Measuring organizational capabilities in the engineering and consulting industry

Tämän työn tarkoituksena oli tutkia kuinka organisaation kyvykkyyksiä voidaan mitata engineering- ja konsultointialalla käyttämällä ns. kyvykkyysauditointimenetelmää. Päämotiivit aineettoman omaisuuden mittaamiseksi tunnistettiin kirjallisuuskatsauksen pohjalta. Erilaisten menetelmien etuja ja haittoja tutkittiin, jotta kyvykkyysauditoinnin suorittamiseen liittyvät haasteet ja vaatimukset tulisivat tunnistetuiksi. Kyvykkyysauditoinnin rakentaminen vaati teollisuudenalan erityispiirteiden tunnistamista. Niiksi havaittiin tietointensiivisyys ja projektikeskeisyys. Auditoinnin implementaatioprosessi koostui neljästä osasta, joista kolmen ensimmäisen suorittamiseen case-yritys antoi merkittävän panoksensa. Kriittisten menestystekijöiden selvittämisen jälkeen voitiin niihin vaikuttavat organisaation kyvykkyydet tunnistaa ja arviointi suorittaa. Arvioinnit kerättiin sisäisiltä ja ulkoisilta arvioijilta, ja ne muodostivat pohjan analyysille, joka selvitti yrityksen kehittämistarpeita. Kyvykkyysauditoinnin hyödyiksi laskettiin kasvanut tietämys yrityksen vahvuuksista ja heikkouksista sekä mahdollisuus tarkkailla säännöllisesti sen kokonaissuorituskykyä ja parantaa sitä.

Development of Customer Satisfaction Models for Electronics Engineering Company

Diplomityön tarkoituksena oli luoda ja kehittää kaksi asiakastyytyväisyysmallia asiakastyytyväisyyden mittaamisen aloittamiseksi ja toteuttamiseksi kohdeyrityksessä. Työ pohjautuu nykyisten tyytyväisyysprosessien analysointiin sekä työn teoriaosaan, joka käsittelee yksityiskohtaisesti niitä asioita, joita asiakastyytyväisyyden mittaamisessa ja prosessissa tulisi huomioida. Työssä tehdyn mallien tarkoituksen on auttaa kohdeyritystä hyödyntämään asiakastyytyväisyysmittauksen tuloksia paremmin liiketoiminnassa, sekä asiakkaiden keskuudessa. Työn yhtenä tavoitteena oli myös sopivan mittaustyökalun löytäminen ja suositteleminen kohdeyritykselle.Teorian ja analysoinnin pohjalta luotiin molemmat asiakastyytyväisyysmallit vastamaan kohdeyksiköiden tarpeita. Kun ulkoiset seikat, kuten mittaustavat, mittausinstrumentit, kyselylomakkeet ja vastaajaryhmät oli määritelty, keskityttiin tulosten analysointiin ja hyödyntämiseen, mikä korostui asiakassuuntautuneessa organisaatiossa. Työssä pohdittiin myös yhtenäisen asiakastyytyväisyysprosessin merkitystä ja etuja kohdeyrityksessä.

Business Process Modeling in Software Requirements Engineering for Small and Medium Software Projects

Vaatimusmäärittelyn tavoitteena on luoda halutun järjestelmän kokonaisen, yhtenäisen vaatimusluettelon vaatimusten määrittämiseksi käsitteellisellä tasolla. Liiketoimintaprosessien mallintaminen on varsin hyödyllinen vaatimusmäärittelyn varhaisissa vaiheissa. Tämä työ tutkii liiketoimintaprosessien mallintamista tietojärjestelmien kehittämistä varten. Nykyään on olemassa erilaisia liiketoimintaprosessien mallintamiseen tarkoitettuja tekniikoita. Tämä työ tarkastaa liiketoimintaprosessien mallintamisen periaatteet ja näkökohdat sekä eri mallinnustekniikoita. Uusi menetelmä, joka on suunniteltu erityisesti pienille ja keskisuurille ohjelmistoprojekteille, on kehitetty prosessinäkökohtien ja UML-kaavioiden perusteella.

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.

Cost Estimation of Engineering Environment Services

Työn tavoitteena oli kehittää tutkittavan insinööriyksikön projektien kustannusestimointiprosessia, siten että yksikön johdolla olisi tulevaisuudessa käytettävänään tarkempaa kustannustietoa. Jotta tämä olisi mahdollista, ensin täytyi selvittää yksikön toimintatavat, projektien kustannusrakenteet sekä kustannusatribuutit. Tämän teki mahdolliseksi projektien kustannushistoriatiedon tutkiminen sekä asiantuntijoiden haastattelu. Työn tuloksena syntyi kohdeyksikön muiden prosessien kanssa yhteensopiva kustannusestimointiprosessi sekä –malli.Kustannusestimointimenetelmän ja –mallin perustana on kustannusatribuutit, jotka määritellään erikseen tutkittavassa ympäristössä. Kustannusatribuutit löydetään historiatietoa tutkimalla, eli analysoimalla jo päättyneitä projekteja, projektien kustannusrakenteita sekä tekijöitä, jotka ovat vaikuttaneet kustannusten syntyyn. Tämän jälkeen kustannusatribuuteille täytyy määritellä painoarvot sekä painoarvojen vaihteluvälit. Estimointimallin tarkuutta voidaan parantaa mallin kalibroinnilla. Olen käyttänyt Goal – Question – Metric (GQM) –menetelmää tutkimuksen kehyksenä.

Web Software Engineering - value added approach

Taking the maximum advantage of technological innovations and the investment in them is of key importance for businesses. The IT industry offers a wide range of innovative high-technology solutions to manage information processing and distribution. However for end-user businesses to make informed decisions in this area is challenging. The aim of this research is to identify the key differences in principal solutions, and what the selection criteria should be for those involved. Existing methodologies for software development are classified, and some key criteria are described to help IT system developers and users determine what are the most important factors in system selection, development and deployment. Statistical data is researched and analysed, a theoretical basis is developed and reviewed, key issues from case studies are identified and generalized to be presented along with the conclusions in the current study. The results give a good basis for corporate consideration and provide overall support to the key decisions in developing web-based software. The conclusion is that new web developments should be considered the stakeholders as an evolution of existing business systems, but they should then pay particular attention to the new advantages that web-based software offers in terms of standardised interfaces and procedures, universal deployment opportunities, and a range of other benefits the study highlights.

Macroporous Scaffolds for Bone Engineering. Studies on Cell Culture and Ectopic Bone Formation

Bone engineering is a rapidly developing area of reconstructive medicine where bone inducing factors and/or cells are combined with a scaffold material to regenerate the structure and function of the original tissue. The aim of this study was to compare the suitability of different macroporous scaffold types for bone engineering applications. The two scaffold categories studied were a) the mechanically strong and stable titanium fiber meshes and b) the elastic and biodegradable porous polymers. Furthermore, bioactive modifications were applied to these basic scaffold types, and their effect on the osteogenic responses was evaluated in cell culture and ectopic bone formation studies. The osteogenic phenotype of cultured cell-scaffold constructs was heightened with a sol-gel derived titania coating, but not with a mixed titania-silica coating. The latter coating also resulted in delayed ectopic bone formation in bone marrow stromal cell seeded scaffolds. However, the better bone contact in early implantation times and more even bone tissue distribution at later times indicated enhanced osteoconductivity of both the coated scaffold types. Overall, the most promising bone engineering results were obtained with titania coated fiber meshes. Elastic and biodegradable poly(ε-caprolactone/D,L-lactide) based scaffolds were also developed in this study. The degradation rates of the scaffolds in vitro were governed by the hydrophilicity of the polymer matrix, and the porous architecture was controlled by the amount and type of porogen used. A continuous phase macroporosity was obtained using a novel CaCl2 • 6H2O porogen. Dynamic culture conditions increased cell invasion, but decreased cell numbers and osteogenicity, within the scaffolds. Osteogenic differentiation in static cultures and ectopic bone formation in cell seeded scaffolds were enhanced in composites, with 30 wt-% of bioactive glass filler.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Creating customer relationship management concept for engineering and service business

Customer relationship management has been one essential part of marketing for over 20 years. Today’s business environment is fast changing, international and highly competitive, and that is why the most important factor for long-term profitability is one-to-one customer relationships. However, managing relationships and serving customers that are profitable has been always challenging. In this thesis the objective was to define the main obstacles that the case company must overcome to succeed in CRM. Possible solutions have also been defined. The main elements of the implementation i.e. people, processes and technologies, can clearly be found behind these matters and solutions. This thesis also presents theoretical information about CRM and it is meant to act as a guide book inside the organisation to spread information about CRM for those who are not so familiar with the topic.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.