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Author:Haapamäki, Lauri
Title:Identifying business models in Networks
Liiketoimintamallien tunnistaminen verkostoista
Publication type:Master's thesis
Publication year:2009
Pages:109      Language:   eng
Department/School:Informaatio- ja luonnontieteiden tiedekunta
Main subject:Sovellettu matematiikka   (Mat-2)
Supervisor:Ehtamo, Harri
Instructor:Lehtinen, Hannele
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

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Location:P1 Ark Aalto  72   | Archive
Keywords:network
data mining
clustering
business model
network analysis
data analysis
klusterointi
data-analyysi
verkosto
bisnesmalli
liiketoimintamalli
verkostoanalyysi
tiedon louhinta
Abstract (eng): Describing complex real world problems like business is difficult.
With recent developments in computer capabilities, numerical methods have made more and more specified examination of networks feasible.
In this study we developed a methodology and tool box to dissect the internal structure of a large company.
We used networks to describe the socio professional network within a company i.e. the internal business model.

For this work we developed numerical algorithms for data examination, verification and clustering.
Our algorithm was based on the K-means algorithm which we then on further developed to handle fragmented datasets.
We also learned how extremely important step in the process data mining is.
Getting relevant data was very difficult and even after that pre processing and cleaning of the data is tidious work.
This required development algorithms for this study.

The study resulted in six identified reoccurring network structures which were then linked to business models.
We did an expert interview in order to validate the results.
The interview supported our conclusions.
We also looked at the data from different perspectives filtering the data in various ways which gave perspective.
No viewpoints contradicted our conclusions.

The process brought up several more questions and lines for further study.
The further development of clustering and filtering algorithms would be very promising.
Also using different data sets to validate the results more or to find new kinds of business models.
Abstract (fin): Monimutkaisten tosimaailman ilmiöiden kuvaaminen on vaikea ongelma.
Viimeisen viidentoista vuoden aikana tapahtunut kehitys tietotekniikassa on avannut ovet uudenlaiselle numeeriselle tutkimukselle ja mahdollistanut sosiaalisten verkostojen tutkimisen.
Tässä tutkimuksessa kehitimme työkalut ja metodologian bisnesmallien tutkimiselle sosiaalisten verkostojen avulla.
Tarkastelimme yhden suuren konsernin sisäistä projektitietokantaa, jonka avulla kuvasimme sen sisäistä bisnesmallia.

Bisnesmallien tutkimista varten kehitimme joukon algoritmeja datan tutkimiseen, verifioimiseen ja klusterointiin.
Klusterointialgoritmimme perustuu K-means-algoritmiin, josta jatkokehitimme oman version, joka kykenee paremmin pureutumaan hajanaiseen projektidataan.
Työn ohessa opimme kuinka suuri ja oleellinen osa datan louhiminen on.
Relevantin datan kaivaminen ja puhdistaminen on erittöain työolöastöa ja vaati merkittävän osan työajasta, ja sitä varten jouduttiin kehittämän joukko omia työkaluja.

Analyysin tuloksena löysimme kuusi erilaista verkkorakennetta.
Linkitimme nämä toistuvat rakenteet edelleen bisnesmalleihin.
Teimme asiantuntijahaastattelun tuloksiemme validoimiseksi.
Haastattelu tuki omia johtopäätöksiämme.
Tarkastelimme dataa myös erilaisten tunnuslukujen ja projektioiden avulla, joiden avulla saimme lisää perspektiiviä bisnesmallien luonteeseen.
Eri tarkastelusuunnat tukivat saatuja tuloksia.

Prosessi toi esiin useita uusia kysymyksiä ja tutkimuskohteita.
Klusterointi- ja filtteröintialgoritmien jatkokehittäminen olisi erittäin houkuttelevaa.
Välitön jatkokohde olisi myös erilaisen lähtödatan käsitteleminen esittelemällämme metodologialla.
Erilaisten yritysten ja julkisyhteisöjen verkostoista voisi löytyä samoja tai hyvin erilaisia malleja.
ED:2010-08-11
INSSI record number: 40081
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