Issues with Fedora & Hydra, experiences from a research-data-driven implementation

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

IT architecture for category driven corporate procurement measurement

The purpose of this thesis was to investigate creating and improving category purchasing visibility for corporate procurement by utilizing financial information. This thesis was a part of the global category driven spend analysis project of Konecranes Plc. While creating general understanding for building category driven corporate spend visibility, the IT architecture and needed purchasing parameters for spend analysis were described. In the case part of the study three manufacturing plants of Konecranes Standard Lifting, Heavy Lifting and Services business areas were examined. This included investigating the operative IT system architecture and needed processes for building corporate spend visibility. The key findings of this study were the identification of the needed processes for gathering purchasing data elements while creating corporate spend visibility in fragmented source system environment. As an outcome of the study, roadmap presenting further development areas was introduced for Konecranes.

A rose is a rose is a rose? Defining significant properties of social science research data

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

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Role of service and data reuse in enterprises

Digitalization has been predicted to change the future as a growing range of non-routine tasks will be automated, offering new kinds of business models for enterprises. Serviceoriented architecture (SOA) provides a basis for designing and implementing welldefined problems as reusable services, allowing computers to execute them. Serviceoriented design has potential to act as a mediator between IT and human resources, but enterprises struggle with their SOA adoption and lack a linkage between the benefits and costs of services. This thesis studies the phenomenon of service reuse in enterprises, proposing an ontology to link different kinds of services with their role conceptually as a part of the business model. The proposed ontology has been created on the basis of qualitative research conducted in three large enterprises. Service reuse has two roles in enterprises: it enables automated data sharing among human and IT resources, and it may provide cost savings in service development and operations. From a technical viewpoint, the ability to define a business problem as a service is one of the key enablers for achieving service reuse. The research proposes two service identification methods, first to identify prospective services in the existing documentation of the enterprise and secondly to model the services from a functional viewpoint, supporting service identification sessions with business stakeholders.