Abstract The information age is characterized by ever-growing amounts of data surrounding us. By reproducing this data into usable knowledge we can start moving toward the knowledge age. Data mining is the science of transforming measurable information into usable knowledge. During the data mining process, the measurements pass through a chain of sophisticated transformations in order to acquire knowledge. Furthermore, in some applications the results are implemented as software solutions so that they can be continuously utilized. It is evident that the quality and amount of the knowledge formed is highly dependent on the transformations and the process applied. This thesis presents an application independent concept that can be used for managing the data mining process and implementing the acquired results as software applications. The developed concept is divided into two parts – solution formation and solution implementation. The first part presents a systematic way for finding a data mining solution from a set of measurement data. The developed approach allows for easier application of a variety of algorithms to the data, manages the work chain, and differentiates between the data mining tasks. The method is based on storage of the data between the main stages of the data mining process, where the different stages of the process are defined on the basis of the type of algorithms applied to the data. The efficiency of the process is demonstrated with a case study presenting new solutions for resistance spot welding quality control. The second part of the concept presents a component-based data mining application framework, called Smart Archive, designed for implementing the solution. The framework provides functionality that is common to most data mining applications and is especially suitable for implementing applications that process continuously acquired measurements. The work also proposes an efficient algorithm for utilizing cumulative measurement data in the history component of the framework. Using the framework, it is possible to build high-quality data mining applications with shorter development times by configuring the framework to process application-specific data. The efficiency of the framework is illustrated using a case study presenting the results and implementation principles of an application developed for predicting steel slab temperatures in a hot strip mill. In conclusion, this thesis presents a concept that proposes solutions for two fundamental issues of data mining, the creation of a working data mining solution from a set of measurement data and the implementation of it as a stand-alone application.

Abstract Recognition of the importance of a regional perspective for understanding the structure and dynamics of local assemblages has stimulated the emergence of the field of macroecology. Most attention has been directed to terrestrial ecosystems, while large-scale patterns in biodiversity of aquatic organisms have received less attention. In this thesis I examined patterns of aquatic diversity across several geographic areas and scales, in an effort to understand some of the environmental and spatial factors determining species diversity in aquatic environments. The main objectives of this thesis were: (i) to examine the latitudinal diversity patterns of marine crustaceans and molluscs and their relationship to large scale environmental gradients, (ii) to study macroinvertebrate species richness in headwater streams at two spatial extents, within and across drainage systems, and assess the relative importance of local, landscape and regional variables, and (iii) to study diversity patterns of macroorganisms vs microorganism, comparing distance decay patterns of stream diatoms, macroinvertebrates and bryophytes. Latitudinal diversity patterns of crustaceans and molluscs were clearly related to larval developmental mode. An increase in species richness towards high latitudes was found for species with direct development, whereas richness of species with planktotrophic development decreased poleward. Sea surface temperature was the most important environmental gradient related to species richness of both phyla and each developmental mode, but with different effects on each mode. Stream macroinvertebrate species richness at the bioregion extent was negatively related to water humic content. Another factor related to species richness at the bioregion extent was elevation range, a variable linked to stream topographic heterogeneity. Local environmental variables explained most of the variation in species richness at the drainage system extent, however high among-region variability was evident. Patterns between macro- and microorganism may not be fundamentally different, but the level of environmental control varied, being strongest for diatoms, while some groups of benthic macroinvertebrates exhibited relatively strong dispersal limitation. The relative importance of niche vs. dispersal processes is not simply a function of organism size but other traits (e.g. life-history type, dispersal capacity) may obscure this relationship.

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

This study provides a systematic description of the nature and evolution of major transactions in the U.S. apparel sector, using a theory that applies across sectors. This research investigates the determinants of the existence and magnitude of major inter-firm transactions, relying on a unique longitudinal dataset of over 2,000 of the largest transactional (buy-sell) relations between publicly traded firms in the U.S. apparel sector. The results indicate the importance of inter-firm complementarity, rather than inter-firm similarity, in explaining the sector architecture; thus contributing to the future improvement of industry classification systems. This study also contributes to a deeper understanding of the apparel sector focusing on the change in the relative importance of manufacturing activities versus service activities and in the involvement of firms from the outside apparel sector. Implications of inter-firm transactions are discussed regarding industry policies, and human and environmental welfare in manufacturing and raw materials industries.

This thesis is about methods of image comparison. Goal is to create a mobile app that allows user to compare images in real time. In the first part there is a theoretical basis, in particular image similarity algorithms. The practical part contains the app implementation including use case analysis, user interface design and functional requirements. It is followed by source code samples a description of frameworks used. Last part is made of testing the implemented algorithms regarding speed and precision.

We study stockpiling problems under uncertain economic and physical factors, and investigate the valuation and optimisation of storage systems where the availability and spot price of the underlying are both subject to stochasticity. Following a Real Options valuation approach, we first study financial derivatives linked to Asian options. A comprehensive set of boundary conditions is compiled, and an alternative (and novel) similarity reduction for fixed-strike Asian options is derived. Hybrid semi-Lagrangian methods for numerically solving the related partial differential equations (PDEs) are implemented, and we assess the accuracy of the valuations thus obtained with respect to results from classical finite-difference valuation methods and with respect to high precision calculations for valuing Asian options with spectral expansion theory techniques. Next we derive a PDE model for valuing the storage of electricity from a wind farm, with an attached back-up battery, that operates by trading electricity in a volatile market in order to meet a contracted fixed rate of energy generation; this system comprises two diffusive-type (stochastic) variables, namely the energy production and the electricity spot price, and two time-like (deterministic) variables, specifically the battery state and time itself. An efficient and novel semi-Lagrangian alternating-direction implicit (SLADI) methodology for numerically solving advection-diffusion problems is developed: here a semi-Lagrangian approach for hyperbolic problems of advection is combined with an alternating-direction implicit method for parabolic problems involving diffusion. Efficiency is obtained by solving (just) tridiagonal systems of equations at every time step. The results are compared to more standard semi-Lagrangian Crank-Nicolson (SLCN) and semi-Lagrangian fully implicit (SLFI) methods. Once he have established our PDE model for a storage-upgraded wind farm, a system that depends heavily on the highly stochastic nature of wind and the volatile market where electricity is sold, we derive a Hamilton-Jacobi-Bellman (HJB) equation for optimally controlling charging and discharging rates of the battery in time, and we assess a series of operation regimes. The solution of the related PDE models is approached numerically using our SLADI methodology to efficiently treat this mixed advection and diffusion problem in four dimensions. Extensive numerical experimentation confirms our SLADI methodology to be robust and yields highly accurate solutions and efficient computations, we also explore effects from correlation between stochastic electricity generation and random prices of electricity as well as effects from a seasonal electricity spot price. Ultimately, the objective of approximating optimal storage policies for a system under uncertain economic and physical factors is accomplished. Finally we examine the steady-state solution of a stochastic storage problem under uncertain electricity market prices and fixed demand. We use a HJB formulation for optimally controlling charging and discharging rates of the storage device with respect to the electricity spot price. A projected successive over-relaxation coupled with the semi-Lagrangian method is implemented, and we explore the use of boundary-fitted coordinates techniques.

The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part are described and analyzed 20 files. Each file contains only quantitative variables and sort characters by which objects are sorted. In each file is calculated success rate of object segmentation into groups for each cluster method. At the end of the practical part is a summary description of the results of cluster methods. The main contribution of this thesis is to evaluate the success of cluster methods for classification objects into known groups.

The creation, storage, manipulation, and transmission of images have become less costly and more efficient. Consequently, the numbers of images and their users are growing rapidly. This poses challenges to those who organize and provide access to them. One of these challenges is similarity matching. Most current content-based image retrieval (CBIR) systems which can extract only low-level visual features such as color, shape, and texture, use similarity measures based on geometric models of similarity. However, most human similarity judgment data violate the metric axioms of these models. Tversky's (1977) contrast model, which defines similarity as a feature contrast task and equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, explains human similarity judgments much better than the geometric models. This study tested the contrast model as a conceptual framework to investigate the nature of the relationships between features and similarity of images as perceived by human judges. Data were collected from 150 participants who performed two tasks: an image description and a similarity judgment task. Qualitative methods (content analysis) and quantitative (correlational) methods were used to seek answers to four research questions related to the relationships between common and distinctive features and similarity judgments of images as well as measures of their common and distinctive features. Structural equation modeling, correlation analysis, and regression analysis confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky (1977). Tversky's (1977) contrast model based upon a combination of two methods for measuring common and distinctive features, and two methods for measuring similarity produced statistically significant structural coefficients between the independent latent variables (common and distinctive features) and the dependent latent variable (similarity). This model fit the data well for a sample of 30 (435 pairs of) images and 150 participants (χ2 =16.97, df=10, p = .07508, RMSEA= .040, SRMR= .0205, GFI= .990, AGFI= .965). The goodness of fit indices showed the model did not significantly deviate from the actual sample data. This study is the first to test the contrast model in the context of information representation and retrieval. Results of the study are hoped to provide the foundations for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments.

This thesis introduces an innovative methodology of combining some traditional dictionary based approaches to word sense disambiguation (semantic similarity measures and overlap of word glosses, both based on WordNet) with some graph-based centrality methods, namely the degree of the vertices, Pagerank, closeness, and betweenness. The approach is completely unsupervised, and is based on creating graphs for the words to be disambiguated. We experiment with several possible combinations of the semantic similarity measures as the first stage in our experiments. The next stage attempts to score individual vertices in the graphs previously created based on several graph connectivity measures. During the final stage, several voting schemes are applied on the results obtained from the different centrality algorithms. The most important contributions of this work are not only that it is a novel approach and it works well, but also that it has great potential in overcoming the new-knowledge-acquisition bottleneck which has apparently brought research in supervised WSD as an explicit application to a plateau. The type of research reported in this thesis, which does not require manually annotated data, holds promise of a lot of new and interesting things, and our work is one of the first steps, despite being a small one, in this direction. The complete system is built and tested on standard benchmarks, and is comparable with work done on graph-based word sense disambiguation as well as lexical chains. The evaluation indicates that the right combination of the above mentioned metrics can be used to develop an unsupervised disambiguation engine as powerful as the state-of-the-art in WSD.