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@article{144148,
        author = {J.Sivaranjani and A.Neela Madheswari},
        title = {An Efficient Methodology of Motif Discovery for Massive Time Series in Health Care Domain},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {7},
        pages = {154-159},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144148},
        abstract = {Map Reduce is the technique widely used to improve the speed and accuracy of finding motifs in time series data. The traditional methods and algorithms of motif discovery in time series data, which are both time consuming as well as computationally expensive. However, the problem of motif discovery in healthcare domain is defined in a much more lucid terms based on the analytical pattern useful for the practitioners. In order to overcome the drawbacks of the present motif discovery methods, our system propose a Motif Discovery method for Large-scale time series data (MDLats) by combining the advantages of both exact and approximate methods. },
        keywords = {Motif, Map reduce, Symbolic aggregate approximation, Brute force.},
        month = {},
        }