A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Vaex: Big Data exploration in the era of Gaia

We present a new Python library called vaex, to handle extremely large tabular datasets, such as astronomical catalogues like the Gaia catalogue, N-body simulations or any other regular datasets which can be structured in rows and columns. Fast computations of statistics on regular N-dimensional grids allows analysis and visualization in the order of a billion rows per second. We use streaming algorithms, memory mapped files and a zero memory copy policy to allow exploration of datasets larger than memory, e.g. out-of-core algorithms. Vaex allows arbitrary (mathematical) transformations using normal Python expressions and (a subset of) numpy functions which are lazily evaluated and computed when needed in small chunks, which avoids wasting of RAM. Boolean expressions (which are also lazily evaluated) can be used to explore subsets of the data, which we call selections. Vaex uses a similar DataFrame API as Pandas, a very popular library, which helps migration from Pandas. Visualization is one of the key points of vaex, and is done using binned statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colormapping) and 3d (using volume rendering). Vaex is split in in several packages: vaex-core for the computational part, vaex-viz for visualization mostly based on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based in IPyWidgets, vaex-server for the (optional) client-server communication, vaex-ui for the Qt based interface, vaex-hdf5 for hdf5 based memory mapped storage, vaex-astro for astronomy related selections, transformations and memory mapped (column based) fits storage. Vaex is open source and available under MIT license on github, documentation and other information can be found on the main website: https://vaex.io, https://docs.vaex.io or https://github.com/maartenbreddels/vaexComment: 14 pages, 8 figures, Submitted to A&A, interactive version of Fig 4: https://vaex.io/paper/fig