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Inria Industry Meeting

Bertifier

New Interactions for Crafting Tabular Visualizations.
Bertifier is a Web app for rapidly creating tabular visualizations from spreadsheets. It directly draws from Jacques Bertin’s matrix analysis method, whose goal was to "simplify without destroying" by encoding cell values visually and grouping similar rows and columns. Bertifier has the potential to bring Bertin’s method to a wide audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.

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Sparklificator

Exploring the Placement and Design of Word-Scale Visualizations
Sparklificator (name comes from adding sparklines to a textual document) is a general open-source jQuery library that eases the process of integrating word-scale visualizations into HTML documents, and provides a range of options for adjusting the position (on top, to the right, as an overlay), size, and spacing of visualizations within the text. The library includes default visualizations, including small line and bar charts, and can also be used to integrate custom word-scale visualizations created using web-based visualization toolkits such as D3

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RDT

In many data driven applications, it is important to know whether a dependency is stronger between one pair of measurements or another.  This may arise in the case that one wishes to know whether there is a stronger dependency between the effectiveness of a drug and one genetic marker or another.  We similarly may be interested in testing the topology of relationships between three languages, or genetic sequences.  Each of these activities at its core asks the question of the relative statistical dependency between pairs of variables.  RDT provides a fast and accurate statistical test that can be flexibly adapted to a wide range of industrial and scientific applications.

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TDA

Topological Data Analysis methods
TDA provides a set of tools and methods to infer topological and geometric informations about the structure of possibly big and complex data, that are not reachable through other classical methods.
Recent TDA developments are motivated by real-world and big data constraints: they focus on the development of statistical approaches to infer relevant topological information without considering the whole data, even when data are corrupted by noise and outliers. This leads to the design of very fast and easily parallelizable algorithms for TDA, and opens the door to the combination of TDA tools with modern learning and artificial intelligence technologies.

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ToMATo

Topological Mode Analysis Tool
ToMATo is a novel and flexible scheme for classification and clustering of various kinds of data, based on a topological approach. It provides the user with feedback about the data, in the form of a 2-dimensional diagram that provably reflects the importance of each cluster and allows the user to select a relevant number of clusters to process the data. Combined with stochastic tools, ToMATo can also perform soft clustering and assign to each data point the probability of belonging to each of the clusters, for greater flexibility.

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Lamark Metadata

Lamark-Metadata is a web Platform that simplifies the access to digital image metadata. Thanks to innovative technologies, fast and secure image identification is possible for large-scale image database.
From a connected device, Lamark-Metadata is used to extract certified information linked to digital images. The user can robustly sign his images and create new metadata fields that increase the value of his assets.
Photo agencies and photographs use Lamark-Metadata to easily and safely communicate rights information, but also to create new means of interaction with their images even after distribution.
Lamark-Metadata is based on patented image recognition and watermarking technologies.

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Cliquesquare

RDF data management platform based on Hadoop architecture.
RDF (Ressource Description Framework) is the data format for the semantic web. CliqueSquare allows storing and querying very large volumes of RDF data in a massively parralel fashion, in a Hadoop cluster. The system uses its own partitioning and storage model for the RDF triples in the cluster. CliqueSquare evaluates queries expressed in a dialect of the SPARQL RDF query language. It is particularly efficient when processing complex queries, because it is capable of translating them into MapReduce programs guaranteed to have the minimum number of successive jobs. Given the high overhead of a MapReduce job, this advantage is considerable.

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WaRG

WarG is a warehouse-­‐style analytics platform on RDF graphs.
RDF (Ressource Description Framework) is the data format for the semantic web. WaRG (Warehousing RDF graph) is an analytical platform  specially designed for the analysis of RDF data. WaRG alows defining RDF analytical schemas, comprising classes and properties interesting for the analysis. The analytical schema can then be materialized, leading to an instance (RDF graph) refined for the needs of the analysis. The analytical schema can also be automatically built from the input RDF instance. Finally, RDF analytical queries can be specified and lead to RDF analysis cubes.

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Scikit-Learn

Scikit-learn can be used as a middleware for prediction tasks. For example, many Web startups adapt Scikit-learn to predict buying behavior of users, provide product recommendations, detect trends or abusive behavior (fraud, spam). Scikit-Learn is used to extract the structure of complex data (text, images) and classify such data with techniques relevant to the state of the art.
Easy to use, efficient and accessible to non data-science experts, Scikit-Learn is an increasingly popular machine learning library in Python. In a data exploration step, the user can enter a few lines on an interactive interface and immediately sees the results of his request.  Scikit-learn does not come with a graphical interface, it is a prediction engine.
Scikit-learn is developed in open source, and available under the BSD license.

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Mixmod

Many-purpose software for data mining and statistical learning
Mixmod is a free toolbox for data mining and statistical learning designed for large and high-dimensional data sets. Mixmod provides reliable estimation algorithms and relevant model selection criteria. It has been successfully applied to  marketing, credit scoring, epidemiology, genomics and reliability among other domains.
Its particularity is to propose a model-based approach leading to a lot of methods for classification and clustering.
It allows to assess the stability of the results with simple and thorough scores and provides an easy-to-use graphical user interface (mixmodGUI) and functions for the R (Rmixmod) and Matlab (mixmodForMatlab) environments.

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