@article{MAKHILLJEAS2020152019466,
    title = {Generating Analytics from Web LOG},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {20},
    pages = {3503-3508},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.3503.3508},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.3503.3508},
    author = {Vempaty,Srinivas and},
    keywords = {Big data,HDD’S,web log,MapReduce,Hadoop},
    abstract = {Modern engineering incorporates
clevertechnologies in all factors of our lives. Smart
technologies are generating terra bytes of log messages
every day to record their status. It is crucial to research
these log messages and present usable records (e.g.,
patterns) to directors, so as to manipulate and reveal those
technology. Patterns minimally represent large
corporations of log messages and enable the
administrators to do further analysis, along with anomaly
detection and event prediction. Although, patterns exist
typically in automatic log messages, spotting them in
large set of log messages from heterogeneous resources
without any prior information is a widespread
undertaking. We propose a big data using Hadoop that
extracts high pleasant styles for a given set of log
messages. Our approach is fast, memory efficient,
accurate and scalable. Hadoop is implemented in
map-reduce framework for disbursed platforms to
procedure hundreds of thousands of log messages in
seconds. It is a robust approach that works for
heterogeneous log messages generated in a wide style of
systems. Our technique exploits algorithmic techniques to
limit the computational over-head based totally on the
truth that log messages are continually routinely
generated. We examine the performance of Log-Mine on
hugeunits of log messages generated in commercial
applications. It has efficiently generated styles which
might be as exact as the styles generated by genuine and
un-scalable method whilst achieving a 500 per speedup.}
    }