G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things

Temporal data is one of the most common form of data in internet of things. Data from various sources such as sensors, smart phones, smart homes and smart vehicles in near future shall be of temporal nature with generated information recorded at different timestamps. We call all such data as time stamped temporal data. Discovery of temporal patterns and temporal trends from such temporal data requires new algorithms and methodologies as most of the existing algorithms do not reveal emerging, seasonal and diminishing patterns. In this paper, the objective is to find temporal patterns whose true prevalence values vary similar to a reference support time sequence satisfying subset constraints through estimating temporal pattern support bounds and using a novel fuzzy dissimilarity measure. We name our approach as G-SPAMINE. Experiment results show that G-SPAMINE out performs naive and sequential approaches and comparatively better to or atleast same as SPAMINE. In addition, the stamped temporal data adds extra level of privacy for temporal patterns in the IoT. A Novel fuzzy Gaussian dissimilarity measure which retains monotonicity property.Design of novel expressions to estimate maximum possible prevalence and minimum possible prevalence values of temporal association patterns.Defining standard deviation and threshold equation for proposed fuzzy Gaussian membership function.

[1]  T. Hong,et al.  Mining linguistic browsing patterns in the world wide web , 2002, Soft Comput..

[2]  Yuqing Wan,et al.  Effect of segmentation on financial time series pattern matching , 2016, Appl. Soft Comput..

[3]  Vincent S. Tseng,et al.  Prediction of user navigation patterns by mining the temporal web usage evolution , 2008, Soft Comput..

[4]  Kim-Kwang Raymond Choo,et al.  A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns , 2018, Soft Comput..

[5]  Vangipuram Radhakrishna,et al.  A Single Database Scan Approach for Mining Temporally Similar Association Patterns , 2016, ICTCS.

[6]  Shusaku Tsumoto,et al.  Mining similar temporal patterns in long time-series data and its application to medicine , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[7]  Noël Crespi,et al.  The Cluster Between Internet of Things and Social Networks: Review and Research Challenges , 2014, IEEE Internet of Things Journal.

[8]  Tzung-Pei Hong,et al.  Mining fuzzy temporal association rules by item lifespans , 2016, Appl. Soft Comput..

[9]  Vangipuram Radhakrishna,et al.  A computationally optimal approach for extracting similar temporal patterns , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[10]  Gilles Selleron,et al.  A soft computing-based approach to spatio-temporal prediction , 2009, Int. J. Approx. Reason..

[11]  Beihong Jin,et al.  Spatio-Temporal Events in the Internet of Things , 2010, 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.

[12]  Vangipuram Radhakrishna,et al.  A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams , 2015, ArXiv.

[13]  Donald H. Burn,et al.  Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques , 2016 .

[14]  Chih-Fong Tsai,et al.  A novel approach for mining cyclically repeated patterns with multiple minimum supports , 2015, Appl. Soft Comput..

[15]  Vassilis Christophides,et al.  Mining Usage Patterns in Residential Intranet of Things , 2016, ANT/SEIT.

[16]  Vangipuram Radhakrishna,et al.  Mining of outlier temporal patterns , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[17]  Vangipuram Radhakrishna,et al.  An Approach for Mining Similarity Profiled Temporal Association Patterns Using Gaussian Based Dissimilarity Measure , 2015 .

[18]  Bhaskar Krishnamachari,et al.  Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition , 2016, Comput. Commun..

[19]  Christian Borgelt,et al.  Soft Pattern Mining in Neuroscience , 2012, SMPS.

[20]  Vangipuram Radhakrishna,et al.  An Approach for Mining Similar Temporal Association Patterns in Single Database Scan , 2016 .

[21]  J. S. Yoo.  Temporal Data Mining: Similarity-Profiled Association Pattern , 2012 .

[22]  Shadi Aljawarneh,et al.  A similarity measure for outlier detection in timestamped temporal databases , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[23]  Beihong Jin,et al.  Specifying and detecting spatio-temporal events in the internet of things , 2013, Decis. Support Syst..

[24]  Suh-Yin Lee,et al.  Mining Temporal Patterns in Time Interval-Based Data , 2015, IEEE Transactions on Knowledge and Data Engineering.

[25]  Shadi A. Aljawarneh,et al.  A similarity measure for temporal pattern discovery in time series data generated by IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[26]  Ahmad Lotfi,et al.  Behavioural pattern identification and prediction in intelligent environments , 2013, Appl. Soft Comput..

[27]  Vlad Trifa,et al.  Building the Web of Things: With examples in Node.js and Raspberry Pi , 2016 .

[28]  Vangipuram Radhakrishna,et al.  Looking into the possibility of novel dissimilarity measure to discover similarity profiled temporal association patterns in IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[29]  Vangipuram Radhakrishna,et al.  A Survey on Temporal Databases and Data mining , 2015 .

[30]  V. Janaki,et al.  A novel approach to discover similar temporal association patterns in a single database scan , 2015, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[31]  Sally I. McClean,et al.  Learning temporal concepts from heterogeneous data sequences , 2003, Soft Comput..

[32]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[33]  Martine De Cock,et al.  Reasoning about fuzzy temporal information from the web: towards retrieval of historical events , 2010, Soft Comput..

[34]  V. Janaki,et al.  A Temporal Pattern Mining Based Approach for Intrusion Detection Using Similarity Measure , 2015 .

[35]  Manuel Díaz,et al.  State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing , 2016, J. Netw. Comput. Appl..

[36]  Vangipuram Radhakrishna,et al.  Mining Outlier Temporal Association Patterns , 2016, ICTCS.

[37]  Shashi Shekhar,et al.  Mining Temporal Association Patterns under a Similarity Constraint , 2008, SSDBM.

[38]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[39]  François Carrez,et al.  A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things , 2015, IEEE Internet of Things Journal.

[40]  Shashi Shekhar,et al.  Similarity-Profiled Temporal Association Mining , 2009, IEEE Transactions on Knowledge and Data Engineering.

[41]  Siobhán Clarke,et al.  Middleware for Internet of Things: A Survey , 2016, IEEE Internet of Things Journal.

[42]  Yeong-Chyi Lee,et al.  Actionable high-coherent-utility fuzzy itemset mining , 2014, Soft Comput..

[43]  Luigi Atzori,et al.  Trustworthiness Management in the Social Internet of Things , 2014, IEEE Transactions on Knowledge and Data Engineering.

[44]  Klaus Moessner,et al.  Neighbor Discovery for Opportunistic Networking in Internet of Things Scenarios: A Survey , 2015, IEEE Access.

[45]  Long Jin,et al.  Discovery of Temporal Frequent Patterns Using TFP-Tree , 2006, WAIM.

[46]  Shadi Aljawarneh,et al.  A computationally efficient approach for temporal pattern mining in IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[47]  Luigi Atzori,et al.  Friendship Selection in the Social Internet of Things: Challenges and Possible Strategies , 2015, IEEE Internet of Things Journal.