Suorituskyvyn mittaaminen rakennusteollisuudessa

Tutkimuksen tavoitteena oli rakentaa rakennusteollisuuteen sopiva suorituskyvyn analysointijärjestelmä. Järjestelmä on koko yrityksen kattava ja tasapainoinen. Tällöin voidaan paremmin saavuttaa yritystoiminnan perimmäinen tarkoitus eli kannattava liiketoiminta sekä taata yrityksen taloudelliset toimintaedellytykset, niin pitkällä kuin lyhyelläkin aikavälillä. Työ oli toimintatutkimus, mikä on reaalimaailmaa koskeva empiirinen tutkimus, joka konstruoi uutta todellisuutta. Toimintatutkimukselle on tyypillistä, että se suuntautuu käytäntöihin, pyrkii muutokseen ja tutkittavat osallistuvat tutkimusprosessiin. Aluksi käytiin läpi suorituskyvyn mittaamiseen liittyvää teoriaa. Sen jälkeen aloitettiin itse järjestelmän suunnittelu pohtimalla yrityksen visiota, strategiaa ja menestystekijöitä huomioiden rakennusteollisuuden erityispiirteet. Näiden pohjalta valittiin mitattavat osa-alueet sekä niihin liittyvät mittarit. Suorituskyvyn analysointijärjestelmän toteutuksen lisäksi havaittiin, että järjestelmän rakentaminen itsessään oli yritykselle erinomainen oppimisprosessi, joka avasi uusia näkökulmia sekä yrityksen toiminnan että henkilöstön kehittämiseen.

Business intelligence viewpoint on managerial decision-making : Ill-structured problems and decision support systems

Vaikka liiketoimintatiedon hallintaa sekä johdon päätöksentekoa on tutkittu laajasti, näiden kahden käsitteen yhteisvaikutuksesta on olemassa hyvin rajallinen määrä tutkimustietoa. Tulevaisuudessa aiheen tärkeys korostuu, sillä olemassa olevan datan määrä kasvaa jatkuvasti. Yritykset tarvitsevat jatkossa yhä enemmän kyvykkyyksiä sekä resursseja, jotta sekä strukturoitua että strukturoimatonta tietoa voidaan hyödyntää lähteestä riippumatta. Nykyiset Business Intelligence -ratkaisut mahdollistavat tehokkaan liiketoimintatiedon hallinnan osana johdon päätöksentekoa. Aiemman kirjallisuuden pohjalta, tutkimuksen empiirinen osuus tunnistaa liiketoimintatiedon hyödyntämiseen liittyviä tekijöitä, jotka joko tukevat tai rajoittavat johdon päätöksentekoprosessia. Tutkimuksen teoreettinen osuus johdattaa lukijan tutkimusaiheeseen kirjallisuuskatsauksen avulla. Keskeisimmät tutkimukseen liittyvät käsitteet, kuten Business Intelligence ja johdon päätöksenteko, esitetään relevantin kirjallisuuden avulla – tämän lisäksi myös dataan liittyvät käsitteet analysoidaan tarkasti. Tutkimuksen empiirinen osuus rakentuu tutkimusteorian pohjalta. Tutkimuksen empiirisessä osuudessa paneudutaan tutkimusteemoihin käytännön esimerkein: kolmen tapaustutkimuksen avulla tutkitaan sekä kuvataan toisistaan irrallisia tapauksia. Jokainen tapaus kuvataan sekä analysoidaan teoriaan perustuvien väitteiden avulla – nämä väitteet ovat perusedellytyksiä menestyksekkäälle liiketoimintatiedon hyödyntämiseen perustuvalle päätöksenteolle. Tapaustutkimusten avulla alkuperäistä tutkimusongelmaa voidaan analysoida tarkasti huomioiden jo olemassa oleva tutkimustieto. Analyysin tulosten avulla myös yksittäisiä rajoitteita sekä mahdollistavia tekijöitä voidaan analysoida. Tulokset osoittavat, että rajoitteilla on vahvasti negatiivinen vaikutus päätöksentekoprosessin onnistumiseen. Toisaalta yritysjohto on tietoinen liiketoimintatiedon hallintaan liittyvistä positiivisista seurauksista, vaikka kaikkia mahdollisuuksia ei olisikaan hyödynnetty. Tutkimuksen merkittävin tulos esittelee viitekehyksen, jonka puitteissa johdon päätöksentekoprosesseja voidaan arvioida sekä analysoida. Despite the fact that the literature on Business Intelligence and managerial decision-making is extensive, relatively little effort has been made to research the relationship between them. This particular field of study has become important since the amount of data in the world is growing every second. Companies require capabilities and resources in order to utilize structured data and unstructured data from internal and external data sources. However, the present Business Intelligence technologies enable managers to utilize data effectively in decision-making. Based on the prior literature, the empirical part of the thesis identifies the enablers and constraints in computer-aided managerial decision-making process. In this thesis, the theoretical part provides a preliminary understanding about the research area through a literature review. The key concepts such as Business Intelligence and managerial decision-making are explored by reviewing the relevant literature. Additionally, different data sources as well as data forms are analyzed in further detail. All key concepts are taken into account when the empirical part is carried out. The empirical part obtains an understanding of the real world situation when it comes to the themes that were covered in the theoretical part. Three selected case companies are analyzed through those statements, which are considered as critical prerequisites for successful computer-aided managerial decision-making. The case study analysis, which is a part of the empirical part, enables the researcher to examine the relationship between Business Intelligence and managerial decision-making. Based on the findings of the case study analysis, the researcher identifies the enablers and constraints through the case study interviews. The findings indicate that the constraints have a highly negative influence on the decision-making process. In addition, the managers are aware of the positive implications that Business Intelligence has for decision-making, but all possibilities are not yet utilized. As a main result of this study, a data-driven framework for managerial decision-making is introduced. This framework can be used when the managerial decision-making processes are evaluated and analyzed.

Motivointi pelillistämisen avulla

Työ käsittelee ihmisten motivointia hyödyntäen pelillistämistä. Työhön kuuluu myös mobiilisovelluksen toteutus, jolla pyritään vaikuttamaan julkisen liikenteen käytön kavattamiseen. Pelillistämisen käyttäminen sovelluksissa on kasvanut huimasti varsinkin mobiililaitteiden yleistymisen myötä. Pelillistämisellä pyritään vaikuttamaan ihmisten käyttäytymiseen pelielementtien avulla. Eli pelielementit, kuten pisteet ja saavutukset, tuodaan reaalimaailmaan ja käyttäjä palkitaan suorituksesta välittömästi. Työssä käydään läpi kaksi pelillistämistutkimusesimerkkiä, EcoIsland ja Orientation Passport, ja niiden havainnot. Pelillistämisen tehokkuuden tutkiminen on vaikeata, mutta yleisesti sitä pidetään toimivana. Kuitenkaan mikään taianomainen työkalu se ei ole.

SobekCM’s Community Ecosystems & Socio-Technical Practices

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Service oriented archive based on Fedora Commons

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Dynamic Analysis Model of Spherical Roller Bearings with Defects

Rolling element bearings are essential components of rotating machinery. The spherical roller bearing (SRB) is one variant seeing increasing use, because it is self-aligning and can support high loads. It is becoming increasingly important to understand how the SRB responds dynamically under a variety of conditions. This doctoral dissertation introduces a computationally efficient, three-degree-of-freedom, SRB model that was developed to predict the transient dynamic behaviors of a rotor-SRB system. In the model, bearing forces and deflections were calculated as a function of contact deformation and bearing geometry parameters according to nonlinear Hertzian contact theory. The results reveal how some of the more important parameters; such as diametral clearance, the number of rollers, and osculation number; influence ultimate bearing performance. Distributed defects, such as the waviness of the inner and outer ring, and localized defects, such as inner and outer ring defects, are taken into consideration in the proposed model. Simulation results were verified with results obtained by applying the formula for the spherical roller bearing radial deflection and the commercial bearing analysis software. Following model verification, a numerical simulation was carried out successfully for a full rotor-bearing system to demonstrate the application of this newly developed SRB model in a typical real world analysis. Accuracy of the model was verified by comparing measured to predicted behaviors for equivalent systems.

Christlich-religiöse Textwelt als translatorisch-hermeneutischer Gegenstand Gegenstandsbeschreibung und Herangehensweisen

Tämä teorialähtöinen, hermeneuttinen tutkimus sijoittuu käännöstieteen sekä uskonnon- ja kielifilosofian rajapintaan. Tutkimuksessa kuvataan kristinuskon tekstimaailmaa relevantin tulkintakehyksen kautta sekä esitetään tekstimaailman tulkintaan soveltuva lähestymistapa ammattikääntäjän eli välittävän vastaanottajan näkökulmasta. Tutkimus pureutuu käännösprosessin hermeneutiikkaan eli lähdetekstin ja sen maailman tulkintaan, ymmärtämiseen ja selittämiseen liittyviin aspekteihin. Tutkimuksessa ei esitetä käännösratkaisuja. Tutkimus nostaa esiin filosofisen ja teologisen hermeneutiikan peruskysymyksiä. Niitä ovat Raamatun tekstien vaikutushistoria, erilaiset todellisuus- ja kielikäsitykset, kirkon oppi, teologian pääsuuntaukset, uskonnolliset yhteisöt ja niiden perinteet, inhimillinen kulttuuri ja aika. Ne ovat ulottuvuuksia, jotka vaikuttavat kääntäjän tekstin- ja maailmantulkintaan ja vaativat laajojen kontekstien huomioonottamista sekä lähestymistä monista eri näkökulmista. Ludwig Wittgensteinin myöhäisfilosofiaan liittyvän kielipelin idea tarjoaa ajatusmallin, jonka avulla uskonnon käsite-, merkki- ja tekstimaailmalle voidaan luoda moninäkökulmainen tulkintakehys. Kielipelin käsitettä käytetään tässä tutkimuksessa tekstien samankaltaisuutta ja erilaisuutta kokoavana järjestelmänä, joka toimii intratekstuaalisesti eli sillä on omat pelisääntönsä, tulkinnan lähteensä sekä tunnistettava identiteetti. Järjestelmän keskiössä ovat uskonnon konstituioivat käsitteet ja kertomukset, jotka ovat muiden tulkintojen eli metatekstien lähde. Kääntäjän varsinaisina tulkintakohteina ovat metatekstit, eivät raamatunkääntämiseen liittyvät kysymykset suoranaisesti. Tutkimuksessa luodaan kuitenkin tulkintamalli, jonka mukaan metatekstejä ja raamatun tekstejä luetaan rinnakkain. Ymmärtäminen ja ongelmanratkaisu rakentuvat tekstuaalisen kommunikaation pohjalta. Tekstejä luetaan sekä sääntöteorian että narratiivisen teorian valossa. Kertomus puhuttelee vastaanottajaa eri tavalla kuin sääntö. Tulkintamallissa yhdistyvät sekä analyyttinen että luovan mielikuvituksen sallima lukutapa. Molemmat toimivat yhdessä kääntäjän tekstin- merkin- ja maailmantulkinnan apuna. Tutkimuksessa pohditaan myös tekstiteorettisia kysymyksiä, tekstin ja merkin käsitteitä sekä niiden toimintadynamiikkaa. Tulkinnan mekanismi esitetään semioosin käsitteen avulla. Teksti määritellään heuristiseksi ja eksemplaariseksi kuvaukseksi maailmasta, ja se edustaa vain osaa tulkintakokonaisuudesta. Kääntäjän hermeneuttinen kenttä ulotetaan tekstinulkoiseen maailmaan, käytäntöön ja elämänmuotoon.

Reliability and validity of the interactive observation scale for psychiatric inpatients applied by nursing aides in daily ward practice

The main characteristic of the nursing Interactive Observation Scale for Psychiatric Inpatients (IOSPI) is the necessity of interaction between raters and patients during assessment. The aim of this study was to evaluate the reliability and validity of the scale in the "real" world of daily ward practice and to determine whether the IOSPI can increase the interaction time between raters and patients and influence the raters' opinion about mental illness. All inpatients of a general university hospital psychiatric ward were assessed daily over a period of two months by 9 nursing aides during the morning and afternoon shifts, with 273 pairs of daily observations. Once a week the patients were interviewed by a psychiatrist who filled in the Brief Psychiatric Rating Scale (BPRS). The IOSPI total score was found to show significant test-retest reliability (interclass correlation coefficient = 0.83) and significant correlation with the BPRS total score (r = 0.69), meeting the criteria of concurrent validity. The instrument can also discriminate between patients in need of further inpatient treatment from those about to be discharged (negative predictive value for discharge = 0.91). Using this scale, the interaction time between nursing aides and patients increased significantly (t = 2.93, P<0.05) and their opinion about the mental illness changed. The "social restrictiveness" factor of the opinion scale about mental illness showed a significant reduction (t = 4.27, P<0.01) and the "interpersonal etiology" factor tended to increase (t = 1.98, P = 0.08). The IOSPI was confirmed as a reliable and valid scale and as an efficient tool to stimulate the therapeutic attitudes of the nursing staff.

Developing usability evaluation heuristics for augmented reality applications

There is no generic usability heuristics for Augmented Reality (AR) applications, thus, the aim of this thesis was to develop one. The development of the heuristics was carried out in phases. Based on a literature review, a preliminary version of the heuristics was developed, which was evaluated by four experts. As a result, six evaluation criteria were formed: 1) interaction methods and controls, 2) presentation of virtual objects, 3) relationship between virtual objects and real world, 4) information related to virtual objects, 5) suitability for the usage context and 6) physical comfort of use. The heuristics should be used with Nielsen's (1995) generic usability evaluation heuristics. The heuristics are not ready to be used as such, since it must still be tested in practice.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

From statin efficacy to everyday effectiveness: Studying the gap in between.

Statins are indicated for preventing cardiovascular disease events. Patients with diabetes have a risk of major cardiovascular events double the risk of their peers without diabetes. Thus, clinical treatment guidelines recommend statins for the management of diabetic dyslipidemia. The evidence base for statin use in cardiovascular disease derives from the randomized controlled statin trials designed to prove statin efficacy under ideal conditions, among a homogenous study population meeting strict trial eligibility criteria. This thesis was implemented as four pharmacoepidemiological statin studies using register data on realworld statin users. The overall purpose was to evaluate the trends, patterns and effectiveness of statin use in everyday life. More specifically, nationwide secular trends in statin use in Finland were analysed, especially among patient groups which had been underrepresented in the statin trials. Furthermore, the benchmarking statin trials in diabetes, the Heart Protection Study and the Collaborative Atorvastatin Diabetes Study, were evaluated for their representativeness for real-world diabetes care with the emphasis placed on adherence to statin use. The association between good adherence and the incidence of major cardiovascular events in the real-world was further investigated in diabetes. These studies demonstrate that statin initiations increased from 1995 to 2005 in Finland. The increase was most pronounced among those aged at least 75 years and was observed already before the publication of rigorous trial data conducted in elderly subjects. Thus, statins seem to have been initiated in clinical practice also going beyond the strict trial eligibility criteria. Nonetheless, low adherence to statin use among the real-world patients with diabetes was found not only to limit the representativeness of the trials for clinical care but also to attenuate in all likelihood their benefits in the real-world. In fact, good adherence to statin use was found to associate with a decreased risk for major cardiovascular events in patients with diabetes. In conclusion, these studies highlight the importance of good adherence to statin use in clinical practice in order to obtain the full therapeutic value demonstrated in the statin trials. Simply increasing the number of statin users will not alone suffice in sharing our common resources appropriately.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Suomenkielisen oppimateriaalin koostaminen ohjelmistotestauksen perusteita käsittelevälle kurssille

Ohjelmistotestauksen merkitys ja testausosaamisen tarve ovat lisääntyneet sen myötä, kun ohjelmistojen koko on kasvanut ja toteutus monimutkaistunut. Testaustaidot kehittyvät parhaiten käytännön kokemuksen kautta mutta testausosaamisen perusta ja edellytykset käytännön taitojen kehittymiselle rakentuvat testauksen keskeisten avainasioiden, näkökulmien ja käytäntöjen teoreettiseen tuntemukseen ja ammattisanaston hyvään hallintaan. Tässä raportissa kuvataan Lappeenrannan teknilliselle yliopistolle tehty työ, jonka tuloksena koostettiin suomenkielinen oppimateriaali uudelle, keväällä 2015 kurssitarjontaan lisätylle opintojaksolle Ohjelmistotestauksen perusteet.