Provenance and logging for sense making

Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs, such as those that emerged recently as the result of so-called “Big Data”. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making. One of the recent advances in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps like unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process. Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research. This Dagstuhl Seminar brought together researchers from the diverse fields that relate to provenance and sense making to foster cross-community collaboration. Shared challenges were identified and progress has been made towards developing novel solutions

Provenance analysis for sensemaking. IEEE Computer Graphics and Applications, 39 (6) . pp. 27-29. ISSN 0272-1716

The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations

Deploying Web-based Visual Exploration Tools on the Grid

We discuss a web-based portal for the exploration, encapsulation, and dissemination of visualization results over the Grid. This portal integrates three components: an interface client for structured visualization exploration, a visualization web application to manage the generation and capture of the visualization results, and a centralized portal application server to access and manage grid resources. Our approach uses standard web technologies to make the system accessible with minimal user setup. We demonstrate the usefulness of the developed system using an example for Adaptive Mesh Refinement (AMR) data visualization

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Vortex Characterization for Engineering Applications

Realistic engineering simulation data often have features that are not optimally resolved due to practical limitations on mesh resolution. To be useful to application engineers, vortex characterization techniques must be sufficiently robust to handle realistic data with complex vortex topologies. In this paper, we present enhancements to the vortex topology identification component of an existing vortex characterization algorithm. The modified techniques are demonstrated by application to three realistic data sets that illustrate the strengths and weaknesses of our approach

Pericentromeric location of the telomeric DNA sequences on the European grayling chromosomes

The chromosomal characteristics, locations and variations of the C-band positive heterochromatin and telomeric DNA sequences were studied in the European grayling karyotype (Thymallus thymallus, Salmonidae) using conventional C-banding, endonucleases digestion banding, silver nitrate (AgNO3), chromomycin A(3) and 4',6-diamidino-2-phenylindole staining techniques as well as fluorescence in situ hybridization (FISH) and primed in situ labelling. Original data on the chromosomal distribution of segments resistant to AluI restriction endonuclease and identification of the C-banded heterochromatin presented here have been used to characterize the grayling karyotype polymorphism. Structural and length polymorphism of the chromosome 21 showing a conspicuous heterochromatin block adjacent to the centromere seems to be the result of the deletion and inversion. Two pairs of nuclear organizer regions (NOR)-bearing chromosomes were found to be polymorphic in size and displaying several distinct forms. FISH with telomeric peptide nucleic acid probe enabled recognition of the conservative telomeric DNA sequences. The karyotype of the thymallid fish is thought to experienced numerous pericentric inversions and internal telomeric sites (ITSs) observed at the pericentromeric regions of the six European grayling metacentric chromosomes are likely relics of the these rearrangements. None of the ITS sites matched either chromosome 21 or NOR bearing chromosomes.University of Warmia and Mazury in Olsztyn, Poland (0804.0809)info:eu-repo/semantics/publishedVersio