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Author:Virtanen, Markus M.
Title:Predictive analytics for improved problem management
Ongelmanhallinnan parantaminen ennustavalla analytiikalla
Publication type:Master's thesis
Publication year:2014
Pages:48 s. + liitt. 2 s.      Language:   eng
Department/School:Perustieteiden korkeakoulu
Main subject:Ohjelmistotekniikka   (T-220)
Supervisor:Heljanko, Keijo
Instructor:Manninen, Esa
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201405131812
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto     | Archive
Keywords:data analysis
predictive analytics
geostatistics
big data
machine learning
random forest
Hadoop
Hive
R
data-analyysi
ennakoiva analytiikka
geostatistiikka
big data
koneoppiminen
random forest
Hadoop
Hive
R
Abstract (eng):This Thesis studies the possible data analysis methods to improve the problem management service at Elisa corporation.
The primary objective is to find and evaluate appropriate data analysis methods to understand the underlying problem causes and to develop predictive models from hypotheses and data, so that the problems could be prevented or minimized before the customer feels she or he has to contact the customer service.
Apache Hadoop framework and R programming language have been integrated to make a linearly scalable analytics process possible.
Customer data is first imported to research computer so that there is no need to execute analytical queries on the production databases.
The analytics phase is divided into two parts both helping with data-based decision making.
At first, exploratory data analysis is used to find valuable information from the historical data with the help of textual data summaries and visualizations.
After that, when there is cleaned data and a good understanding has been obtained of its behaviour, predictive analytics is included to make forecasts about the future behaviour.
Out of all the evaluated machine learning algorithms for the given task, the Random Forest algorithm had the best performance.
The final tuned model predicted the right outcome for approximately 77 % of problem tickets minimizing the need to send a repair technician if a customer's Internet connection works well.
Abstract (fin):Tämä diplomityö tutkii mahdollisia data-analyysimenetelmiä parantaakseen Elisan vianhallintapalvelua.
Ensisijainen tarkoitus on löytää ja evaluoida sopivia menetelmiä, jotka antavat ymmärrystä vikoihin, ja luoda ennustemalleja hypoteesien ja datan perusteella, joilla ongelmat saataisiin estettyä tai minimoitua ennen kuin asiakkaan tarvitsee ottaa yhteyttä asiakaspalveluun.
Apachen Hadoop ohjelmisto ja R-ohjelmointikieli integroitiin tehden lineaarisesti skaalautuva analyysiprosessi mahdolliseksi.
Asiakkaan data haettiin ensin tutkimustietokoneelle, jotta analyyttiset haut eivät kuormita tuotantotietokantoja.
Analyysivaihe on jaettu kahteen osaan, jotka molemmat auttavat dataan perustuvaa päätöksentekoa.
Ensin tutkivia analyysimenetelmiä käytetään löytämään arvokasta informaatiota historiallisesta datasta hyödyntäen tekstipohjaisia yhteenvetoja ja visuaalisia menetelmiä.
Seuraavaksi, kun data on siivottua ja siitä on hyvä käsitys, prediktiivistä analyytiikkaa käytetään luomaan ennusteita tulevasta.
Kaikista tarkastelluista koneoppimismalleista Random Forest algoritmilla oli paras suorituskyky annettuun ennustetehtävään.
Lopullinen optimoitu malli ennusti oikein 77 % kaikista ongelmatiketeistä vähentäen tarvetta lähettää korjaaja kentälle asiakkaan Internet-yhteyden toimiessa hyvin.
ED:2014-05-18
INSSI record number: 49044
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