17,046 research outputs found
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
Interactive exploration of population scale pharmacoepidemiology datasets
Population-scale drug prescription data linked with adverse drug reaction
(ADR) data supports the fitting of models large enough to detect drug use and
ADR patterns that are not detectable using traditional methods on smaller
datasets. However, detecting ADR patterns in large datasets requires tools for
scalable data processing, machine learning for data analysis, and interactive
visualization. To our knowledge no existing pharmacoepidemiology tool supports
all three requirements. We have therefore created a tool for interactive
exploration of patterns in prescription datasets with millions of samples. We
use Spark to preprocess the data for machine learning and for analyses using
SQL queries. We have implemented models in Keras and the scikit-learn
framework. The model results are visualized and interpreted using live Python
coding in Jupyter. We apply our tool to explore a 384 million prescription data
set from the Norwegian Prescription Database combined with a 62 million
prescriptions for elders that were hospitalized. We preprocess the data in two
minutes, train models in seconds, and plot the results in milliseconds. Our
results show the power of combining computational power, short computation
times, and ease of use for analysis of population scale pharmacoepidemiology
datasets. The code is open source and available at:
https://github.com/uit-hdl/norpd_prescription_analyse
Visualisation of semantic architectural information within a game engine environment
Because of the importance of graphics and information within the domain of architecture, engineering and construction (AEC), an appropriate combination of visualisation technology and information management technology is of utter importance in the development of appropriately supporting design and construction applications. We therefore started an investigation of two of the newest developments in these domains, namely game engine technology and semantic web technology. This paper documents part of this research, containing a review and comparison of the most prominent game engines and documenting our architectural semantic web. A short test-case illustrates how both can be combined to enhance information visualisation for architectural design and construction
Designing and evaluating the usability of a machine learning API for rapid prototyping music technology
To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable
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