memory that is required by SPAMINE, G-SPAMINE, MASTER algorithms. Experiment results prove that computational time and memory consumed by VRKSHA are significantly very much better than by approaches such as Naïve, Sequential, SPAMINE, and G-SPAMINE. To the best of our survey and knowledge, VRKSHA is the pioneering work to introduce and propose a compressed tree-based data structure for mining similarity-profiled temporal association patterns in the area of time-profiled temporal association mining.

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References

  1. Radhakrishna V, Kumar PV, Janaki V (2015) A survey on temporal databases and data mining. In: Proceedings of the international conference on engineering and MIS 2015 (ICEMIS ‘15). ACM, New York. https://doi.org/10.1145/2832987.2833064

  2. Yoo JS, Shekhar S (2009) Similarity-profiled temporal association mining. IEEE Trans Knowl Data Eng 21(8):1147–1161

    Article  Google Scholar 

  3. Radhakrishna V, Aljawarneh SA, Kumar PV, Janaki V (2017) A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2017.03.016

    Article  Google Scholar 

  4. Yoo JS, Zhang P, Shekhar S (2005) Mining time-profiled associations: an extended abstract. In: Ho TB, Cheung D, Liu H (eds) Advances in knowledge discovery and data mining. PAKDD 2005. Lecture notes in computer science, vol 3518. Springer, Berlin

    Google Scholar 

  5. Yoo JS, Shekhar S (2008) Mining temporal association patterns under a similarity constraint. In: Proceedings of the 20th international conference on scientific and statistical database management, vol 17. Springer, Berlin, pp 401–417

  6. Aljawarneh SA, Radhakrishna V, Kumar PV, Janaki V (2017) G-SPAMINE: an approach to discover temporal association patterns and trends in internet of things. Future Gener Comput Syst 74:430–443. https://doi.org/10.1016/j.future.2017.01.013

    Article  Google Scholar 

  7. Radhakrishna V, Kumar PV, Janaki V (2017) SRIHASS—a similarity measure for discovery of hidden time profiled temporal associations. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5185-9

    Article  Google Scholar 

  8. Radhakrishna V, Aljawarneh SA, Veereswara Kumar P et al (2017) ASTRA—a novel interest measure for unearthing latent temporal associations and trends through extending basic Gaussian membership function. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5280-y

    Article  Google Scholar 

  9. Gupta M, Gao J, Aggarwal CC, Han J (2014) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 26(9):2250–2267. https://doi.org/10.1109/TKDE.2013.184

    Article  MATH  Google Scholar 

  10. Yoo JS (2012) Temporal data mining: similarity-profiled association pattern. In: Holmes DE, Jain LC (eds) Data mining: foundations and intelligent paradigms. Intelligent systems reference library, vol 23. Springer, Berlin

    Google Scholar 

  11. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C (eds) Proceedings of the 20th international conference on very large data bases (VLDB ‘94). Morgan Kaufmann Publishers Inc, San Francisco, pp 487–499

    Google Scholar 

  12. Srikant R, Agrawal R (1997) Mining generalized association rules. Future Gener Comput Syst 13((2–3)):161–180. https://doi.org/10.1016/s0167-739x(97)00019-8

    Article  Google Scholar 

  13. Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. Technical report. University of Rochester, Rochester

  14. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Peckman JM, Ram S, Franklin M (eds) Proceedings of the 1997 ACM SIGMOD international conference on management of data (SIGMOD ‘97). ACM, New York, pp 255–264. https://doi.org/10.1145/253260.253325

    Chapter  Google Scholar 

  15. Savasere A, Omiecinski E, Navathe SB (1995) An efficient algorithm for mining association rules in large databases. In: Dayal U, Gray PMD, Nishio S (eds) Proceedings of the 21th international conference on very large data bases (VLDB ‘95). Morgan Kaufmann Publishers Inc, San Francisco, pp 432–444

    Google Scholar 

  16. Park JS, Chen M-S, Yu PS (1995) An effective hash-based algorithm for mining association rules. In: Carey M, Schneider D (eds) Proceedings of the 1995 ACM SIGMOD international conference on management of data (SIGMOD ‘95). ACM, New York, pp 175–186. https://doi.org/10.1145/223784.2238

    Chapter  Google Scholar 

  17. Toivonen H (1996) Sampling large databases for association rules. In: Vijayaraman TM, Buchmann AP, Mohan C, Sarda NL (eds) Proceedings of the 22th international conference on very large data bases (VLDB ‘96). Morgan Kaufmann Publishers Inc, San Francisco, pp 134–145

    Google Scholar 

  18. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Widom J (ed) Proceedings of the 1996 ACM SIGMOD international conference on management of data (SIGMOD ‘96). ACM, New York, pp 1–12. https://doi.org/10.1145/233269.233311

    Chapter  Google Scholar 

  19. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Apers PMG, Bouzeghoub M, Gardarin G (eds) Proceedings of the 5th international conference on extending database technology: advances in database technology (EDBT ‘96). Springer, London, pp 3–17

    Google Scholar 

  20. Evfimievski A, Srikant R, Agrawal R, Gehrke J (2002) Privacy preserving mining of association rules. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘02). ACM, New York, pp 217–228

  21. Ale JM, Rossi GH (2000) An approach to discovering temporal association rules. In: Carroll J, Damiani E, Haddad H, Oppenheim D (eds) Proceedings of the 2000 ACM symposium on applied computing (SAC ‘00), vol 1. ACM, New York, pp 294–300

    Chapter  Google Scholar 

  22. de Amo S, Furtado DA (2007) First-order temporal pattern mining with regular expression constraints. Data Knowl Eng 62(3):401–420

    Article  Google Scholar 

  23. Guyet T, Quiniou R (2008) Mining temporal patterns with quantitative intervals. In: Proceedings of the 2008 IEEE international conference on data mining workshops (ICDMW ‘08). IEEE Computer Society, Washington, pp 218–227. https://doi.org/10.1109/icdmw.2008.16

  24. Batal I, Fradkin D, Harrison J, Moerchen F, Hauskrecht M (2012) Mining recent temporal patterns for event detection in multivariate time series data. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ‘12). ACM, New York, pp 280–288. https://doi.org/10.1145/2339530.2339578

  25. Huang C-K, Yang P-T, Hsieh K-Y (2018) Knowledge discovery of consensus and conflict interval-based temporal patterns. Knowl Based Syst 140(C):201–213. https://doi.org/10.1016/j.knosys.2017.11.002

    Article  Google Scholar 

  26. Jin L, Lee Y, Seo S, Ryu KH (2006) Discovery of temporal frequent patterns using TFP-Tree. In: Yu JX, Kitsuregawa M, Leong HV (eds) Proceedings of the 7th international conference on advances in web-age information management (WAIM ‘06). Springer, Berlin, pp 349–361. https://doi.org/10.1007/11775300_30

    Chapter  Google Scholar 

  27. Ke J, Zhan Y, Chen X, Wang M (2013) The retrieval of motion event by associations of temporal frequent pattern growth. Future Gener Comput Syst 29(1):442–450. https://doi.org/10.1016/j.future.2011.06.004

    Article  Google Scholar 

  28. Uday Kiran R, Kitsuregawa M, Krishna Reddy P (2016) Efficient discovery of periodic-frequent patterns in very large databases. J Syst Softw 112:110–121. https://doi.org/10.1016/j.jss.2015.10.035

    Article  Google Scholar 

  29. Uday Kiran R, Venkatesh JN, Toyoda M, Kitsuregawa M, Krishna Reddy P (2017) Discovering partial periodic-frequent patterns in a transactional database. J Syst Softw 125:170–182. https://doi.org/10.1016/j.jss.2016.11.035

    Article  Google Scholar 

  30. Jung JJ (2012) Constraint graph-based frequent pattern updating from temporal databases. Expert Syst Appl 39(3):3169–3173. https://doi.org/10.1016/j.eswa.2011.09.003

    Article  Google Scholar 

  31. Wang L, Meng J, Xu P, Peng K (2018) Mining temporal association rules with frequent itemsets tree. Appl Soft Comput 62:817–829. https://doi.org/10.1016/j.asoc.2017.09.013

    Article  Google Scholar 

  32. Radhakrishna V, Kumar PV, Janaki V (2015) A novel approach for mining similarity profiled temporal association patterns using venn diagrams. In: Proceedings of the international conference on engineering and MIS 2015 (ICEMIS’15). ACM, New York. https://doi.org/10.1145/2832987.2833071

  33. Radhakrishna V, Kumar PV, Janaki V (2016) A computationally efficient approach for mining similar temporal patterns. In: Proceedings of the 22nd international conference on soft computing (MENDEL 2016) held in Brno, Czech Republic, vol 576. Advances in intelligent systems and computing. https://doi.org/10.1007/978-3-319-58088-3_19

  34. Radhakrishna V, Kumar PV, Janaki V (2016) Estimating prevalence bounds of patterns to discover similar temporal association patterns. In: Proceedings of the 22nd international conference on soft computing (MENDEL 2016) held in Brno, Czech Republic, vol 576. Advances in intelligent systems and computing. https://doi.org/10.1007/978-3-319-58088-3_20

  35. Cheruvu A, Radhakrishna V (2016) Estimating temporal pattern bounds using negative support computations. In: 2016 international conference on engineering and MIS (ICEMIS), Agadir, pp 1–4. https://doi.org/10.1109/icemis.2016.7745352

  36. Radhakrishna V, Kumar PV, Janaki V (2015) An approach for mining similarity profiled temporal association patterns using Gaussian based dissimilarity measure. In: Proceedings of the international conference on engineering and MIS 2015 (ICEMIS ‘15). ACM, New York. https://doi.org/10.1145/2832987.2833069

  37. Radhakrishna V, Aljawarneh SA, Kumar PV, Choo K-KR (2016) A novel fuzzy Gaussian-based dissimilarity measure for discovering similarity temporal association patterns. Soft Comput. https://doi.org/10.1007/s00500-016-2445-y

    Article  Google Scholar 

  38. Chen YC, Peng WC, Lee SY (2015) Mining temporal patterns in time interval-based data. IEEE Trans Knowl Data Eng 27(12):3318–3331. https://doi.org/10.1109/TKDE.2015.2454515

    Article  Google Scholar 

  39. Aljawarneh SA, Vangipuram R (2018) GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things. J Supercomput. https://doi.org/10.1007/s11227-018-2397-3

    Article  Google Scholar 

  40. Radhakrishna V, Kumar PV, Aljawarneh SA, Janaki V (2017) Design and analysis of a novel temporal dissimilarity measure using Gaussian membership function. In: 2017 international conference on engineering and MIS (ICEMIS), Monastir, pp 1–5. https://doi.org/10.1109/icemis.2017.8273098

  41. Aljawarneh SA, Krishna VR, Cheruvu A (2017) Finding similar patterns in time stamped temporal datasets. In: 2017 international conference on engineering and MIS (ICEMIS), pp 1–5. ISSN: 2575-1328

  42. Aljawarneh SA, Radhakrishna V, Cheruvu A (2017) Extending the Gaussian membership function for finding similarity between temporal patterns. In: 2017 international conference on engineering and MIS (ICEMIS), pp 1–6. ISSN: 2575-1328

  43. Aljawarneh S, Radhakrishna V, Kumar PV, Janaki V (2016) A similarity measure for temporal pattern discovery in time series data generated by IoT. In: 2016 international conference on engineering and MIS (ICEMIS), Agadir, pp 1–4

  44. Radhakrishna V, Kumar PV, Janaki V (2016) Mining of outlier temporal patterns. In: 2016 international conference on engineering and MIS (ICEMIS), Agadir, pp 1–6

  45. Radhakrishna V, Kumar PV, Janaki V (2017) Design and analysis of similarity measure for discovering similarity profiled temporal association patterns. IADIS Int J Comput Sci Inf Syst 12(1):45–60. http://www.iadisportal.org/ijcsis/papers/2017200104.pdf

  46. Radhakrishna V, Kumar PV, Janaki V, Cheruvu A (2017) A dissimilarity measure for mining similar temporal association patterns. IADIS Int J Comput Sci Inf Syst 12(1):126–142. http://www.iadisportal.org/ijcsis/papers/2017200109.pdf

  47. Radhakrishna V, Kumar PV, Janaki V (2017) Normal distribution based similarity profiled temporal association pattern mining (N-SPAMINE). Database Syst J 7(3):22–33

    Google Scholar 

  48. Radhakrishna V, Kumar PV, Janaki V (2017) A novel similar temporal system call pattern mining for efficient intrusion detection. J Univers Comput Sci 22(4):475–493. https://doi.org/10.3217/jucs-022-04-0475

    Article  MathSciNet  Google Scholar 

  49. Radhakrishna V, Kumar PV, Janaki V (2016) Mining of outlier temporal patterns. In: 2016 international conference on engineering and MIS (ICEMIS), Agadir, pp 1–6. https://doi.org/10.1109/icemis.2016.7745343

  50. Radhakrishna V, Kumar PV, Janaki V, Aljawarneh S (2018) GANDIVA—time profiled temporal pattern tree. In: Proceedings of the fourth international conference on engineering and MIS 2018 (ICEMIS ‘18). ACM, New York. https://doi.org/10.1145/3234698.3234734

  51. Aljawarneh S, Radhakrishna V, Cheruvu A (2018) VRKSHA: a novel multi-tree based sequential approach for seasonal pattern mining. In: Proceedings of the fourth international conference on engineering and MIS 2018 (ICEMIS ‘18). ACM, New York

  52. Radhakrishna V, Aljawarneh S, Cheruvu A (2018) sequential approach for mining of temporal itemsets. In: Proceedings of the fourth international conference on engineering and MIS 2018 (ICEMIS ‘18). ACM, New York

  53. Radhakrishna V, Kumar PV, Janaki V (2018) Krishna Sudarsana: a Z-space similarity measure. In: Proceedings of the fourth international conference on engineering and MIS 2018 (ICEMIS ‘18). ACM, New York

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Authors and Affiliations

  1. Jordan University of Science and Technology, Ar Ramtha, Jordan

    Shadi A. Aljawarneh

  2. Department of Information Technology, VNR VJIET, Hyderabad, India

    V. Radhakrishna & Aravind Cheruvu

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  1. Shadi A. Aljawarneh
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  2. V. Radhakrishna
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  3. Aravind Cheruvu
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Correspondence to Shadi A. Aljawarneh.

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Aljawarneh, S.A., Radhakrishna, V. & Cheruvu, A. VRKSHA: a novel tree structure for time-profiled temporal association mining. Neural Comput & Applic 32, 16337–16365 (2020). https://doi.org/10.1007/s00521-018-3776-7

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