Development of data sheets for statistical evaluation of fatigue data

To enable a mathematically and physically sound execution of the fatigue test and a correct interpretation of its results, statistical evaluation methods are used to assist in the analysis of fatigue testing data. The main objective of this work is to develop step-by-stepinstructions for statistical analysis of the laboratory fatigue data. The scopeof this project is to provide practical cases about answering the several questions raised in the treatment of test data with application of the methods and formulae in the document IIW-XIII-2138-06 (Best Practice Guide on the Statistical Analysis of Fatigue Data). Generally, the questions in the data sheets involve some aspects: estimation of necessary sample size, verification of the statistical equivalence of the collated sets of data, and determination of characteristic curves in different cases. The series of comprehensive examples which are given in this thesis serve as a demonstration of the various statistical methods to develop a sound procedure to create reliable calculation rules for the fatigue analysis.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

Building Dynamic Data Centers for Fast Delivery of New Data and Data Updates

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

The requirements and needs of global data usage in product lifecycle management

This study was done for ABB Ltd. Motors and Generators business unit in Helsinki. In this study, global data movement in large businesses is examined from a product data management (PDM) and enterprise resource planning (ERP) point-of-view. The purpose of this study was to understand and map out how a large global business handles its data in a multiple site structure and how it can be applied in practice. This was done by doing an empirical interview study on five different global businesses with design locations in multiple countries. Their master data management (MDM) solutions were inspected and analyzed to understand which solution would best benefit a large global architecture with many design locations. One working solution is a transactional hub which negates the effects of multisite transfers and reduces lead times. Also, the requirements and limitations of the current MDM architecture were analyzed and possible reform ideas given.  

Data Governance and Automated Marketing – A Case Study of Expected Benefits of Organizing Data Governance in an ICT Company

This research is looking to find out what benefits employees expect the organization of data governance gains for an organization and how it benefits implementing automated marketing capabilities. Quality and usability of the data are crucial for organizations to meet various business needs. Organizations have more data and technology available what can be utilized for example in automated marketing. Data governance addresses the organization of decision rights and accountabilities for the management of an organization’s data assets. With automated marketing it is meant sending a right message, to a right person, at a right time, automatically. The research is a single case study conducted in Finnish ICT-company. The case company was starting to organize data governance and implementing automated marketing capabilities at the time of the research. Empirical material is interviews of the employees of the case company. Content analysis is used to interpret the interviews in order to find the answers to the research questions. Theoretical framework of the research is derived from the morphology of data governance. Findings of the research indicate that the employees expect the organization of data governance among others to improve customer experience, to improve sales, to provide abilities to identify individual customer’s life-situation, ensure that the handling of the data is according to the regulations and improve operational efficiency. The organization of data governance is expected to solve problems in customer data quality that are currently hindering implementation of automated marketing capabilities.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Governance of Healthcare Data: International Standards and Recommendations

In the global phenomenon, the aging population becomes a critical issue. Data and information concerning elderly citizens are increasing and are not well organized. In addition, these unstructured data and information cause the problems for decision makers. Since we live in a digital world, Information Technology is considered to be a tool in order to solve problems. Data, information, and knowledge are crucial components to facilitate success in IT service system. Therefore, it is necessary to study how to organize or to govern data from various sources related elderly citizens. The research is conducted due to the fact that there is no internationally accepted holistic framework for governance of data. The research limits the scope to study on the healthcare domain; however, the results can be applied to the other areas. The research starts with an ongoing research of Dahlberg and Nokkala (2015) as a theory. It explains the classification of existing data sources and their characteristics with the focus on managerial perspectives. Then the studies of existing frameworks at international and national level organizations have been performed to show the current frameworks, which have been used and are useful in compiling data on elderly citizens. The international organizations in this research are selected based on their reputations and the reliability to obtain information. The selected countries at national level provide different point of views between two countries. Australia is a forerunner in IT governance while Thailand is the country which the author has familiar knowledge of the current situation. Considered the discussions of frameworks at international and national organizations level illustrate the main characteristics of each framework. At international organization level gives precedence to the interoperability of exchanging data and information between different parties. Whereas at national level shows the importance of the acknowledgement of using frameworks throughout the country in order to make the frameworks to be effective. After the studies of both international and national organization levels, the thesis shows the summarized tables to answer the fitness to the proposed framework by Dahlberg and Nokkala whether the framework help to consolidate data from various sources with different formats, hierarchies, structures, velocities, and other attributes of data storages. In addition, suggestions and recommendations will be proposed for the future research.

Exploring the feasibility of applying data mining for library reference service improvement : a case study of Turku Main Library

Data mining, as a heatedly discussed term, has been studied in various fields. Its possibilities in refining the decision-making process, realizing potential patterns and creating valuable knowledge have won attention of scholars and practitioners. However, there are less studies intending to combine data mining and libraries where data generation occurs all the time. Therefore, this thesis plans to fill such a gap. Meanwhile, potential opportunities created by data mining are explored to enhance one of the most important elements of libraries: reference service. In order to thoroughly demonstrate the feasibility and applicability of data mining, literature is reviewed to establish a critical understanding of data mining in libraries and attain the current status of library reference service. The result of the literature review indicates that free online data resources other than data generated on social media are rarely considered to be applied in current library data mining mandates. Therefore, the result of the literature review motivates the presented study to utilize online free resources. Furthermore, the natural match between data mining and libraries is established. The natural match is explained by emphasizing the data richness reality and considering data mining as one kind of knowledge, an easy choice for libraries, and a wise method to overcome reference service challenges. The natural match, especially the aspect that data mining could be helpful for library reference service, lays the main theoretical foundation for the empirical work in this study. Turku Main Library was selected as the case to answer the research question: whether data mining is feasible and applicable for reference service improvement. In this case, the daily visit from 2009 to 2015 in Turku Main Library is considered as the resource for data mining. In addition, corresponding weather conditions are collected from Weather Underground, which is totally free online. Before officially being analyzed, the collected dataset is cleansed and preprocessed in order to ensure the quality of data mining. Multiple regression analysis is employed to mine the final dataset. Hourly visits are the independent variable and weather conditions, Discomfort Index and seven days in a week are dependent variables. In the end, four models in different seasons are established to predict visiting situations in each season. Patterns are realized in different seasons and implications are created based on the discovered patterns. In addition, library-climate points are generated by a clustering method, which simplifies the process for librarians using weather data to forecast library visiting situation. Then the data mining result is interpreted from the perspective of improving reference service. After this data mining work, the result of the case study is presented to librarians so as to collect professional opinions regarding the possibility of employing data mining to improve reference services. In the end, positive opinions are collected, which implies that it is feasible to utilizing data mining as a tool to enhance library reference service.

Spatiotemporal Aspects of Environmental Monitoring in the Complex Coastal Region of Southwest Finland

The management and conservation of coastal waters in the Baltic is challenged by a number of complex environmental problems, including eutrophication and habitat degradation. Demands for a more holistic, integrated and adaptive framework of ecosystem-based management emphasize the importance of appropriate information on the status and changes of the aquatic ecosystems. The thesis focuses on the spatiotemporal aspects of environmental monitoring in the extensive and geomorphologically complex coastal region of SW Finland, where the acquisition of spatially and temporally representative monitoring data is inherently challenging. Furthermore, the region is subject to multiple human interests and uses. A holistic geographical approach is emphasized, as it is ultimately the physical conditions that set the frame for any human activity. Characteristics of the coastal environment were examined using water quality data from the database of the Finnish environmental administration and Landsat TM/ETM+ images. A basic feature of the complex aquatic environment in the Archipelago Sea is its high spatial and temporal variability; this foregrounds the importance of geographical information as a basis of environmental assessments. While evidence of a consistent water turbidity pattern was observed, the coastal hydrodynamic realm is also characterized by high spatial and temporal variability. It is therefore also crucial to consider the spatial and temporal representativeness of field monitoring data. Remote sensing may facilitate evaluation of hydrodynamic conditions in the coastal region and the spatial extrapolation of in situ data despite their restrictions. Additionally, remotely sensed images can be used in the mapping of many of those coastal habitats that need to be considered in environmental management. With regard to surface water monitoring, only a small fraction of the currently available data stored in the Hertta-PIVET register can be used effectively in scientific studies and environmental assessments. Long-term consistent data collection from established sampling stations should be emphasized but research-type seasonal assessments producing abundant data should also be encouraged. Thus a more comprehensive coordination of field work efforts is called for. The integration of remote sensing and various field measurement techniques would be especially useful in the complex coastal waters. The integration and development of monitoring system in Finnish coastal areas also requires further scientific assesement of monitoring practices. A holistic approach to the gathering and management of environmental monitoring data could be a cost-effective way of serving a multitude of information needs, and would fit the holistic, ecosystem-based management regimes that are currently being strongly promoted in Europe.

Cost Optimisation of Mobile Advertising Client Data Transfer

Data traffic caused by mobile advertising client software when it is communicating with the network server can be a pain point for many application developers who are considering advertising-funded application distribution, since the cost of the data transfer might scare their users away from using the applications. For the thesis project, a simulation environment was built to mimic the real client-server solution for measuring the data transfer over varying types of connections with different usage scenarios. For optimising data transfer, a few general-purpose compressors and XML-specific compressors were tried for compressing the XML data, and a few protocol optimisations were implemented. For optimising the cost, cache usage was improved and pre-loading was enhanced to use free connections to load the data. The data traffic structure and the various optimisations were analysed, and it was found that the cache usage and pre-loading should be enhanced and that the protocol should be changed, with report aggregation and compression using WBXML or gzip.