Explanatory visualization of multidimensional projections

Het verkrijgen van inzicht in grote gegevensverzalelingen (tegenwoording bekend als ‘big data’) kan gedaan worden door ze visueel af te beelden en deze visualisaties vervolgens interactief exploreren. Toch kunnen beide het aantal datapunten of metingen, en ook het aantal dimensies die elke meting beschrijven, zeer groot zijn – zoals een table met veel rijen en kolommen. Het visualiseren van dergelijke zogenaamde hoog-dimensionale datasets is zeer uitdagend. Een manier om dit te doen is door het maken van een laag (twee of drie) dimensionale afbeelding, waarin men dan zoekt naar interessante datapatronen in plaats van deze te zoeken in de oorspronkelijke hoog-dimensionale data. Technieken die dit scenario ondersteunen, de zogenaamde projecties, hebben verschillende voordelen – ze zijn visueel schaalbaar, ze werken robuust met ruizige data, en ze zijn snel. Toch is het gebruik van projecties ernstig beperkt door het feit dat ze moeilijk te interpreteren zijn. We benaderen dit problem door verschillende technieken te ontwikkelen die de interpretative vergemakkelijken, zoals het weergeven van projectiefouten en het uitleggen van projecties door middel van de oorpronkelijke hoge dimensies. Onze technieken zijn makkelijk te leren, snel te rekenen, en makkelijk toe te voegen aan elke dataexploratiescenario dat gebruik maakt van elke projectie. We demonstreren onze oplossingen met verschillende toepassingen en data van metingen, wetenschappelijke simulaties, software-engineering, en netwerken

The role of habitat features in a primary succession

In order to determine the role of habitat features in a primary succession on lava domes of Terceira Island (Azores) we addressed the following questions: (1) Is the rate of cover development related to environmental stress? (2) Do succession rates differ as a result of habitat differences? One transect, intercepting several habitats types (rocky hummocks, hollows and pits, small and large fissures), was established from the slope to the summit of a 247 yr old dome. Data on floristic composition, vegetation bioarea, structure, demography and soil nutrients were collected. Quantitative and qualitative similarities among habitats were also analyzed. Cover development and species accumulation are mainly dependent on habitat features. Habitat features play a critical role in determining the rate of succession by providing different environmental conditions that enable different rates of colonization and cover development. Since the slope’s surface is composed of hummocks, hollows and pits the low succession rates in these habitats are responsible for the lower rates of succession in this geomorphologic unit, whereas the presence of fissures in the dome’s summit accelerates its succession rate

Explaining neighborhood preservation for multidimensional projections

Dimensionality reduction techniques are the tools of choice for exploring high-dimensional datasets by means of low-dimensional projections. However, even state-of-the-art projection methods fail, up to various degrees, in perfectly preserving the structure of the data, expressed in terms of inter-point distances and point neighborhoods. To support better interpretation of a projection, we propose several metrics for quantifying errors related to neighborhood preservation. Next, we propose a number of visualizations that allow users to explore and explain the quality of neighborhood preservation at different scales, captured by the aforementioned error metrics.We demonstrate our exploratory views on three real-world datasets and two state-of-the-art multidimensional projection techniques.São Paulo Research Foundation (FAPESP) (grant 2012/07722-9)CAPES–NUFFIC 028/1

Exploring Multidimensional Projections Through Explanatory Maps

Multidimensional Projections (MPs) are popular tools used to support the analysis of multidimensional data. MPs can project data to a low dimensional space, and are typically visualized as 2D scatterplots where similar elements are conveniently positioned in close neighborhoods. However such visualizations tell us which points are similar, but not why. Our aim is, thus, to enrich 2D MP scatterplots with explanatory information telling users which key dimensions make closely projected points similar, and their values

Visual text mining: ensuring the presence of relevant studies in systematic literature reviews

One of the activities associated with the Systematic Literature Review (SLR) process is the\ud selection review of primary studies. When the researcher faces large volumes of primary studies\ud to be analyzed, the process used to select studies can be arduous. In a previous experiment, we\ud conducted a pilot test to compare the performance and accuracy of PhD students in conducting\ud the selection review activity manually and using Visual Text Mining (VTM) techniques. The\ud goal of this paper is to describe a replication study involving PhD and Master students. The\ud replication study uses the same experimental design and materials of the original experiment.\ud This study also aims to investigate whether the researcher's level of experience with conducting\ud SLRs and research in general impacts the outcome of the primary study selection step of the\ud SLR process. The replication results have con¯rmed the outcomes of the original experiment,\ud i.e., VTM is promising and can improve the performance of the selection review of primary\ud studies. We also observed that both accuracy and performance increase in function of the\ud researcher's experience level in conducting SLRs. The use of VTM can indeed be bene¯cial\ud during the selection review activity.FAPES

Flora and vegetation in different physiognomies of a Mussununga in southeastern Brazil.

Mussununga is an understudied ecosystem within the Atlantic Forest domain, in sandy spodosol lowlands from Bahia to Esp?rito Santo. Its physiognomy varies from grassland to forest, with a transitional savannic area. We evaluated the life-form spectra differences between the grassland and savanna Mussunungas and its relationship with the depth of a soil impermeable layer (ortstein). The study area is located in the municipality of Linhares, Esp?rito Santo State. Ten plots were settled in each physiognomy. The floristic and vegetation spectra (accordingly to Raunkiaer) were compared using the G-test. A total of 35 species into three life-forms were found: Phanerophytes, hemicryptophytes and cryptophytes. The floristic spectra were similar in both physiognomies, with a greater richness of phanerophytes. However, the vegetation spectra of the two areas were different. Phanerophytes dominated in the savannas (where ortstein is deeper), while the shallower ortstein of the grasslands favored hemicryptophytes