Knowledge Bases and Related Tools (KBART)

Magaly Basconesin esitys Kirjastoverkkopäivillä 24.10.2013 Helsingissä.

Searchig the links between "knowledge governance mechanisms", "the individual conditions of knowledge sharing", and "individual's knowledge sharing behaviour".

The objective of this study was to understand how organizational knowledge governance mechanisms affect individual motivation, opportunity, and the ability to share knowledge (MOA framework), and further, how individual knowledge-sharing conditions affect actual knowledge sharing behaviour. The study followed the knowledge governance approach and a micro-foundations perspective to develop a theoretical model and hypotheses, which could explain the casual relationships between knowledge governance mechanisms, individual knowledge sharing conditions, and individual knowledge sharing behaviour. The quantitative research strategy and multivariate data analysis techniques (SEM) were used in the hypotheses testing with a survey dataset of 256 employees from eleven military schools of Finnish Defence Forces (FDF). The results showed that “performance-based feedback and rewards” affects employee’s “intrinsic motivation towards knowledge sharing”, that “lateral coordination” affects employee’s “knowledge self-efficacy”, and that ”training and development” is positively related to “time availability” for knowledge sharing but affects negatively employee’s knowledge self-efficacy. Individual motivation and knowledge self-efficacy towards knowledge sharing affected knowledge sharing behaviour when work-related knowledge was shared 1) between employees in a department and 2) between employees in different departments, however these factors did not play a crucial role in subordinate–superior knowledge sharing. The findings suggest that individual motivation, opportunity, and the ability towards knowledge sharing affects individual knowledge sharing behaviour differently in different knowledge sharing situations. Furthermore, knowledge governance mechanisms can be used to manage individual-level knowledge sharing conditions and individual knowledge sharing behaviour but their affect also vary in different knowledge sharing situations.

Organizing for Systemic Innovations – Research on Knowledge, Interaction and Organizational Interdependencies

Systemic innovation has emerged as an important topic due to the interconnected technological and sociotechnical change of our current complex world. This study approaches the phenomenon from an organizing perspective, by analyzing the various actors, collaborative activities and resources available in innovation systems. It presents knowledge production for innovation and discusses the organizational challenges of shared innovation activities from a dynamic perspective. Knowledge, interaction, and organizational interdependencies are seen as the core elements of organizing for systemic innovations. This dissertation is divided into two parts. The first part introduces the focus of the study and the relevant literature and summarizes conclusions. The second part includes seven publications, each reporting on an important aspect of the phenomenon studied. Each of the in-depth single-case studies takes a distinct and complementary systems approach to innovation activities – linking the refining of knowledge to the enabling of organizations to participate in shared innovation processes. These aspects are summarized as theoretical and practical implications for recognizing innovation opportunities and turning ideas into innovations by means of using information and organizing activities in an efficient manner. Through its investigation of the existing literature and empirical case studies, this study makes three main contributions. First, it describes the challenges inherent in utilizing information and transforming it into innovation knowledge. Secondly, it presents the role of interaction and organizational interdependencies in innovation activities from various novel perspectives. Third, it highlights the interconnection between innovations and organizations, and the related path dependency and anticipatory aspects in innovation activities. In general, the thesis adds to our knowledge of how different aspects of systems form innovations through interaction and organizational interdependencies. It highlights the continuous need to redefine information and adjust organizations and networks based on ongoing activities – stressing the emergent, systemic nature of innovation.

Review of water electrolysis technologies and design of renewable hydrogen production systems

An electric system based on renewable energy faces challenges concerning the storage and utilization of energy due to the intermittent and seasonal nature of renewable energy sources. Wind and solar photovoltaic power productions are variable and difficult to predict, and thus electricity storage will be needed in the case of basic power production. Hydrogen’s energetic potential lies in its ability and versatility to store chemical energy, to serve as an energy carrier and as feedstock for various industries. Hydrogen is also used e.g. in the production of biofuels. The amount of energy produced during hydrogen combustion is higher than any other fuel’s on a mass basis with a higher-heating-value of 39.4 kWh/kg. However, even though hydrogen is the most abundant element in the universe, on Earth most hydrogen exists in molecular forms such as water. Therefore, hydrogen must be produced and there are various methods to do so. Today, the majority hydrogen comes from fossil fuels, mainly from steam methane reforming, and only about 4 % of global hydrogen comes from water electrolysis. Combination of electrolytic production of hydrogen from water and supply of renewable energy is attracting more interest due to the sustainability and the increased flexibility of the resulting energy system. The preferred option for intermittent hydrogen storage is pressurization in tanks since at ambient conditions the volumetric energy density of hydrogen is low, and pressurized tanks are efficient and affordable when the cycling rate is high. Pressurized hydrogen enables energy storage in larger capacities compared to battery technologies and additionally the energy can be stored for longer periods of time, on a time scale of months. In this thesis, the thermodynamics and electrochemistry associated with water electrolysis are described. The main water electrolysis technologies are presented with state-of-the-art specifications. Finally, a Power-to-Hydrogen infrastructure design for Lappeenranta University of Technology is presented. Laboratory setup for water electrolysis is specified and factors affecting its commissioning in Finland are presented.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

The role of knowledge management and renewal in building innovation performance

Tämän työn tavoitteena oli selvittää tietojohtamisen eri käytäntöjen vaikutusta oppimiseen, uudistumiseen sekä yrityksen innovaatiokyvykkyyteen. Työssä on keskitytty erityisesti sellaisiin tietojohtamisen käytäntöihin, jotka edistävät oppimista ja uusiutumista yrityksissä. Työssä on käytetty tilastollisia menetelmiä, muun muassa faktorianalyysia, korrelaatioanalyysia sekä regressiota, analysoitaessa 259 suomalaisesta yrityksestä kerättyä kyselydataa niiden tietojohtamisen käytöntöihin ja aineettomaan pääomaan liittyen. Analyysi osoittaa, että useat tietojohtamisen käytännöt vaikuttavat positiivisesti yrityksen uudistumiseen ja sitä kautta innovaatiokyvykkyyteen. Henkilöstön kouluttaminen sekä parhaiden käytäntöjen kerääminen ja soveltaminen yrityksessä ovat positiivisesti yhteydessä innovaatiokyvykkyyteen. Henkilöstön kouluttamisella on merkittävin suora vaikutus innovaatiokyvykkyyteen ja tässä työssä on esitetty, että koulutuksen tarjoamisen suurin vaikutus on oppimismyönteisen kulttuurin kehittyminen yrityksiin sen sijaan, että koulutuksella pyrittäisiin vain parantamaan tehtäväkenttään liittyviä taitoja ja tietoja. Henkilöstön kouluttaminen, parhaat käytännöt sekä sosialisaatiossa tapahtuva tiedon vaihto ja suhteiden solmiminen vaikuttavat positiivisesti uudistumispääomaan. Työn tulosten perusteella uudistumispääomalla on merkittävä rooli innovaatioiden syntymisessä yrityksissä. Uudistumispääoma medioi koulutuksen, parhaiden käytäntöjen ja mahdollisesti myös sosialisaation vaikutusta innovaatiokyvykkyyteen ja on näin merkittävä osa innovaatioiden syntyä yrityksissä. Innovaatiokyvykkyyden osatekijöiden ymmärtäminen voi auttaa johtajia ja esimiehiä keskittämään huomionsa tiettyihin tietojohtamisen käytäntöihin edistääkseen innovaatioiden syntymistä yrityksessä sen sijaan, että he pyrkisivät vain vaikuttamaan innovaatioprosessiin.

Possibilities and challenges in utilization of information provided by Industrial Internet applications

Global digitalization has affected also industrial sector. A trend called Industrial Internet has been present for some years and established relatively steady position in businesses. Industrial Internet is also referred with the terminology Industry 4.0 and in consumer businesses IoT (Internet of Things). Eventually, trend consists of many traditionally proven technologies and concepts, such as condition monitoring, remote services, predictive maintenance and Internet customer portals. All these technologies and information related to them are estimated to change the rules of business in industrial sector. This may result even a new industrial revolution. This research has its focus on Industrial Internet products, services and applications. The study analyses four case companies and their digital service offerings. According to this analysis the comparison of these services is done to find out if there is still space for companies to gain competitive advantage through differentiation with these state of the art solutions. One of the case companies, Case Company Ltd., is working as a primary case company and a subscriber of this particular research. The research and results are analyzed primarily from this company’s perspective and need. In empirical part, the research clarifies how Case Company Ltd. has allocated its development resources through last five years. These allocations in certain categories are then compared to other case companies’ current customer offering and conclusions are made how the approach of different companies differ from each other. Existing theoretical knowledge of Industrial Internet is about to find its shape. In this research we take a look how the case company analysis and findings correlate with the existing knowledge and literature of the topic.

Modeling competencies and capabilities in knowledge intensive business environment

Tämän diplomityön päämääränä oli tutkia Perloksen teknologiaosaamisia. Perloksen tavoitteena on tulevaisuudessa yhdistää ja soveltaa uusia teknologioita ja älykkäitä materiaaleja muovimekaniikkaan.Ideana oli mallintaa Perloksen osaamisia ja osaamisgapeja ottaen huomioon heidän tulevaisuuden visionsa. Projektituotteena osaamisten mallintamisessa oli Perlos Healthcaren asiakkaan analysoiva mittauslaite. Tutkimuksen arvo on huomattava sillä tunnistamalla osaamisensa ja kyvykkyytensä yritys pystyy luomaan paremman tarjooman vastatessaan koko ajan kasvaviin asiakasvaatimuksiin. Tutkimus on osa TEKESin rahoittamaa LIIMA -projektia. Työn ensimmäisessä osassa esitellään osaamiseen ja partneroitumiseen liittyviä teorioita. Osaamisten mallintaminen tehtiin Excel -pohjaisella työkalulla. Se sisältää projektituotteeseen liittyen osaamisriippuvuuksien mallintamisen ja gap -analyysin. Yhtenä tutkimusmetodina käytettiin haastattelututkimusta. Työ ja sen tulokset antavat operatiivista hyötyä teknologioiden ja markkinoiden välisessä kentässä.

Wireless Technologies in e-Business; Services, Security and Management

Uusien mobiilien laitteiden ja palveluiden kehitys ovat herättäneet yritysten mielenkiinnon soveltaa langattomia sovelluksia omassa liiketoiminnassaan. Erilaisten tekniikoiden myötä myös mahdollisuuksien kirjo on laajentumassa, mikä johtaa erilaisten verkkojen ja laitteiden yhtenäiselle hallinnalle asetettavien vaatimusten kasvuun. Yritysten siirtyessä soveltamaan uusia langattomia palveluita ja sovelluksia on myös huomioon otettavaa sovellusten sekä palveluiden vaatima tietoturva ja sen hallittavuus. Tutkimuksessa esitetään langattoman sähköisen liiketoiminnan määritelmä sekä kyseisien teknologioiden käyttöä edistävät tekijät. Tutkimus luo viitekehyksen yrityksen langattomien teknologioiden käytölle ja siihen olennaisesti vaikuttavista tekijöistä. Viitekehystä on käytetty todelliseen esimerkkiin, liikkuva myyntihenkilö, kyseisten teknologioiden, palveluiden, tietoturvan ja hallittavuuden näkökulmasta. Johtopäätöksinä on arvioitu mobiilien ja langattomien teknologioiden sekä palveluiden, tietoturvan ja hallittavuuden tilaa ja analysoimalla niitä tulevaa ajatellen.

Mobile learning and video experience in delivering digital content

In recent years, the worldwide distribution of smartphone devices has been growing rapidly. Mobile technologies are evolving fast, a situation which provides new possibilities for mobile learning applications. Along with new delivery methods, this development enables new concepts for learning. This study focuses on the effectiveness and experience of a mobile learning video promoting the key features of a specific device. Through relevant learning theories, mobile technologies and empirical findings, the thesis presents the key elements for a mobile learning video that are essential for effective learning. This study also explores how previous experience with mobile services and knowledge of a mobile handset relate to final learning results. Moreover, this study discusses the optimal delivery mechanisms for a mobile video. The target group for the study consists of twenty employees of a Sanoma Company. The main findings show that the individual experience of learning and the actual learning results may differ and that the design for certain video elements, such as sound and the presentation of technical features, can have an impact on the experience and effectiveness of a mobile learning video. Moreover, a video delivery method based on cloud technologies and HTML5 is suggested to be used in parallel with standalone applications.