An approach to Machine Learning with Big Data

Show simple item record

dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Tietojenkäsittelytieteen laitos fi
dc.contributor.author Peltonen, Ella fi
dc.date.accessioned 2013-10-02T12:00:23Z
dc.date.available 2013-10-02T12:00:23Z
dc.date.issued 2013-10-02
dc.identifier.uri http://hdl.handle.net/10138/40924
dc.description.abstract Cloud computing offers important resources, performance, and services nowadays when it has became popular to collect, store and analyze large data sets. This thesis builds on Berkeley Data Analysis Stack (BDAS) as a cloud computing environment designed for Big Data handling and analysis. Especially two parts of the BDAS, the cluster resource manager Mesos and the distribution manager Spark will be introduced. They offer important features, such as efficiency, multi-tenancy, and fault tolerance, for cloud computing. The Spark system expands MapReduce, the well-known cloud computing paradigm. Machine learning algorithms can predict trends and anomalies of large data sets. This thesis will present one of them, a distributed decision tree algorithm, implemented on the Spark system. As an example case, the decision tree will be used on the versatile energy consumption data from mobile devices, such as smart phones and tablets, of the Carat project. The data consists of information about the usage of the device, such as which applications have been running, network connections, battery temperatures, and screen brightness, for example. The decision tree aims to find chains of data features that might lead to energy consumption anomalies. Results of the analysis can be used to advise users on how to improve their battery life. This thesis will present selected analysis results together with advantages and disadvantages of the decision tree analysis. fi
dc.language.iso en fi
dc.title An approach to Machine Learning with Big Data fi
dc.type.ontasot Pro gradu -työ fi
dc.subject.discipline Tietojenkäsittelytiede fi

Files in this item

Total number of downloads: Loading...

Files Size Format View
prograduepeltonen.pdf 1.131Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record