Kvalitatiivisen aineiston tietokoneavusteisen analyysin lähtökohtia

Abstract: Towards computer-assisted qualitative data analysis

Evaluation of performance analysis software for Linux computing cluster

Tietokonejärjestelmän osien ja ohjelmistojen suorituskykymittauksista saadaan tietoa,jota voidaan käyttää suorituskyvyn parantamiseen ja laitteistohankintojen päätöksen tukena. Tässä työssä tutustutaan suorituskyvyn mittaamiseen ja mittausohjelmiin eli ns. benchmark-ohjelmistoihin. Työssä etsittiin ja arvioitiin eri tyyppisiä vapaasti saatavilla olevia benchmark-ohjelmia, jotka soveltuvat Linux-laskentaklusterin suorituskyvynanalysointiin. Benchmarkit ryhmiteltiin ja arvioitiin testaamalla niiden ominaisuuksia Linux-klusterissa. Työssä käsitellään myös mittausten tekemisen ja rinnakkaislaskennan haasteita. Benchmarkkeja löytyi moneen tarkoitukseen ja ne osoittautuivat laadultaan ja laajuudeltaan vaihteleviksi. Niitä on myös koottu ohjelmistopaketeiksi, jotta laitteiston suorituskyvystä saisi laajemman kuvan kuin mitä yhdellä ohjelmalla on mahdollista saada. Olennaista on ymmärtää nopeus, jolla dataa saadaan siirretyä prosessorille keskusmuistista, levyjärjestelmistä ja toisista laskentasolmuista. Tyypillinen benchmark-ohjelma sisältää paljon laskentaa tarvitsevan matemaattisen algoritmin, jota käytetään tieteellisissä ohjelmistoissa. Benchmarkista riippuen tulosten ymmärtäminen ja hyödyntäminen voi olla haasteellista.

Optimisation of data analysis method for a fluidized bed combustion test rig

Nowadays the used fuel variety in power boilers is widening and new boiler constructions and running models have to be developed. This research and development is done in small pilot plants where more faster analyse about the boiler mass and heat balance is needed to be able to find and do the right decisions already during the test run. The barrier on determining boiler balance during test runs is the long process of chemical analyses of collected input and outputmatter samples. The present work is concentrating on finding a way to determinethe boiler balance without chemical analyses and optimise the test rig to get the best possible accuracy for heat and mass balance of the boiler. The purpose of this work was to create an automatic boiler balance calculation method for 4 MW CFB/BFB pilot boiler of Kvaerner Pulping Oy located in Messukylä in Tampere. The calculation was created in the data management computer of pilot plants automation system. The calculation is made in Microsoft Excel environment, which gives a good base and functions for handling large databases and calculations without any delicate programming. The automation system in pilot plant was reconstructed und updated by Metso Automation Oy during year 2001 and the new system MetsoDNA has good data management properties, which is necessary for big calculations as boiler balance calculation. Two possible methods for calculating boiler balance during test run were found. Either the fuel flow is determined, which is usedto calculate the boiler's mass balance, or the unburned carbon loss is estimated and the mass balance of the boiler is calculated on the basis of boiler's heat balance. Both of the methods have their own weaknesses, so they were constructed parallel in the calculation and the decision of the used method was left to user. User also needs to define the used fuels and some solid mass flowsthat aren't measured automatically by the automation system. With sensitivity analysis was found that the most essential values for accurate boiler balance determination are flue gas oxygen content, the boiler's measured heat output and lower heating value of the fuel. The theoretical part of this work concentrates in the error management of these measurements and analyses and on measurement accuracy and boiler balance calculation in theory. The empirical part of this work concentrates on the creation of the balance calculation for the boiler in issue and on describing the work environment.

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

Kolme kaupallista analyysiohjelmaa ekologeille

Tietokoneohjelman arvostelu.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Spurring innovation through idea contests : data analysis and process improvements

This research concerns the Urban Living Idea Contest conducted by Creator Space™ of BASF SE during its 150th anniversary in 2015. The main objectives of the thesis are to provide a comprehensive analysis of the Urban Living Idea Contest (ULIC) and propose a number of improvement suggestions for future years. More than 4,000 data points were collected and analyzed to investigate the functionality of different elements of the contest. Furthermore, a set of improvement suggestions were proposed to BASF SE. Novelty of this thesis lies in the data collection and the original analysis of the contest, which identified its critical elements, as well as the areas that could be improved. The author of this research was a member of the organizing team and involved in the decision making process from the beginning until the end of the ULIC.

Front end development for building automation systems using JavaScript Frameworks

Automation technologies are widely acclaimed to have the potential to significantly reduce energy consumption and energy-related costs in buildings. However, despite the abundance of commercially available technologies, automation in domestic environments keep on meeting commercial failures. The main reason for this is the development process that is used to build the automation applications, which tend to focus more on technical aspects rather than on the needs and limitations of the users. An instance of this problem is the complex and poorly designed home automation front-ends that deter customers from investing in a home automation product. On the other hand, developing a usable and interactive interface is a complicated task for developers due to the multidisciplinary challenges that need to be identified and solved. In this context, the current research work investigates the different design problems associated with developing a home automation interface as well as the existing design solutions that are applied to these problems. The Qualitative Data Analysis approach was used for collecting data from research papers and the open coding process was used to cluster the findings. From the analysis of the data collected, requirements for designing the interface were derived. A home energy management functionality for a Web-based home automation front-end was developed as a proof-of-concept and a user evaluation was used to assess the usability of the interface. The results of the evaluation showed that this holistic approach to designing interfaces improved its usability which increases the chances of its commercial success.

Startup IPR strategies: Why should startup companies protect their intellectual property?

This thesis studies intellectual property right (also: IPR) strategies from the perspective of high growth startup companies. Due to technology development and intellectualization of business, large part of companies’ assets are nowadays intangible. At the same time, the importance of protection instruments designed to protect these intangible assets, intellectual property rights, is increasing. Utilization of these instruments, however, requires understanding of the functioning of the IPR system, as well as financial resources. Startup companies aiming for growth need to be able compete with more established companies also in relation to intangible assets, but they might not have the required knowledge ot resources to fully utilize IPRs in their business. This research aims to understand what are the benefits a startup company can have from protecting their IPRs, and how can the company achieve those benefits. Based on a review of previous literature, altogether 11 benefits of IPR registration were recognized. To answer to the research questions, six half-structured interviews were conducted with experts form different fields, all with experience in working with startup companies and IPR issues. The interviews were analyzed using different methods of qualitative data analysis, mainly derived from grounded theory and case study methods. As a result, out of the 11 benefits recognized from earlier literature, 8 were recognized to be relevant for startup companies. The most central benefits were recognized to be linked with the financial lifecycle of the startup company, including increasing credibility of the startup and stimulating an investment. In addition it was noticed, that startup companies are mainly able to utilize these benefits at later stages of their lifecycle. However, to be able to utilize the benefits at later stages, the startup company needs to be aware of the functioning of the IPR system and might need to apply for appropriate protection already early on. As a result of this study, a three-step model was formed to describe different levels of IPR utilization. The first level of the model represents the minimum level of understanding that every startup company should have regarding IPRs. The second level views IPR strategy from a risk management perspective, including securing the minimum protection of the company’s own IPRs, contract management and establishing processes for handling IPR issues. The last stage reflects strategic use of IPRs. At this third stage intellectual property rights have a central role in the startup company’s business, and they are used in the company’s value creation.

Development of internet-based value added services in housing estate business in Russia

Value added services are becoming increasingly popular as they increase the perceived value of the core product and can be a strong method of attracting customers and motivating them to make a choice. The purpose of this research is to develop internet-based value added services for housing estate business in Russia. The research is a case study of Russian housing estate market utilising a triangulation of methods for better results. For the qualitative data analysis, 7 interviews with heads of regional departments of construction companies from different regions of Russia were conducted. For the quantitative data analysis, a survey of 128 inhabitants of Saint-Petersburg housing estates was held. Factor analysis and descriptive statistics including cross-tabulations and chi-square tests for significance were used to analyse the results. In this study, a list 19 value added services that can be provided through online platforms in housing estate market was developed. These services fall into three big groups: social networking services, compulsory and additional services. Additionally, the question of monetisation of online platforms in housing estate market was discussed and three business models were suggested.

Evolutionary computing in search-based software engineering

This master’s thesis aims to study and represent from literature how evolutionary algorithms are used to solve different search and optimisation problems in the area of software engineering. Evolutionary algorithms are methods, which imitate the natural evolution process. An artificial evolution process evaluates fitness of each individual, which are solution candidates. The next population of candidate solutions is formed by using the good properties of the current population by applying different mutation and crossover operations. Different kinds of evolutionary algorithm applications related to software engineering were searched in the literature. Applications were classified and represented. Also the necessary basics about evolutionary algorithms were presented. It was concluded, that majority of evolutionary algorithm applications related to software engineering were about software design or testing. For example, there were applications about classifying software production data, project scheduling, static task scheduling related to parallel computing, allocating modules to subsystems, N-version programming, test data generation and generating an integration test order. Many applications were experimental testing rather than ready for real production use. There were also some Computer Aided Software Engineering tools based on evolutionary algorithms.

KUMPPANUUKSIEN LAATUTEKIJÄT JA KUMPPANUUSKYVYKKYYS VERKOSTOSUHTEIDEN KRIITTISINÄ MENESTYSTEKIJÖINÄ

Pro gradu -tutkielman tavoitteena on selvittää, mitkä ovat kahden yrityksen läheisen verkostoyhteistyösuhteen menestystekijät ja kuinka yritys pystyy parantamaan onnistumistaan verkostosuhteissaan. Päämääränä on selvittää kirjallisuudesta löydettyjen menestystekijöiden avulla, mikä on case –yritysten yhteistyösuhteiden ja kumppanuuskyvykkyyden nykytila, pullonkaulatekijät ja kuinka niitä voidaan parantaa. Tutkielman tavoitteisiin on pyritty kvalitatiivisiin tutkimusmenetelmiin kuuluvalla case –tutkimuksella. Aineisto kerättiin teemahaastatteluin ja analysoitiin purkamalla ne sanallisiin, teema-alueiden mukaisiin kokonaisuuksiin. Analyysissä käytettiin osittain Atlas.Ti –ohjelmistoa. Tutkimuksen tulokset osoittavat, että läheisen verkostoyhteistyösuhteen menestystekijät ovat luottamus, sitoutuminen ja kommunikaatio sekä verkostoidentiteetti. Näistä luottamuksen tila oli tutkituissa suhteissa hyvä ja sitoutumisenkin kohtuullinen. Sen sijaan verkostoidentiteetti oli jossain määrin epäselvä ja kommunikaatiossa oli puutteita kasvaen sitä mukaan, kun organisaatiohierarkiassa edetään korkeammalle. Nämä mahdollisesti heikentävät läheisestä suhteesta saatavia hyötyjä. Tulokset osoittavat myös sen, että nostaakseen onnistumisen tason kaikissa yhteistyösuhteissaan, niiden tulee pyrkiä järjestelmällisesti hyödyntämään aikaisempaa yhteistyökokemustaan ja näin rakentamaan kumppanuuskyvykkyyttään. Kohdeyrityksissä oli tehty vielä vähän konkreettisia toimenpiteitä yritystason kumppanuuskyvykkyyden kohentamiseksi.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.