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Aligning Machine Learning for the Lambda Architecture

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Luukkonen, Olli
dc.contributor.author Nair, Visakh
dc.date.accessioned 2015-11-19T10:59:13Z
dc.date.available 2015-11-19T10:59:13Z
dc.date.issued 2015-10-19
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/18665
dc.description.abstract We live in the era of Big Data. Web logs, internet media, social networks and sensor devices are generating petabytes of data every day. Traditional data storage and analysis methodologies have become insufficient to handle the rapidly increasing amount of data. The development of complex machine learning techniques has led to the proliferation of advanced analytics solutions. This has led to a paradigm shift in the way we store, process and analyze data. The avalanche of data has led to the development of numerous platforms and solutions satisfying various business analytics needs. It becomes imperative for the business practitioners and consultants to choose the right solution which can provide the best performance and maximize the utilization of the data available. In this thesis, we develop and implement a Big Data architectural framework called the Lambda Architecture. It consists of three major components, namely batch data processing, realtime data processing and a reporting layer. We develop and implement analytics use cases using machine learning techniques for each of these layers. The objective is to build a system in which the data storage and processing platforms and the analytics frameworks can be integrated seamlessly. en
dc.format.extent 61
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Aligning Machine Learning for the Lambda Architecture en
dc.type G2 Pro gradu, diplomityö en
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword machine learning en
dc.subject.keyword big data en
dc.subject.keyword data mining en
dc.subject.keyword lambda architecture en
dc.subject.keyword internet of things en
dc.identifier.urn URN:NBN:fi:aalto-201511205222
dc.programme.major Software Engineering and Business en
dc.programme.mcode T3003 en
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Heljanko, Keijo
dc.programme Master’s Programme in Machine Learning and Data Mining (Macadamia) en
local.aalto.openaccess yes
dc.rights.accesslevel openAccess
local.aalto.idinssi 52562
dc.type.publication masterThesis
dc.type.okm G2 Pro gradu, diplomityö

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