Veslava Osinska

Study of dynamics of structured knowledge: Qualitative analysis of different mapping approaches

The authors compared three methods of mapping, considering the maps as a visual interface for the exploration of scientific articles related to computer science. Data were classified according to the original Computing Classification System (CCS) classification and co-categories were used for similarity metrics calculation. The authors’ approach based on MDS was enriched by algorithm mapping to spherical topology. Three other methods were based on VOS, VxOrd and SOM mapping techniques. Visualization of the classified collection was done for three different decades. Tracking the changes in visualization patterns, the authors sought the method that would reveal the real evolution of the CCS scheme, which is still being updated by the editorial board. Comparative analysis is based on qualitative methods. Changes in those properties over two decades were evaluated for the benefit of the authors’ method of mapping. The qualitative analysis shows clustering of proper categories and overlapping of other ones in the authors’ approach, which corresponds to the current changes in the classification scheme and computer science literature.

Fractal Analysis of Knowledge Organization in Digital Library

Visualization of the large-scale collections of information became one of the essential purpose in data analysis. The new methods of visualization are increasingly applied as a significant component in scientific research. Particularly qualitative nature of Infoviz studies (Information visualization) can be combined with quantitative character of digital libraries volumes. This paper describes and demonstrates the case of hierarchical structure visualization i.e. visual representation of both classification adopted by ACM (Association for Computing Machinery ) digital library and classification universe. Given maps were processed by nonlinear graphical filters. Finally fractal dimension (FD) and derived techniques have used to analyze the patterns of clusters on the visualization maps. Quantification of output graphical representation by means of fractals makes possible to adjust visualization parameters as well as evaluate initial classification scheme and its dynamical characteristics.

PCA Algorithms in the Visualization of Big Data from Polish Digital Libraries

The visualization of large data sets from Polish digital libraries requires proper preparation of a comprehensive consolidated data set. Differences in the organizational systems of digital resources, and other factors affecting the heterogeneity of distributed data and metadata, require the use of clustering algorithms. To achieve this goal, the authors decided to use the PCA method and compare it with k-means results. PCA fulfills the condition of efficient size reduction for multidimensional data but is largely sensitive to deviations and differences in stochastic distributions. To eliminate the problem of noise in the input data, the deterministic model in the form of the Langevin function was used first. This leads to the “flattening” of the distribution of factors influencing the data structure. Due to such an approach, the most relevant categories to information systems were distinguished and Polish digital libraries were visualized.

Distribution of date elements and its relationship to the types of digital libraries: Analysing aggregated metadata of a Polish large-scale distributed system

Large-scale distributed digital library systems with aggregated metadata provide platforms for resource discovery and retrieval. For researchers, aggregated metadata offers a potential for big data analysis and exploration of digital knowledge growth. The paper reports the findings of the study that investigated the distribution of the date elements in the metadata aggregated in the Polish Federation of Digital Libraries and related it to the types of libraries. The purpose of this study was to address the gap in research about heterogeneous digital libraries and explore the dynamics of their growth. The authors included timeline characteristics of the development of Polish digital libraries and proposed a new dynamics parameter – resource release interval. They used histograms, which have been grouped according to the organizational and thematic criteria, developed for this study. All charts are characterized by two similar maximum points. Their shapes and ratio have been analysed by both statistical and visual methods. The shape of resource release interval charts revealed characteristic differences for libraries types. The proposed approach, based on time characteristics, is an important step in the development of systematic classification of digital libraries and digitizing institutions. It can be also considered as a new tool in monitoring the dynamics of digital knowledge growth.

Rozwój badań nad wizualizacją informacji

W ostatniej dekadzie obserwowany jest szybki rozwoj interdyscyplinarnych badan nad wizualizacją informacji (Infovis). W bibliologii jako pierwszy zainteresowal sie tymi metodami pionier naukometrii E. Garfield, ktory wprowadzil termin „naukogramy”. W tworzenie naukogramow albo map nauki są zaangazowani informatolodzy i bibliolodzy, informatycy i naukoznawcy, specjaliści od prezentacji informacji oraz graficy. Niniejszy artykul ma na celu zaprezentowanie mozliwości wykorzystania Infovis jako nowego pola badan w informatologii poprzez serie przykladow wizualizacji dla kolekcji metadanych, ktore pochodzą z opisow bibliograficznych dokumentow dostepnych w polskich bibliotekach cyfrowych. Wyniki analiz metadanych autorzy zaprezentowali w graficznej postaci, wybierając technike wizualizacji stosownie do typu danych (liczba, tekst, daty) oraz kontekstu analizy. Wyznaczone cele wizualizacji danych: wykrycie zmian ilościowych na wykresie, pokazanie geograficznego rozrzutu danych, ujawnienie związkow intelektualno-spolecznych oraz efektywne komunikowanie idei eksperymentu zostaly szczegolowo opisane i zilustrowane.

Mapping evaluation for semantic browsing

The paper contributes to the problem solving in semantic browsing and analysis of scientific articles. With reference to presented visual interface, four - the most popular methods of mapping including own approach - MDS with spherical topology, have been compared. For a comparison quantitative measures were applied which allowed to select the most appropriate mapping way with an accurate reflection of the dynamics of data. For the quantitative analysis the authors used machine learning and pattern recognition algorithms and described: clusterization degree, fractal dimension and lacunarity. Local density differences, clusterization, homogeneity, and gappiness were measured to show the most acceptable layout for an analysis, perception and exploration processes. Visual interface for analysis how computer science evolved through the two last decades is presented on website. Results of both quantitative and qualitative analysis have revealed good convergence.