Blog entries

Graphing version progress

2009/07/06 by Arthur Lutz

As you might have noticed we've upgraded http://www.logilab.org and http://www.cubicweb.org to CubicWeb 3.3 and a bunch of cubes were upgraded too. We can now benefit from a few cool bugfixes and features on those two forges.

One of them I like and wish to mention is the graphing of a project's progress as a Burn Down Chart, you can see an example below. We're using the some jQuery magic here, and so you can roll over the mouse to get more info on the graph... (not on the screenshot below). This type of graph is generated on all the version views... This is particularly useful on some of our extranets to see the progress of a version (and if tickets were added along the way).

http://www.cubicweb.org/image/344424?vid=download

For the coders out there you can check out cubicweb/web/views/plots.py and the example in the forge cube.


Reusing OpenData from Data.gouv.fr with CubicWeb in 2 hours

2011/12/07 by Vincent Michel

Data.gouv.fr is great news for the OpenData movement!

Two days ago, the French government released thousands of data sets on http://data.gouv.fr/ under an open licensing scheme that allows people to access and play with them. Thanks to the CubicWeb semantic web framework, it took us only a couple hours to put some of that open data to good use. Here is how we mapped the french railway system.

http://www.cubicweb.org/file/2110281?vid=download

Train stations in french Britany

Source Datasets

We used two of the datasets available on data.gouv.fr:

  • Train stations : description of the 6442 train stations in France, including their name, type and geographic coordinates. Here is a sample of the file

    441000;St-Germain-sur-Ille;Desserte Voyageur;48,23955;-1,65358
    441000;Montreuil-sur-Ille;Desserte Voyageur-Infrastructure;48,3072;-1,6741
    
  • LevelCrossings : description of the 18159 level crossings on french railways, including their type and location. Here is a sample of the file

    558000;PN privé pour voitures avec barrières sans passage piétons accolé;48,05865;1,60697
    395000;PN privé pour voitures avec barrières avec passage piétons accolé public;;48,82544;1,65795
    

Data Model

Given the above datasets, we wrote the following data model to store the data in CubicWeb:

class Location(EntityType):
    name = String(indexed=True)
    latitude = Float(indexed=True)
    longitude = Float(indexed=True)
    feature_type = SubjectRelation('FeatureType', cardinality='?*')
    data_source = SubjectRelation('DataGovSource', cardinality='1*', inlined=True)

class FeatureType(EntityType):
    name = String(indexed=True)

class DataGovSource(EntityType):
    name = String(indexed=True)
    description = String()
    uri = String(indexed=True)
    icon = String()

The Location object is used for both train stations and level crossings. It has a name (text information), a latitude and a longitude (numeric information), it can be linked to multiple FeatureType objects and to a DataGovSource. The FeatureType object is used to store the type of train station or level crossing and is defined by a name (text information). The DataGovSource object is defined by a name, a description and a uri used to link back to the source data on data.gouv.fr.

http://www.cubicweb.org/file/2110311?vid=download

Schema of the data model

Data Import

We had to write a few lines of code to benefit from the massive data import feature of CubicWeb before we could load the content of the CSV files with a single command:

$ cubicweb-ctl import-datagov-location datagov_geo gare.csv-fr.CSV  --source-type=gare
$ cubicweb-ctl import-datagov-location datagov_geo passage_a_niveau.csv-fr.CSV  --source-type=passage

In less than a minute, the import was completed and we had:

  • 2 DataGovSource objects, corresponding to the two data sets,
  • 24 FeatureType objects, corresponding to the different types of locations that exist (e.g. Non exploitée, Desserte Voyageur, PN public isolé pour piétons avec portillons or PN public pour voitures avec barrières gardé avec passage piétons accolé manoeuvré à distance),
  • 24601 Locations, corresponding to the different train stations and level crossings.

Data visualization

CubicWeb allows to build complex applications by assembling existing components (called cubes). Here we used a cube that wraps the Mapstraction and the OpenLayers libraries to display information on maps using data from OpenStreetMap.

In order for the Location type defined in the data model to be displayable on a map, it is sufficient to write the following adapter:

class IGeocodableAdapter(EntityAdapter):
      __regid__ = 'IGeocodable'
      __select__ = is_instance('Location')
      @property
      def latitude(self):
          return self.entity.latitude
      @property
      def longitude(self):
          return self.entity.longitude

That was it for the development part! The next step was to use the application to browse the structure of the french train network on the map.

Train stations in use:

http://www.cubicweb.org/file/2110279?vid=download

Train stations not in use:

http://www.cubicweb.org/file/2110280?vid=download

Zooming on some parts of the map, for example Brittany, we get to see more details and clicking on the train icons gives more information on the corresponding Location.

Train stations in use:

http://www.cubicweb.org/file/2110281?vid=download

Train stations not in use:

http://www.cubicweb.org/file/2110282?vid=download

Since CubicWeb separates querying the data and displaying the result of a query, we can switch the view to display the same data in tables or to export it back to a CSV file.

http://www.cubicweb.org/file/2110313?vid=download

Querying Data

CubicWeb implements a query langage very similar to SPARQL, that makes the data available without the need to learn a specific API.

  • Example 1: http:/some.url.demo/?rql=Any X WHERE X is Location, X name LIKE "%miny"

    This request gives all the Location with a name that ends with "miny". It returns only one element, the Firminy train station.

http://www.cubicweb.org/file/2110286?vid=download
  • Example 2: http:/some.url.demo/?rql=Any X WHERE X is Location, X name LIKE "%ny"

    This request gives all the Location with a name that ends with "ny", and return 112 trainstations.

http://www.cubicweb.org/file/2110287?vid=download
  • Example 3: http:/some.url.demo/?rql=Any X WHERE X latitude < 47.8, X latitude>47.6, X longitude >-1.9, X longitude<-1.8

    This request gives all the Location that have a latitude between 47.6 and 47.8, and a longitude between -1.9 and -1.8.

    We obtain 11 Location (9 levelcrossings and 2 trainstations). We can map them using the view mapstraction.map that we describe previously.

    http://www.cubicweb.org/file/2110288?vid=download
  • Example 4: http:/domainname:8080/?rql=Any X WHERE X latitude < 47.8, X latitude>47.6, X longitude >-1.9, X longitude<-1.8, X feature_type F, F name "Desserte Voyageur"

    Will limit the previous results set to train stations that are used for passenger service:

    http://www.cubicweb.org/file/2110289?vid=download
  • Example 5: http:/domainname:8080/?rql=Any X WHERE X feature_type F, F name "PN public pour voitures sans barrières sans SAL"&vid=mapstraction.map

    Finally, one can map all the level crossings for vehicules without barriers (there are 3704):

    http://www.cubicweb.org/file/2110290?vid=downloadhttp://www.cubicweb.org/file/2110291?vid=download

As you could see in the last URL, the map view was chosen directly with the parameter vid, meaning that the URL is shareable and can be easily included in a blog with a iframe for example.

Data sharing

The result of a query can also be "displayed" in RDF, thus allowing users to download a semantic version of the information, without having to do the preprocessing themselves:

<rdf:Description rdf:about="cwuri24684b3a955d4bb8830b50b4e7521450">
  <rdf:type rdf:resource="http://ns.cubicweb.org/cubicweb/0.0/Location"/>
  <cw:cw_source rdf:resource="http://some.url.demo/"/>
  <cw:longitude rdf:datatype="http://www.w3.org/2001/XMLSchema#float">-1.89599</cw:longitude>
  <cw:latitude rdf:datatype="http://www.w3.org/2001/XMLSchema#float">47.67778</cw:latitude>
  <cw:feature_type rdf:resource="http://some.url.demo/7222"/>
  <cw:data_source rdf:resource="http://some.url.demo/7206"/>
</rdf:Description>

Conclusion

For someone who knows the CubicWeb framework, a couple hours are enough to create a CubicWeb application that stores, displays, queries and shares data downloaded from http://www.data.gouv.fr/

The full source code for the above will be released before the end of the week.

If you want to see more of CubicWeb in action, browse http://data.bnf.fr or learn how to develop your own application at http://docs.cubicweb.org/


Geonames in CubicWeb !

2011/12/14 by Vincent Michel

CubicWeb is a semantic web framework written in Python that has been succesfully used in large-scale projects, such as data.bnf.fr (French National Library's opendata) or Collections des musées de Haute-Normandie (museums of Haute-Normandie).

CubicWeb provides a high-level query language, called RQL, operating over a relational database (PostgreSQL in our case), and allows to quickly instantiate an entity-relationship data-model. By separating in two distinct steps the query and the display of data, it provides powerful means for data retrieval and processing.

In this blog, we will demonstrate some of these capabilities on the Geonames data.

Geonames

Geonames is an open-source compilation of geographical data from various sources:

"...The GeoNames geographical database covers all countries and contains over eight million placenames that are available for download free of charge..." (http://www.geonames.org)

The data is available as a dump containing different CSV files:

  • allCountries: main file containing information about 8,000,000 places in the world. We won't detail the various attributes of each location, but we will focus on some important properties, such as population and elevation. Moreover, admin_code_1 and admin_code_2 will be used to link the different locations to the corresponding AdministrativeRegion, and feature_code will be used to link the data to the corresponding type.
  • admin1CodesASCII.txt and admin2Codes.txt detail the different administrative regions, that are parts of the world such as region (Ile-de-France), department (Department of Yvelines), US counties...
  • featureCodes.txt details the different types of location that may be found in the data, such as forest(s), first-order administrative division, aqueduct, research institute, ...
  • timeZones.txt, countryInfo.txt, iso-languagecodes.txt are additional files prodividing information about timezones, countries and languages. They will be included in our CubicWeb database but won't be explained in more details here.

The Geonames website also provides some ways to browse the data: by Countries, by Largest Cities, by Highest mountains, by postal codes, etc. We will see that CubicWeb could be used to automatically create such ways of browsing data while allowing far deeper queries. There are two main challenges when dealing with such data:

  • the number of entries: with 8,000,000 placenames, we have to use efficient tools for storing and querying them.
  • the structure of the data: the different types of entries are separated in different files, but should be merged for efficient queries (i.e. we have to rebuild the different links between entities, e.g Location to Country or Location to AdministrativeRegion).

Data model

With CubicWeb, the data model of the application is written in Python. It defines different entity classes with their attributes, as well as the relationships between the different entity classes. Here is a sample of the schema.py that we have used for Geonames data:

class Location(EntityType):
    name = String(maxsize=1024, indexed=True)
    uri = String(unique=True, indexed=True)
    geonameid = Int(indexed=True)
    latitude = Float(indexed=True)
    longitude = Float(indexed=True)
    feature_code = SubjectRelation('FeatureCode', cardinality='?*', inlined=True)
    country = SubjectRelation('Country', cardinality='?*', inlined=True)
    main_administrative_region = SubjectRelation('AdministrativeRegion',
                              cardinality='?*', inlined=True)
    timezone = SubjectRelation('TimeZone', cardinality='?*', inlined=True)
    ...

This indicates that the main Location class has a name attribute (string), an uri (string), a geonameid (integer), a latitude and a longitude (both floats), and some relation to other entity classes such as FeatureCode (the relation is named feature_code), Country (the relation is named country), or AdministrativeRegion called main_administrative_region.

The cardinality of each relation is classically defined in a similar way as RDBMS, where * means any number, ? means zero or one and 1 means one and only one.

We give below a visualisation of the schema (obtained using the /schema relative url)

http://www.cubicweb.org/file/2124618?vid=download

Import

The data contained in the CSV files could be pushed and stored without any processing, but it is interesting to reconstruct the relations that may exist between different entities and entity classes, so that queries will be easier and faster.

Executing the import procedure took us 80 minutes on regular hardware, which seems very reasonable given the amount of data (~7,000,000 entities, 920MB for the allCountries.txt file), and the fact that we are also constructing many indexes (on attributes or on relations) to improve the queries. This import procedure uses some low-level SQL commands to load the data into the underlying relational database.

Queries and views

As stated before, queries are performed in CubicWeb using RQL (Relational Query Language), which is similar to SPARQL, but with a syntax that is closer to SQL. This language may be used to query directly the concepts while abstracting the physical structure of the underlying database. For example, one can use the following request:

Any X LIMIT 10 WHERE X is Location, X population > 1000000,
    X country C, C name "France"

that means:

Give me 10 locations that have a population greater than 1000000, and that are in a country named "France"

The corresponding SQL query is:

SELECT _X.cw_eid FROM cw_Country AS _C, cw_Location AS _X
WHERE _X.cw_population>1000000
      AND _X.cw_country=_C.cw_eid AND _C.cw_name="France"
LIMIT 10

We can see that RQL is higher-level than SQL and abstracts the details of the tables and the joins.

A query returns a result set (a list of results), that can be displayed using views. A main feature of CubicWeb is to separate the two steps of querying the data and displaying the results. One can query some data and visualize the results in the standard web framework, download them in different formats (JSON, RDF, CSV,...), or display them in some specific view developed in Python.

In particular, we will use the mapstraction.map which is based on the Mapstraction and the OpenLayers libraries to display information on maps using data from OpenStreetMap. This mapstraction.map view uses a feature of CubicWeb called adapter. An adapter adapts a class of entity to some interface, hence views can rely on interfaces instead of types and be able to display entities with different attributes and relations. In our case, the IGeocodableAdapter returns a latitude and a longitude for a given class of entity (here, the mapping is trivial, but there are more complex cases... :) ):

class IGeocodableAdapter(EntityAdapter):
      __regid__ = 'IGeocodable'
      __select__ = is_instance('Location')
      @property
      def latitude(self):
          return self.entity.latitude
      @property
      def longitude(self):
          return self.entity.longitude

We will give some results of queries and views later. It is important to notice that the following screenshoots are taken without any modification of the standard web interface of CubicWeb. It is possible to write specific views and to define a specific CSS, but we only wanted to show how CubicWeb could handle such data. However, the default web template of CubicWeb is sufficient for what we want to do, as it dynamically creates web pages showing attributes and relations, as well as some specific forms and javascript applets adapted directly to the data (e.g. map-based tools). Last but not least, the query and the view could be defined within the url, and thus open a world of new possibilities to the user:

http://baseurl:port/?rql=The query that I want&vid=Identifier-of-the-view

Facets

We will not get into too much details about Facets, but let's just say that this feature may be used to determine some filtering axis on the data, and thus may be used to post-filter a result set. In this example, we have defined four different facets: on the population, on the elevation, one the feature_code and one the main_administrative_region. We will see illustration of these facets below.

We give here an example of the definition of a Facet:

class LocationPopulationFacet(facet.RangeFacet):
    __regid__ = 'population-facet'
    __select__ = is_instance('Location')
    order = 2
    rtype = 'population'

where __select__ defines which class(es) of entities are targeted by this facet, order defines the order of display of the different facets, and rtype defines the target attribute/relation that will be used for filtering.

Geonames in CubicWeb

The main page of the Geoname application is illustrated in the screenshot below. It provides general information on the database, in particular the number of entities in the different classes:

  • 7,984,330 locations.
  • 59,201 administrative regions (e.g. regions, counties, departments...)
  • 7,766 languages.
  • 656 features (e.g. types of location).
  • 410 time zones.
  • 252 countries.
  • 7 continents.
http://www.cubicweb.org/file/2124617?vid=download

Simple query

We will first illustrate the possibilites of CubicWeb with the simple query that we have detailed before (that could be directly pasted in the url...):

Any X LIMIT 10 WHERE X is Location, X population > 1000000,
    X country C, C name "France"

We obtain the following page:

http://www.cubicweb.org/file/2124615?vid=download

This is the standard view of CubicWeb for displaying results. We can see (right box) that we obtain 10 locations that are indeed located in France, with a population of more than 1,000,000 inhabitants. The left box shows the search panel that could be used to launch queries, and the facet filters that may be used for filtering results, e.g. we may ask to keep only results with a population greater than 4,767,709 inhabitants within the previous results:

http://www.cubicweb.org/file/2124616?vid=download

and we obtain now only 4 results. We can also notice that the facets are linked: by restricting the result set using the population facet, the other facets also restricted their possibilities.

Simple query (but with more information !)

Let's say that we now want more information about the results that we have obtained previously (for example the exact population, the elevation and the name). This is really simple ! We just have to ask within the RQL query what we want (of course, the names N, P, E of the variables could be almost anything...):

Any N, P, E LIMIT 10 WHERE X is Location,
    X population P, X population > 1000000,
    X elevation E, X name N, X country C, C name "France"
http://www.cubicweb.org/file/2124619?vid=download

The empty column for the elevation simply means that we don't have any information about elevation.

Anyway, we can see that fetching particular information could not be simpler! Indeed, with more complex queries, we can access countless information from the Geonames database:

Any N,E,LA,LO ORDERBY E DESC LIMIT 10  WHERE X is Location,
      X latitude LA, X longitude LO,
      X elevation E, NOT X elevation NULL, X name N,
      X country C, C name "France"

which means:

Give me the 10 highest locations (the 10 first when sorting by decreasing elevation) with their name, elevation, latitude and longitude that are in a country named "France"
http://www.cubicweb.org/file/2124626?vid=download

We can now use another view on the same request, e.g. on a map (view mapstraction.map):

Any X ORDERBY E DESC LIMIT 10  WHERE X is Location,
       X latitude LA, X longitude LO, X elevation E,
       NOT X elevation NULL, X country C, C name "France"
http://www.cubicweb.org/file/2124631?vid=download

And now, we can add the fact that we want more results (20), and that the location should have a non-null population:

Any N, E, P, LA, LO ORDERBY E DESC LIMIT 20  WHERE X is Location,
       X latitude LA, X longitude LO,
       X elevation E, NOT X elevation NULL, X population P,
       X population > 0, X name N, X country C, C name "France"
http://www.cubicweb.org/file/2124632?vid=download

... and on a map ...

http://www.cubicweb.org/file/2124633?vid=download

Conclusion

In this blog, we have seen how CubicWeb could be used to store and query complex data, while providing (among other...) Web-based views for data vizualisation. It allows the user to directly query data within the URL and may be used to interact with and explore the data in depth. In a next blog, we will give more complex queries to show the full possibilities of the system.


"Data Fast-food": quick interactive exploratory processing and visualization of complex datasets with CubicWeb

2012/01/19 by Vincent Michel

With the emergence of the semantic web in the past few years, and the increasing number of high quality open data sets (cf the lod diagram), there is a growing interest in frameworks that allow to store/query/process/mine/visualize large data sets.

We have seen in previous blog posts how CubicWeb may be used as an efficient knowledge management system for various types of data, and how it may be used to perform complex queries. In this post, we will see, using Geonames data, how CubicWeb may perform simple or complex data mining and machine learning procedures on data, using the datamining cube. This cube adds powerful tools to CubicWeb that make it easy to interactively process and visualize datasets.

At this point, it is not meant to be used on massive datasets, for it is not fully optimized yet. If you try to perform a TF-IDF (term frequency–inverse document frequency) with a hierarchical clustering on the full dbpedia abstracts dataset, be prepared to wait. But it is a promising way to enrich the user experience while playing with different datasets, for quick interactive exploratory datamining processing (what I've called the "Data fast-food"). This cube is based on the scikit-learn toolbox that has recently gained a huge popularity in the machine learning and Python community. The release of this cube drastically increases the interest of CubicWeb for data management.

The Datamining cube

For a given query, similarly to SQL, CubicWeb returns a result set. This result set may be presented by a view to display a table, a map, a graph, etc (see documentation and previous blog posts).

The datamining cube introduces the possibility to process the result set before presenting it, for example to apply machine learning algorithms to cluster the data.

The datamining cube is based on two concepts:

  • the concept of processor: basically, a processor transforms a result set in a numpy array, given some criteria defining the mathematical processing, and the columns/rows of the result set to be taken into account. The numpy-array is a polyvalent structure that is widely used for numerical computation. This array could thus be efficiently used with any kind of datamining algorithms. Note that, in our context of knowledge management, it is more convenient to return a numpy array with additional meta-information, such as indices or labels, the result being stored in what we call a cw-array. Meta-information may be useful for display, but is not compulsory.
  • the concept of array-view: the "views" are basic components of CubicWeb, distinguish querying and displaying the data is key in this framework. So, on a given result set, many different views can be applied. In the datamining cube, we simply overload the basic view of CubicWeb, so that it works with cw-array instead of result sets. These array-views are associated to some machine learning or datamining processes. For example, one can apply the k-means (clustering process) view on a given cw-array.

A very important feature is that the processor and the array-view are called directly through the URL using the two related parameters arid (for ARray ID) and vid (for View ID, standard in CubicWeb).

http://www.cubicweb.org/file/2154793?vid=download

Processors

We give some examples of basic processors that may be found in the datamining cube:

  • AttributesAsFloatArrayProcessor (arid='attr-asfloat'): This processor turns all Int, BigInt and Float attributes in the result set to floats, and returns the corresponding array. The number of rows is equal to the number of rows in the result set, and the number of columns is equal to the number of convertible attributes in the result set.
  • EntityAsFloatArrayProcessor (arid='entity-asfloat'): This processor performs similarly to the AttributesAsFloatArrayProcessor, but keeps the reference to the entities used to create the numpy-array. Thus, this information could be used for display (map, label, ...).
  • AttributesAsTokenArrayProcessor (arid='attr-astoken'): This processor turns all String attributes in the result set in a numpy array, based on a Word-n-gram analyze. This may be used to tokenize a set of strings.
  • PivotTableCountArrayProcessor (arid='pivot-table-count'): This processor is used to create a pivot table, with a count function. Other functions, such as sum or product also exist. This may be used to create some spreadsheet-like views.
  • UndirectedRelationArrayProcessor (arid='undirected-rel'): This processor creates a binary numpy array of dimension (nb_entities, nb_entities), that represents the relations (or corelations) between entities. This may be used for graph-based vizualisation.

We are also planning to extend the concept of processor to sparse matrix (scipy.sparse), in order to deal with very high dimensional data.

Array Views

The array views that are found in the datamining cube, are, for most of them, used for simple visualization. We used HTML-based templates and the Protovis Javascript Library.

We will not detail all the views, but rather show some examples. Read the reference documentation for a complete and detailed description.

Examples on numerical data

Histogram

The request:

Any LO, LA WHERE X latitude LA, NOT X latitude NULL, X longitude LO,  NOT X longitude NULL,
X country C, NOT X elevation NULL, C name "France"

that may be translated as:

All couples (latitude, longitude) of the locations in France, with an elevation not null

and, using vid=protovis-hist and arid=attr-asfloat

http://www.cubicweb.org/file/2154795?vid=download

Scatter plot

Using the notion of view, we can display differently the same result set, for example using a scatter plot (vid=protovis-scatterplot).

http://www.cubicweb.org/file/2156233?vid=download

Another example with the request:

Any P, E WHERE X is Location, X elevation E, X elevation >1, X population P,
X population >10, X country CO, CO name "France"

that may be translated as:

All couples (population, elevation) of locations in France,
with a population higher than 10 (inhabitants),and an elevation higher than 1 (meter)

and, using the same vid (vid=protovis-scatterplot) and the same arid (arid=attr-asfloat)

http://www.cubicweb.org/file/2154802?vid=download

If a third column is given in the result set (and thus in the numpy array), it will be encoded in the size/color of each dot of the scatter plot. For example with the request:

Any LO, LA, E WHERE X latitude LA, NOT X latitude NULL, X longitude LO,  NOT X longitude NULL,
X country C, NOT X elevation NULL, X elevation E, C name "France"

that may be translated as:

All tuples (latitude, longitude, elevation) of the locations in France, with an elevation not null

and, using the same vid (vid=protovis-scatterplot) and the same arid (arid=attr-asfloat), we can visualize the elevation on a map, encoded in size/color

http://www.cubicweb.org/file/2154805?vid=download

Another example with the request:

Any LO, LA LIMIT 50000 WHERE X is Location, X population  >1000, X latitude LA, X longitude LO,
X country CO, CO name "France"

that may be translated as:

All couples (latitude, longitude) of 50000 locations in France, with a population higher than 100 (inhabitants)
http://www.cubicweb.org/file/2156095?vid=download

There also exist some AreaChart view, LineArray view, ...

Examples on relational data

Relational Matrix (undirected graph)

The request:

Any X,Y WHERE X continent CO, CO name "North America", X neighbour_of Y

that may be translated as:

All neighbour countries in North America

and using the vid='protovis-binarymap' and arid='undirected-rel'

http://www.cubicweb.org/file/2154796?vid=download

Relational Matrix (directed graph)

If we do not want a symmetric matrix, i.e. if we want to keep the direction of a link (X,Y is not the same relation as Y,X), we can use the directed*rel array processor. For example, with the following request:

Any X,Y LIMIT 20 WHERE X continent Y

that may be translated as:

20 countries and their continent

and using the vid='protovis-binarymap' and arid='directed-rel'

http://www.cubicweb.org/file/2154797?vid=download

Force directed graph

For a dynamic representation of relations, we can use a force directed graph. The request:

Any X,Y WHERE X neighbour_of Y

that may be translated as:

All neighbour countries in the World.

and using the vid='protovis-forcedirected' and arid='undirected-rel', we can see the full graph, with small independent components (e.g. UK and Ireland)

http://www.cubicweb.org/file/2154800?vid=download

Again, a third column in the result set could be used to encode some labeling information, for example the continent.

The request:

Any X,Y,CO WHERE X neighbour_of Y, X continent CO

that may be translated as:

All neighbour countries in the World, and their corresponding continent.

and again, using the vid='protovis-forcedirected' and arid='undirected-rel', we can see the full graph with the continents encoded in color (Americas in green, Africa in dark blue, ...)

http://www.cubicweb.org/file/2154801?vid=download

Dendrogram

For hierarchical information, one can use the Dendrogram view. For example, with the request:

Any X,Y WHERE X continent Y

that may be translated as:

All couple (country, continent) in the World

and using vid='protovis-dendrogram' and arid='directed-rel', we have the following dendrogram (we only show a part due to lack of space)

http://www.cubicweb.org/file/2154806?vid=download

Unsupervised Learning

We have also developed some machine learning view for unsupervised learning. This is more a proof of concept than a fully optimized development, but we can already do some cool stuff. Each machine learning processing is referenced by a mlid. For example, with the request:

Any LO, LA WHERE X is Location, X elevation E, X elevation >1, X latitude LA, X longitude LO,
X country CO, CO name "France"

that may be translated as:

All couples (latitude, longitude) of the locations in France, with an elevation higher than 1

and using vid='protovis-scatterplot' arid='attr-asfloat' and mlid='kmeans', we can construct a scatter plot of all couples of latitude and longitude in France, and create 10 clusters using the kmeans clustering. The labeling information is thus encoded in color/size:

http://www.cubicweb.org/file/2154804?vid=download

Download

Finally, we have also implement a download view, based on the Pickle of the numpy-array. It is thus possible to access remotely any data within a Python shell, allowing to process them as you want. Changing the request can be done very easily by changing the rql parameter in the URL. For example:

import pickle, urllib
data = pickle.loads(urllib.open('http://mydomain?rql=my request&vid=array-numpy&arid=attr-asfloat'))

Follow up of IRI conference about Museums and the Web #museoweb

2012/04/12 by Arthur Lutz

I attented the conference organised by IRI in a series of conferences about "Muséologie, muséographie et nouvelles formes d’adresse au public" (hashtag #museoweb). This particular occurence was about "Le Web devient audiovisuel" (the web is also audio and video content). Here are a few notes and links we gathered. The event was organised by Alexandre Monnin @aamonnz.

http://polemictweet.com/2011-2012-museo-audiovisuel/images/slide4_museo_fr.png

Yves Raimond from the BBC

Yves Raimond @moustaki made a presentation about his work at the BBC around semantic web technologies and speech recognition over large quantities of digitized archives. Parts of the BCC web sites use semantic web data as the database and do mashups with external sources of data (musicbrainz, dbpedia, wikipedia). For example Tom Waits has an html web page : http://www.bbc.co.uk/music/artists/c3aeb863-7b26-4388-94e8-5a240f2be21b add .rdf at the end of the URL http://www.bbc.co.uk/music/artists/c3aeb863-7b26-4388-94e8-5a240f2be21b.rdf

He also made an introduction about the ABC-IP The Automatic Broadcast Content Interlinking Project and the Kiwi-API project that uses CMU Sphinx on Amazon Web Services to process large quantities of archives. A screenshot of Kiwi-API is shown on the BBC R&D blog. The code should be open sourced soon and should appear on the BBC R&D github page.

Following his presentation, the question was asked if using Wikipedia content on an institutional web site would be possible in France, I pointed to the use of Wikipedia on http://data.bnf.fr , for example at the bottom of the Victor Hugo page.

Raphaël Troncy about Media Fragments

Raphaël Troncy @rtroncy made a presentation about "Media Fragments" which will enable sharing parts of a video on the web. Two major features : the sharing of specific extracts and the optimization of bandwith use when streaming the extract (usefull for mobile devices for example). It is a W3C working draft : http://www.w3.org/TR/media-frags-reqs/. Here are a few links of demos and players :

Part of the presentation was about the ACAV project done jointly with Dailymotion : http://www.capdigital.com/projet-acav/

The slides of his presentation are available here : http://www.slideshare.net/troncy/addressing-and-annotating-multimedia-fragments

IRI presentation

Vincent Puig @vincentpuig and Raphaël Velt @raphv made a presentation of various projects led by IRI :

http://www.iri.centrepompidou.fr/wp-content/themes/IRI-Theme/images/logo-iri-petit_fr_fr.png

Final words

The technologies seen during this conference are often related to semantic web technologies or at least web standards. Some of the visualizations are quite impressive and could mean new uses of the Web and an inspiration for CubicWeb projects.

A few of the people present at the conference will be attending or presenting talks at SemWeb.Pro which will take place in Paris on the 2nd and 3rd of may 2012.