Tehdasmittausten varastointi moniulotteisella tietomallilla

Tietovarastoissa moniulotteinen tietomalli on tehokkain tapa esittää tietoa päätöksentekijöille. Sen toimivuus on hyväksi havaittu monissa eri liiketoimintaympäristöissä. Tehdasympäristöissä on tuhansia mittalaitteita, joista jokainen mittaa uniikkia valmistusprosessiin liittyvää piirrettä. Tässä työssä kehitettiin tietovarasto tehdasmittausten varastointiin käyttäen moniulotteista tietomallia. Havaittiin, että moniulotteisella mallilla tehdasmittaukset voidaan tallentaa joustavalla tavalla ja esittää käyttäjälle mielekkäässä muodossa. Moniulotteinen malli antaa myös erinomaiset keinot tiedon ryhmittelyyn ja vertailuun. Sillä ei kuitenkaan saada vastaavanlaisia hyötyjä kuin klassisissa kaupanalan tietovarastointi esimerkeissä, koska eri mittaukset ovat keskenään hyvin erilaisia. Vaikka mittaukset eivät olekaan aina vertailtavissa tai summattavissa keskenään, saadaan ne moniulotteisella mallilla tallennettua ja luokiteltua loogisesti siten, että käyttäjän on helppo löytää tarvitsemansa tieto. Lisäksi yleisesti tunnettu ja paljon käytetty tietovaraston suunnittelumalli takaa sen, että markkinoilta on saatavissa työkaluja tietovaraston käyttöön. Tietokannan toteutus tehtiin vapaasti levitettävän MySQLtiedonhallintajärjestelmän avulla. Sitä ei ole suunniteltu pääasiassa tietovarastokäyttöön, mutta halpa lisenssi ja hyvä skaalautuvuus tekevät siitä mielenkiintoisen vaihtoehdon. Sitä onkin käytetty luultua enemmän tietovarastoinnissa ja myös monien nimekkäiden organisaatioiden toimesta. Myös tässä työssä todettiin, että MySQL tarjoaa riittävät välineet tietovaraston kehittämiseen.

Evaluating multidimensional visualization techniques in data mining tasks

Visual data mining (VDM) tools employ information visualization techniques in order to represent large amounts of high-dimensional data graphically and to involve the user in exploring data at different levels of detail. The users are looking for outliers, patterns and models – in the form of clusters, classes, trends, and relationships – in different categories of data, i.e., financial, business information, etc. The focus of this thesis is the evaluation of multidimensional visualization techniques, especially from the business user’s perspective. We address three research problems. The first problem is the evaluation of projection-based visualizations with respect to their effectiveness in preserving the original distances between data points and the clustering structure of the data. In this respect, we propose the use of existing clustering validity measures. We illustrate their usefulness in evaluating five visualization techniques: Principal Components Analysis (PCA), Sammon’s Mapping, Self-Organizing Map (SOM), Radial Coordinate Visualization and Star Coordinates. The second problem is concerned with evaluating different visualization techniques as to their effectiveness in visual data mining of business data. For this purpose, we propose an inquiry evaluation technique and conduct the evaluation of nine visualization techniques. The visualizations under evaluation are Multiple Line Graphs, Permutation Matrix, Survey Plot, Scatter Plot Matrix, Parallel Coordinates, Treemap, PCA, Sammon’s Mapping and the SOM. The third problem is the evaluation of quality of use of VDM tools. We provide a conceptual framework for evaluating the quality of use of VDM tools and apply it to the evaluation of the SOM. In the evaluation, we use an inquiry technique for which we developed a questionnaire based on the proposed framework. The contributions of the thesis consist of three new evaluation techniques and the results obtained by applying these evaluation techniques. The thesis provides a systematic approach to evaluation of various visualization techniques. In this respect, first, we performed and described the evaluations in a systematic way, highlighting the evaluation activities, and their inputs and outputs. Secondly, we integrated the evaluation studies in the broad framework of usability evaluation. The results of the evaluations are intended to help developers and researchers of visualization systems to select appropriate visualization techniques in specific situations. The results of the evaluations also contribute to the understanding of the strengths and limitations of the visualization techniques evaluated and further to the improvement of these techniques.

A systematic mapping study on open data

This thesis presented the overview of Open Data research area, quantity of evidence and establishes the research evidence based on the Systematic Mapping Study (SMS). There are 621 such publications were identified published between years 2005 and 2014, but only 243 were selected in the review process. This thesis highlights the implications of Open Data principals’ proliferation in the emerging era of the accessibility, reusability and sustainability of data transparency. The findings of mapping study are described in quantitative and qualitative measurement based on the organization affiliation, countries, year of publications, research method, star rating and units of analysis identified. Furthermore, units of analysis were categorized by development lifecycle, linked open data, type of data, technical platforms, organizations, ontology and semantic, adoption and awareness, intermediaries, security and privacy and supply of data which are important component to provide a quality open data applications and services. The results of the mapping study help the organizations (such as academia, government and industries), re-searchers and software developers to understand the existing trend of open data, latest research development and the demand of future research. In addition, the proposed conceptual framework of Open Data research can be adopted and expanded to strengthen and improved current open data applications.