Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Melt/peridotite interaction in the Southern Lanzo peridotite: Field, textural and geochemical evidence

This paper presents field, petrographic-structural and geochemical data on spinet and plagioclase peridotites from the southern domain of the Lanzo ophiolitic peridotite massif (Western Alps). Spinet lherzolites, harzburgites and dunites crop out at Mt. Arpone and Mt. Musine. Field evidence indicates that pristine porphyroclastic spinet lherzolites are transformed to coarse granular spinet harzburgites, which are in turn overprinted by plagioclase peridotites, while strongly depleted spinet harzburgite and dunite bands and bodies replace the plagioclase peridotites. On the northern flank of Mt. Arpone, deformed, porphyroclastic (lithospheric) lherzolites, with diffuse pyroxenite banding, represent the oldest spinel-facies rocks. They show microstructures of a composite subsolidus evolution, suggesting provenance from deeper (asthenospheric) mantle levels and accretion to the lithosphere. These protoliths are locally transformed to coarse granular (reactive) spinet harzburgites and dunites, which show textures reminiscent of melt/rock reaction and geochemical characteristics suggesting that they are products of peridotite interaction with reactively percolating melts. Geochemical data and modelling suggest that <1-5% fractional melting of spinel-facies DMM produced the injected melts. Plagioclase peridotites are hybrid rocks resulting from pre-existing spinet peridotites and variable enrichment of plagioclase and micro-gabbroic material by percolating melts. The impregnating melts attained silica-saturation, as testified by widespread orthopyroxene replacement of olivine, during open system migration in the lithosphere. At Mt. Musine, coarse granular spinet harzburgite and dunite bodies replace the plagioclase peridotites. Most of these replacive, refractory peridotites have interstitial magmatic clinopyroxene with trace element compositions in equilibrium with MORB, while some Cpx have REE-depleted patterns suggesting transient geochemical features of the migrating MORB-type melts, acquired by interaction with the ambient plagioclase peridotite. These replacive spinet harzburgite and dunite bodies are interpreted as channels exploited for focused and reactive migration of silica-undersaturated melts with aggregate MORB compositions. Such melts were unrelated to the silica-saturated melts that refertilized the pre-existing plagioclase peridotites. Finally, MORB melt migration occurred along open fractures, now recorded as gabbroic dikes. Our data document the complexity of rock-types and mantle processes in the South Lanzo peridotite massif and describe a composite tectonic and magmatic scenario that is not consistent with the ``asthenospheric scenario'' proposed by previous authors. We envisage a ``transitional scenario'' in which extending subcontinental lithospheric mantle was strongly modified (both depleted and refertilized) by early melts with MORB-affinity formed by decompression partial melting of the upwelling asthenosphere, during pre-oceanic rifting and lithospheric thinning in the Ligurian Tethys realm. (C) 2006 Elsevier B.V. All rights reserved.

A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that are parameterised in such models to ensure generic applicability make them complex and difficult to test on available data. Here, we outline an alternative - data-driven - inverse approach. We apply SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity. we take a Bayesian approach to the inverse problem of determining the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) diffuse pollution risk in eleven UK catchments covering a range of landscapes. The model results show that: 1) some land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. Taken on a case-by-case basis, this type of inverse approach may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems. (C) 2012 Elsevier B.V. All rights reserved.

Experience-based Auditory Predictions Modulate Brain Activity to Silence as Do Real Sounds.

Interactions between stimuli's acoustic features and experience-based internal models of the environment enable listeners to compensate for the disruptions in auditory streams that are regularly encountered in noisy environments. However, whether auditory gaps are filled in predictively or restored a posteriori remains unclear. The current lack of positive statistical evidence that internal models can actually shape brain activity as would real sounds precludes accepting predictive accounts of filling-in phenomenon. We investigated the neurophysiological effects of internal models by testing whether single-trial electrophysiological responses to omitted sounds in a rule-based sequence of tones with varying pitch could be decoded from the responses to real sounds and by analyzing the ERPs to the omissions with data-driven electrical neuroimaging methods. The decoding of the brain responses to different expected, but omitted, tones in both passive and active listening conditions was above chance based on the responses to the real sound in active listening conditions. Topographic ERP analyses and electrical source estimations revealed that, in the absence of any stimulation, experience-based internal models elicit an electrophysiological activity different from noise and that the temporal dynamics of this activity depend on attention. We further found that the expected change in pitch direction of omitted tones modulated the activity of left posterior temporal areas 140-200 msec after the onset of omissions. Collectively, our results indicate that, even in the absence of any stimulation, internal models modulate brain activity as do real sounds, indicating that auditory filling in can be accounted for by predictive activity.

Sinkkirikasteen liukenemisnopeuteen vaikuttavat tekijät ja sinkkirikasteen suoraliuotuksen mallintaminen

Työn kirjallisuusosassa selvitettiin eri tekijöiden (lämpötila, paine, mineralogia, partikkelikoko, sekoitus, kiintoainepitoisuus, liuoksen happo-, rauta- ja happipitoisuus) vaikutusta sinkkirikasteen suoraliuotusprosessin tärkeimpiin ilmiöihin (diffuusio, aineensiirto, reaktiokinetiikka). Kirjallisuusosassa kartoitettiin myös tämän hetkistä tietämystä niistä tekijöistä, jotka ovat oleellisia, kun sinkkirikasteen suoraliuotusprosessia mallinnetaan. Näitä tekijöitä ovat: sinkkirikasteiden liuotuksen kemia, sinkkirikasteiden liuotuksen kinetiikka ja mekanismit, kaasuneste aineensiirto ja kiinteäneste aineensiirto. Lisäksi selvitettiin millä tavoin aikaisemmissa tutkimuksissa sinkkirikasteiden suoraliuotusta on mallinnettu. Mallinnusosassa käsiteltiin atmosfääristä sinkkirikasteen suoraliuotusta, jossa hapettimena toimi ferri(III)rauta. Mallintamisessa käytettiin kirjallisuudessa esitettyjä mittaustuloksia ja mallintaminen tehtiin Modest tietokoneohjelmistolla. Työssä tehty atmosfäärisen suoraliuotuksen mallintaminen labo-ratoriomittakaavassa (laimeat liuokset ja pienet kiintoainepitoisuudet) antoi lupaavia tuloksia. Ongelmia mallin ennustuksen kanssa esiintyi pienissä happopitoisuuksissa, alhaisissa lämpötiloissa sekä pienillä ja suurilla partikkeleilla. Työn kirjallisuusosassa tunnistettiin ne ongelmakohdat, jotka vaativat lisätutkimuksia, jotta sinkkirikasteen atmosfääriselle suoraliuotukselle pystytään kehittämään simulointimalli. Näitä ovat: 1. Hapen liukoisuus ja aineensiirto teollisuuden käyttämissä sinkkirikastelietteissä, 2. Sopivien mittaustulosten puuttuminen, jotta atmosfääristä suoraliuotusta, jossa O2 ja Fe3+ toimivat hapettimena voitaisiin mallintaa, 3. Kiinteäneste aineensiirron merkitys sinkkirikasteen suoraliuotuksessa. Mallinnusosassa osoitettiin, että mekanistisella mallintamisella voidaan simuloida sinkkirikasteen atmosfääristä suoraliuotusta ainakin laboratoriomittakaavassa. Työn perusteella voidaan todeta, että sinkkirikasteen atmosfääriselle suoraliuotukselle voidaan työssä ehdotettujen jatkotutkimusten avulla kehittää numeerinen mekanistinen malli, jolla atmosfääristä sinkkirikasteiden suoraliuotusprosessia voidaan simuloida eri olosuhteissa.

Long-term functional improvements in the 2-year treatment of schizophrenia outpatients with olanzapine long-acting injection

BACKGROUND: Little is known about the long-term changes in the functioning of schizophrenia patients receiving maintenance therapy with olanzapine long-acting injection (LAI), and whether observed changes differ from those seen with oral olanzapine. METHODS: This study describes changes in the levels of functioning among outpatients with schizophrenia treated with olanzapine-LAI compared with oral olanzapine over 2 years. This was a secondary analysis of data from a multicenter, randomized, open-label, 2-year study comparing the long-term treatment effectiveness of monthly olanzapine-LAI (405 mg/4 weeks; n=264) with daily oral olanzapine (10 mg/day; n=260). Levels of functioning were assessed with the Heinrichs-Carpenter Quality of Life Scale. Functional status was also classified as 'good', 'moderate', or 'poor', using a previous data-driven approach. Changes in functional levels were assessed with McNemar's test and comparisons between olanzapine-LAI and oral olanzapine employed the Student's t-test. RESULTS: Over the 2-year study, the patients treated with olanzapine-LAI improved their level of functioning (per Quality of Life total score) from 64.0-70.8 (P<0.001). Patients on oral olanzapine also increased their level of functioning from 62.1-70.1 (P<0.001). At baseline, 19.2% of the olanzapine-LAI-treated patients had a 'good' level of functioning, which increased to 27.5% (P<0.05). The figures for oral olanzapine were 14.2% and 24.5%, respectively (P<0.001). Results did not significantly differ between olanzapine-LAI and oral olanzapine. CONCLUSION: In this 2-year, open-label, randomized study of olanzapine-LAI, outpatients with schizophrenia maintained or improved their favorable baseline level of functioning over time. Results did not significantly differ between olanzapine-LAI and oral olanzapine.

Tactile-Proprioceptive Robotic Grasping

Robotic grasping has been studied increasingly for a few decades. While progress has been made in this field, robotic hands are still nowhere near the capability of human hands. However, in the past few years, the increase in computational power and the availability of commercial tactile sensors have made it easier to develop techniques that exploit the feedback from the hand itself, the sense of touch. The focus of this thesis lies in the use of this sense. The work described in this thesis focuses on robotic grasping from two different viewpoints: robotic systems and data-driven grasping. The robotic systems viewpoint describes a complete architecture for the act of grasping and, to a lesser extent, more general manipulation. Two central claims that the architecture was designed for are hardware independence and the use of sensors during grasping. These properties enables the use of multiple different robotic platforms within the architecture. Secondly, new data-driven methods are proposed that can be incorporated into the grasping process. The first of these methods is a novel way of learning grasp stability from the tactile and haptic feedback of the hand instead of analytically solving the stability from a set of known contacts between the hand and the object. By learning from the data directly, there is no need to know the properties of the hand, such as kinematics, enabling the method to be utilized with complex hands. The second novel method, probabilistic grasping, combines the fields of tactile exploration and grasp planning. By employing well-known statistical methods and pre-existing knowledge of an object, object properties, such as pose, can be inferred with related uncertainty. This uncertainty is utilized by a grasp planning process which plans for stable grasps under the inferred uncertainty.

Excelling in procurement : IT Contract Management with Lean and Six Sigma. Case study on legacy contract management at Philips Global IT.

Few people see both opportunities and threats coming from IT legacy in current world. On one hand, effective legacy management can bring substantial hard savings and smooth transition to the desired future state. On the other hand, its mismanagement contributes to serious operational business risks, as old systems are not as reliable as it is required by the business users. This thesis offers one perspective of dealing with IT legacy – through effective contract management, as a component towards achieving Procurement Excellence in IT, thus bridging IT delivery departments, IT procurement, business units, and suppliers. It developed a model for assessing the impact of improvements on contract management process and set of tools and advices with regards to analysis and improvement actions. The thesis conducted case study to present and justify the implementation of Lean Six Sigma in IT legacy contract management environment. Lean Six Sigma proved to be successful and this thesis presents and discusses all the steps necessary, and pitfalls to avoid, to achieve breakthrough improvement in IT contract management process performance. For the IT legacy contract management process two improvements require special attention and can be easily copied to any organization. First is the issue of diluted contract ownership that stops all the improvements, as people do not know who is responsible for performing those actions. Second is the contract management performance evaluation tool, which can be used for monitoring, identifying outlying contracts and opportunities for improvements in the process. The study resulted in a valuable insight on the benefits of applying Lean Six Sigma to improve IT legacy contract management, as well as on how Lean Six Sigma can be applied in IT environment. Managerial implications are discussed. It is concluded that the use of data-driven Lean Six Sigma methodology for improving the existing IT contract management processes is a significant addition to the existing best practices in contract management.

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.

The Human Communication Science Virtual Lab (HCS vLab): A repository microclimate in a rapidly evolving research-ecosystem

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

Organisaation osaamiskartoituksella mitattavan strategisen osaamisen määrittäminen resursoinnin näkökulmasta

Tämän pro gradu –tutkielman tarkoituksena on selvittää minkälaisella prosessilla saadaan määriteltyä resursoinnin näkökulmasta toteutettu osaamiskartoitus. Tutkimus on laadullinen tapaustutkimus kohdeorganisaatiossa. Tutkimusaineisto on kerätty dokumenteista ja tutkimuksessa toteutetuista tapaamisista sekä työpajoista. Tutkimusaineisto on analysoitu aineistolähtöisellä sisällönanalyysimenetelmällä. Tutkimuksen tulosten mukaan osaamiskartoitusprosessiin ja sen onnistumiseen vaikuttavat merkittävästi yrityksen strategia, johdon sitoutuminen osaamiskartoitustyöhön, nykytilan analyysi, yhteiset käsitteistöt, mittarit ja tavoitteet. Resursoinnin näkökulmasta vaadittavat osaamiset eivät välttämättä ole samat kuin kehittämisen näkökulmasta. Määrittelyprosessin onnistumisen kannalta merkittäviä tekijöitä ovat oikeiden henkilöiden osallistuminen prosessiin ja heidän halunsa jakaa tietoa.

Evaluation of Location Intelligence exploitation in capital-intensive business

As technology has developed it has increased the number of data produced and collected from business environment. Over 80% of that data includes some sort of reference to geographical location. Individuals have used that information by utilizing Google Maps or different GPS devices, however such information has remained unexploited in business. This thesis will study the use and utilization of geographically referenced data in capital-intensive business by first providing theoretical insight into how data and data-driven management enables and enhances the business and how especially geographically referenced data adds value to the company and then examining empirical case evidence how geographical information can truly be exploited in capital-intensive business and what are the value adding elements of geographical information to the business. The study contains semi-structured interviews that are used to scan attitudes and beliefs of an organization towards the geographic information and to discover fields of applications for the use of geographic information system within the case company. Additionally geographical data is tested in order to illustrate how the data could be used in practice. Finally the outcome of the thesis provides understanding from which elements the added value of geographical information in business is consisted of and how such data can be utilized in the case company and in capital-intensive business.

Contemporary marketing in the high-tech industry: Perceptions of Finnish IT marketing professionals

Over the last 30 years, new technologies and globalization have radically changed the way in which marketing is conducted. However, whereas their effects on business in general have been widely discussed, the focus of the effects on marketing remains without clear recognition. Global research has been made to shed light onto the issue, but it has widely concentrated on the views of executives as well as the consumer markets. In addition, a research gap is existent in applying the concept of marketing change in a specific business-to-business (B2B) industry. Therefore, the main research question this study seeks to answer is: “How is contemporary marketing conducted in the high-technology industry?” In this research, the researcher considers the specific industry of high-technology. However, as the industry is comprised of differing markets, the focus will be given to one of the industry’s prime sectors – the information technology (IT) markets, where companies offer other firms products or services manufactured with advanced technology. The growing IT-market is considered of critical importance in the economies of technologically ready countries such as Finland, where this research is also conducted. Through multiple case studies the researcher aims to describe how the changes in technology, customer engagement and future trends have shaped the way in which successful high-tech marketing is conducted in today’s marketplace. Then, results derived from the empirical research are presented to the reader with links to existing literature. As a conclusion, a generalized framework is constructed to depict and ideal marketer-customer relationship, with emphasis on dynamic, two-way communication and its supporting elements of customer analytics, change adaptation, strategic customer communication and organizational support. From a managerial point of view, the research may provide beneficial information as contemporary marketing can yield profitable outcomes if managed correctly. As a new way to grasp competitive advantage, strategic marketing is much more data-driven and customer-focused than ever before. The study can also prove to be relevant for the academic communities, while its results may act as inspiring for new focus on the education trends of future marketers. This study was limited to the internal activities done at the high-tech industry, leaving out the considerations for co-marketing, marketing via business partners or marketing at other B2B-industries.