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XDATA-software.json
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XDATA-software.json
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[
{
"DARPA Program":"XDATA",
"Program Teams":[
"Aptima, Inc."
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Network Query by Example",
"Internal Link":"",
"External Link":"https://github.com/Aptima/pattern-matching",
"Public Code Repo":"https://github.com/Aptima/pattern-matching.git",
"Instructional Material":"2014-07",
"Stats":"pattern-matching",
"Description":"Hadoop MapReduce-over-Hive based implementation of network query by example utilizing attributed network pattern matching. (Java)",
"Internal Code Repo":"",
"License":[
"ALv2"
],
"Languages":[
"Java"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Boeing",
"University of Pittsburgh"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"SMILE-WIDE: A scalable Bayesian network library",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/ANL/Boeing+-+Pitt",
"External Link":"http://smilewide.github.io/main/",
"Public Code Repo":"https://github.com/SmileWide/main.git",
"Instructional Material":"2014-07",
"Stats":"main",
"Description":"SMILE-WIDE is a scalable Bayesian network library. Initially, it is a version of the SMILE library, as in SMILE With Integrated Distributed Execution. The general approach has been to provide an API similar to the existing API SMILE developers use to build \"local,\" single-threaded applications. However, we provide \"vectorized\" operations that hide a Hadoop-distributed implementation. Apart from invoking a few idioms like generic Hadoop command line argument parsing, these appear to the developer as if they were executed locally. (Java)",
"Internal Code Repo":"tools\\analytics\\boeing-pitt\\smile-wide-final-summercamp",
"License":[
"ALv2"
],
"Languages":[
"Java"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Carnegie Mellon University"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Support Distribution Machines",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/pages/viewpage.action?pageId=6717624",
"External Link":"https://github.com/dougalsutherland/py-sdm",
"Public Code Repo":"https://github.com/dougalsutherland/py-sdm.git",
"Instructional Material":"2014-07",
"Stats":"py-sdm",
"Description":"Python implementation of the nonparametric divergence estimators described by Barnabas Poczos, Liang Xiong, Jeff Schneider (2011). Nonparametric divergence estimation with applications to machine learning on distributions. Uncertainty in Artificial Intelligence. ( http://autonlab.org/autonweb/20287.html ) and also their use in support vector machines, as described by Dougal J. Sutherland, Liang Xiong, Barnabas Poczos, Jeff Schneider (2012). Kernels on Sample Sets via Nonparametric Divergence Estimates. ( http://arxiv.org/abs/1202.0302 ). (Python)",
"Internal Code Repo":"tools\\analytics\\cmu",
"License":[
"BSD"
],
"Languages":[
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Blaze",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"https://github.com/ContinuumIO/blaze",
"Public Code Repo":"https://github.com/ContinuumIO/blaze.git",
"Instructional Material":"2014-07",
"Stats":"blaze",
"Description":"Blaze is the next-generation of NumPy. It is designed as a foundational set of abstractions on which to build out-of-core and distributed algorithms over a wide variety of data sources and to extend the structure of NumPy itself. Blaze allows easy composition of low level computation kernels (C, Fortran, Numba) to form complex data transformations on large datasets. In Blaze, computations are described in a high-level language (Python) but executed on a low-level runtime (outside of Python), enabling the easy mapping of high-level expertise to data without sacrificing low-level performance. Blaze aims to bring Python and NumPy into the massively-multicore arena, allowing it to leverage many CPU and GPU cores across computers, virtual machines and cloud services. (Python)",
"Internal Code Repo":"tools\\analytics\\continuum\\blaze",
"License":[
"BSD"
],
"Languages":[
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Infrastructure"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Numba",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"https://github.com/numba/numba",
"Public Code Repo":"https://github.com/numba/numba.git",
"Instructional Material":"2014-07",
"Stats":"numba",
"Description":"Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the LLVM compiler infrastructure to compile Python syntax to machine code.<br/><br/>It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code is translated to Python C-API calls effectively removing the \"interpreter\" but not removing the dynamic indirection.<br/><br/>Numba is also not a tracing just in time (JIT) compiler. It compiles your code before it runs either using run-time type information or type information you provide in the decorator.<br/><br/>Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. (Python)",
"Internal Code Repo":"tools\\analytics\\continuum\\numba",
"License":[
"BSD"
],
"Languages":[
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Infrastructure"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Bokeh",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"http://bokeh.pydata.org",
"Public Code Repo":"https://github.com/ContinuumIO/bokeh.git",
"Instructional Material":"2014-07",
"Stats":"bokeh",
"Description":"Bokeh (pronounced bo-Kay or bo-Kuh) is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. (Python/JavaScript/Coffeescript)",
"Internal Code Repo":"tools/visualization/continuum/bokeh; tools/visualization/continuum/bokehjs",
"License":[
"BSD"
],
"Languages":[
"Python",
"JavaScript",
"Coffeescript"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics",
"Indiana University"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Abstract Rendering",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"http://www.github.com/JosephCottam/AbstractRendering",
"Public Code Repo":"https://github.com/JosephCottam/AbstractRendering.git",
"Instructional Material":"2014-07",
"Stats":"AbstractRendering",
"Description":"Information visualization rests on the idea that a meaningful relationship can be drawn between pixels and data. This is most often mediated by geometric entities (such as circles, squares and text) but always involves pixels eventually to display. In most systems, the pixels are tucked away under levels of abstraction in the rendering system. Abstract Rendering takes the opposite approach: expose the pixels and gain powerful pixel-level control. This pixel-level power is a complement to many existing visualization techniques. It is an elaboration on rendering, not an analytic or projection step, so it can be used as an epilogue to many existing techniques. In standard rendering, geometric objects are projected to an image and represented on that image's discrete pixels. The source space is an abstract canvas that contains logically continuous geometric primitives and the target space is an image that contains discrete colors. Abstract Rendering fits between these two states. It introduces a discretization of the data at the pixel-level, but not necessarily all the way to colors. This enables many pixel-level concerns to be efficiently and concisely captured. (Java)",
"Internal Code Repo":"/tools/visualization/continuum/AbstractRendering/",
"License":[
"BSD"
],
"Languages":[
"Java"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"CDX",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"https://github.com/ContinuumIO/cdx",
"Public Code Repo":"https://github.com/ContinuumIO/cdx.git",
"Instructional Material":"2014-07",
"Stats":"cdx",
"Description":"Software to visualize the structure of large or complex datasets / produce guides that help users or algorithms gauge the quality of various kinds of graphs & plots. (JavaScript/Coffeescript/Python)",
"Internal Code Repo":"/tools/visualization/continuum/",
"License":[
"BSD"
],
"Languages":[
"JavaScript",
"Coffeescript",
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Continuum Analytics",
"Indiana University"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Stencil",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Continuum+Analytics+Home+Page",
"External Link":"https://github.com/JosephCottam/Stencil",
"Public Code Repo":"https://github.com/JosephCottam/Stencil.git",
"Instructional Material":"2014-07",
"Stats":"Stencil",
"Description":"Stencil is a grammar-based approach to visualization specification at a higher-level. (Clojure)",
"Internal Code Repo":"tools\\visualizations\\continuum\\Stencil",
"License":[
"BSD"
],
"Languages":[
"Clojure"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Data Tactics Corporation"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Vowpal Wabbit",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/ANL/Cloud+Analytics+for+Structure+Extractions+%28CASE%29+Group",
"External Link":"https://github.com/JohnLangford/vowpal_wabbit",
"Public Code Repo":"https://github.com/JohnLangford/vowpal_wabbit.git",
"Instructional Material":"2014-07",
"Stats":"vowpal_wabbit",
"Description":"The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. Support is available through the mailing list. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). The code should be easily usable. Its only external dependence is on the boost library, which is often installed by default. (C)",
"Internal Code Repo":"tools\\analytics\\dt",
"License":[
"BSD"
],
"Languages":[
"C"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Data Tactics Corporation"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Circuit",
"Internal Link":"",
"External Link":"http://www.gocircuit.org/",
"Public Code Repo":"https://github.com/gocircuit/circuit.git",
"Instructional Material":"2014-07",
"Stats":"",
"Description":"Go Circuit reduces the human development and sustenance costs of complex massively-scaled systems nearly to the level of their single-process counterparts. It is a combination of proven ideas from the Erlang ecosystem of distributed embedded devices and Go's ecosystem of Internet application development. Go Circuit extends the reach of Go's linguistic environment to multi-host/multi-process applications. (Go)",
"Internal Code Repo":"",
"License":[
"ALv2"
],
"Languages":[
"Go"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Infrastructure"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Georgia Tech / GTRI"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"libNMF: A high-performance library for nonnegative matrix factorization and hierarchical clustering",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/pages/viewpage.action?pageId=7274597",
"External Link":"",
"Public Code Repo":"Not Ready for Release",
"Instructional Material":"2014-07",
"Stats":"",
"Description":"LibNMF is a high-performance, parallel library for nonnegative matrix factorization on both dense and sparse matrices written in C++. Implementations of several different NMF algorithms are provided, including multiplicative updating, hierarchical alternating least squares, nonnegative least squares with block principal pivoting, and a new rank2 algorithm. The library provides an implementation of hierarchical clustering based on the rank2 NMF algorithm.",
"Internal Code Repo":"tools\\analytics\\gatech\\_x000D_\n",
"License":[
"ALv2"
],
"Languages":[
""
],
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""
],
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"Industry":[
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],
"Functionality":[
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],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Giant Oak, Inc."
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Markov modulated Poisson process for event detection",
"Internal Link":"",
"External Link":"https://github.com/giantoak/MMPP",
"Public Code Repo":"https://github.com/giantoak/MMPP.git",
"Instructional Material":"http://giantoak.github.io/MMPP_Tutorial/",
"Stats":"MMPP",
"Description":"R-implementation of Markov Modulated Poisson Process for unsupervised event detection in time series of counts data. (R)",
"Internal Code Repo":"",
"License":[
"GPLv2"
],
"Languages":[
"R"
],
"Platform Requirements":[
""
],
"Dependent modules":[
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],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
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],
"Industry":[
""
],
"Functionality":[
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],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"IBM Research"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"SKYLARK: Randomized Numerical Linear Algebra and ML",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/pages/viewpage.action?pageId=2752591",
"External Link":"http://xdata-skylark.github.io/",
"Public Code Repo":"2014-05-15",
"Instructional Material":"2014-07",
"Stats":"",
"Description":"SKYLARK implements Numerical Linear Algebra (NLA) kernels based on sketching for distributed computing platforms. Sketching reduces dimensionality through randomization, and includes Johnson-Lindenstrauss random projection (JL); a faster version of JL based on fast transform techniques; sparse techniques that can be applied in time proportional to the number of nonzero matrix entries; and methods for approximating kernel functions and Gram matrices arising in nonlinear statistical modeling problems. We have a library of such sketching techniques, built using MPI in C++ and callable from Python, and are applying the library to regression, low-rank approximation, and kernel-based machine learning tasks, among other problems. (C++/Python)",
"Internal Code Repo":"tools\\analytics\\ibm\\skylark",
"License":[
"ALv2"
],
"Languages":[
"C++",
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Institute for Creative Technologies",
"USC"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Immersive Body-Based Interactions",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/pages/viewpage.action?pageId=7274668",
"External Link":"",
"Public Code Repo":"http://code.google.com/p/svnmimir/source/checkout",
"Instructional Material":"2014-07",
"Stats":"immersive_body-based_interactions",
"Description":"Provides innovative interaction techniques to address human-computer interaction challenges posed by Big Data. Examples include:<br>* Wiggle Interaction Technique: user-induced motion to speed visual search.<br>* Immersive Tablet Based Viewers: low-cost 3D virtual reality fly-throughs of data sets.<br>* Multi-touch interfaces: browsing/querying multi-attribute and geospatial data, hosted by SOLR.<br>* Tablet-based visualization controller: eye-free rapid interaction with visualizations.",
"Internal Code Repo":"tools\\visualizations\\usc-ict",
"License":[
"ALv2"
],
"Languages":[
""
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
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],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Johns Hopkins University"
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"igraph",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/ANL/Johns+Hopkins+University",
"External Link":"https://github.com/igraph/xdata-igraph/",
"Public Code Repo":"https://github.com/igraph/xdata-igraph.git",
"Instructional Material":"2014-07",
"Stats":"xdata-igraph",
"Description":"igraph provides a fast generation of large graphs, fast approximate computation of local graph invariants, fast parallelizable graph embedding. API and Web-service for batch processing graphs across formats. (C)",
"Internal Code Repo":"tools\\analytics\\jhu",
"License":[
"GPLv2"
],
"Languages":[
"C"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
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],
"Component module URLs":[
""
],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Analytics"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Trifacta (Stanford, University of Washington, Kitware, Inc. Team)"
],
"Contributors":[
"Trifacta"
],
"Sub-contractors":[
""
],
"Software":"Vega",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Visual+Design+Environment-Kitware+Home",
"External Link":"https://github.com/trifacta/vega",
"Public Code Repo":"https://github.com/trifacta/vega.git",
"Instructional Material":"2014-07",
"Stats":"vega",
"Description":"Vega is a visualization grammar, a declarative format for creating and saving visualization designs. With Vega you can describe data visualizations in a JSON format, and generate interactive views using either HTML5 Canvas or SVG. (JavaScript)",
"Internal Code Repo":"tools\\visualizations\\kitware\\vega",
"License":[
"BSD"
],
"Languages":[
"JavaScript"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
""
],
"Component module URLs":[
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],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Kitware, Inc."
],
"Contributors":[
""
],
"Sub-contractors":[
""
],
"Software":"Tangelo",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Visual+Design+Environment-Kitware+Home",
"External Link":"http://kitware.github.io/tangelo/",
"Public Code Repo":"https://github.com/Kitware/tangelo.git",
"Instructional Material":"2014-07",
"Stats":"tangelo",
"Description":"Tangelo provides a flexible HTML5 web server architecture that cleanly separates your web applications (pure JavaScript, HTML, and CSS) and web services (pure Python). This software is bundled with some great tools to get you started. (JavaScript/Python)",
"Internal Code Repo":"tools\\visualizations\\kitware\\tangelo; tools\\visualizations\\kitware\\xdata-apps",
"License":[
"ALv2"
],
"Languages":[
"JavaScript",
"Python"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
""
],
"Component modules":[
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],
"Component module URLs":[
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],
"Industry":[
""
],
"Functionality":[
""
],
"Categories":[
"Visualization"
],
"New Date":"",
"Update Date":""
},
{
"DARPA Program":"XDATA",
"Program Teams":[
"Harvard",
"Kitware, Inc."
],
"Contributors":[
"Johannes Kepler University Linz"
],
"Sub-contractors":[
""
],
"Software":"LineUp",
"Internal Link":"https://xd-wiki.xdata.data-tactics-corp.com:8443/display/VIS/Visual+Design+Environment-Kitware+Home",
"External Link":"http://lineup.caleydo.org",
"Public Code Repo":"https://github.com/Caleydo/org.caleydo.vis.lineup.demos.git",
"Instructional Material":"2014-07",
"Stats":"org.caleydo.vis.lineup.demos",
"Description":"LineUp is a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time. (Java)",
"Internal Code Repo":"tools\\visualizations\\kitware\\xdata-apps",
"License":[
"BSD"
],
"Languages":[
"Java"
],
"Platform Requirements":[
""
],
"Dependent modules":[
""
],
"Dependent module URLs":[
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],
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],
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],
"Categories":[
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],
"New Date":"",
"Update Date":""
},