A D4D Data Visualization

Orange Data for Development is an open data challenge, encouraging research teams around the world to use four datasets of anonymous call patterns of Orange’s Ivory Coast subsidiary, to help address society development questions in novel ways. The data sets are based on anonymized Call Detail Records extracted from Orange’s customer base, covering the months of December 2011 to April 2012.

D4D Viz Approach

Our team used the geolocation data from call detail records extracted from Orange’s customer base in order to know in which areas the customers have been moving around, to help us discover the morning and evening rush hours: the time when users were commuting between their place of residence and place of work.


We used Python for crunching the numbers and D3.js for creating the visualization.

Bar Chart

The bar chart shows the total population density at a fixed time slot. Rush hours can be identified by the two peaks that emerge every day, one in the morning and one in the afternoon.


The choropleth shows how the population density flows over time, as people move from one region to another. Notice how the density increases (areas get darker) as the time gets closer to the rush hours.

Take a look!

If you want to see it running, you can either visit this link for a demo with simulated data, or clone the repo and start a local web server.

D4D Challenge completed!!

As we explained in a previous post, during the last months we have been working on a project for the Orange D4D Challenge. Our main task has been analyzing and visualizing the provided mobile communication datasets (collected in Ivory Coast from December, 2011 to April, 2012) looking for relevant and original findings for the society of this West-African country, that is, showing deductions in an easy and friendly way which helps government and NGOs to perform more accurate and correct decisions.

Therefore, it could be said that the project is divided into 2 dimensions:

a) Scientific side: gathering information on similar research projects (behavioural data, data mobile commuting, people dynamics), using different tools and strategies to manipulate such big amounts of data in an efficient way (BigData, Hadoop, Pig), checking diverse visualization options (Excel and R charts, Gephi, GIS tools like qGIS, uDig, ArcGIS, Leaflet, Polymaps, D3.js…), reflecting on the kind of extracted conclusions and their possible interpretations.

b) Cooperative side: mobile communication data are plentiful and their structure is really simple. There is a great deal of applications where this sort of data can play a really important role. Moreover, as their nature is so related with all of us (communications), many of the inferred ideas can be quickly tied with common people’s daily lives. Leaving aside those solutions with a major interest for companies (improving business basing on potential customers’ behavior, habits and trends, elaborating more sophisticated and customized marketing campaigns…), we have focused on those ones which can contribute to make people day-to-day fairer and more comfortable, especially in underdeveloped countries (detecting commuting patterns allowing public transport policy improvements, more adequate urban planning, determining massive usage of hospitals, police stations…).

Let’s describe how the project was faced and developed:

1) Studying related research project, both from private companies and from universities.
2) Storing and Processing datasets with cutting-edge technologies Hadoop/PIG, Mongo, Python, GIT…
3) Statistics: normalizations, means, dispersions, medians…
4) Charts: Excel, R, Python
5) Visualizations: network diagrams (Gephi), Kernel Densities Estimations maps(qGIS, ArcGIS)…
6) Web: customizable and interactive animations, making easier to display and spread the reached conclusions (Leaflet, D3.js, CartoCSS, TileMill, Mapnik, Polymaps)
7) Paper: collecting all our discoveries to write a final report (Latex)

As a result of the whole process, many interesting findings and ideas:

a) A designed and implemented mathematical model to detect geospatial-temporal commuting patterns.
b) Distinction between commuters and non-commuters, apart from their evolution during every day and for each city.
c) Identification of time periods (hours, week days) depending on the amount of phone calls; moreover, those regions or cities originating them are also located.
d) A set of charts and maps which illustrate the previous model, making easier to deduce interesting findings.
e) Discovery of the diary commuting pattern for this specific dataset (morning peak, central valley, evening peak).
f) An online application to display all this information in a friendly and customizable way
g) Drafting new R&D open worklines with a igh potential (clustering, replicating algrithms with other datasets, tesellations, use of DTW & LCS operators…)

Summing up, we are really glad and satisfied with the work we carried out. It has been a fantastic opportunity which has allowed us to learn a lot in different knowledge areas. The key for all this, motivation, doubtless; since the very beginning and during the whole challenge we have been very thrilled trying to collaborate and, at the same time, eager to learn from each other.

D4D Challenge accepted!

We are glad to announce that a multidisciplinary team formed by engineers and scientifics of Paradigma Labs and Spanish National Research Council will take part into Orange “Data for Development” D4D Challenge.

Orange challenge

Orange “Data for Development” – D4D – is an open data challenge, encouraging research teams around the world to use four datasets of anonymous call patterns of Orange’s Ivory Coast subsidiary, to help address society development questions in novel ways. The data sets are based on anonymized Call Detail Records extracted from Orange’s customer base, covering the months of December 2011 to April 2012.

Paradigma labs team

Short abstract

Our idea is to use the geolocation data from the antennas processing the mobile phones calls in order to know which sub-prefectures the customers have been getting around. The main goal of our project is developing spatio-temporal models to detect patterns for the different sub-prefectures, including some other factors related to the region and/or time: wealth, development, infrastructure, investment, grants…

By means of GIS technology, we will be able to apply our generated models to the gathered data and to analyze their correlations over the Côte d’Ivoire surface, working with geographical layers: landcover, roads map, railways lines, water sources… Consequently, the reached conclusions from our study will be properly visualized, allowing a better explanation of the facts.

In the near future, some other measures could be included. For instance, hospitals and police stations locations, their calls rate… Thus, we could know its real use, being able to improve their service to the citizens: dangerous areas, crowded hospitals…

People involved

At Paradigma tecnologico:

At Spanish National Research Council:
  • Esther Perez Asensio
At Complutense University:
  • Jose Luis Fernandez-Pacheco Saez

Throughput analysis with Continuous-time Markov Chains simulations and design of realiable cloud services system based on Gunicorn, Tornado and Iptables

At this moment a lot of companies offer end-point services (data providers, semantic analysis, …) that we can integrate with our applications. However, when designing our own service, it could be tough find the ideal parameters to configure it and to find the best software to make it scalable and highly available.

Continuous-Time Markov Chains (Yin, G. et all, 1998) (CTMC) provides an ideal framework to estimate this most important parameters, and by means of simulations we can find them. An special model of CTMC which belongs to the Queuing Theory (Breuer, L. et all, 2005) is the M/M/c/K model, and modelize our service like a queuing system, implying that our system holds:

  • c: the number of parallel process
  • K: is the maximum number of clients waiting in the queue
  • Input: Poisson
  • Service: Exponential

E.g.: The next CTMC can represent a simple M/M/3/4 queuing system (Download .dot):

As seen in the picture above, grey nodes mean that n-3 clients exist waiting in the queue and the last state will be the red node (#7) which implies that at this moment incoming  clients will be reject of our system.
Like a CTMC we can derivate the equilibrium equations or we can use directly the formulae of the model M/M/c/K. By means of the software developed at ParadigmaLabs we are able to simulate several configurations on this model, and get other features too, e.g.:
M/M/c/K model simulation

    Lambda: 40.0000
    Mu: 30.0000
    c: 3.0000
    K: 7.0000
    Stability: True (rho = 0.4444)

    Average number of clients (l) = 1.4562
    Average length (lq) = 0.1268
    Average waiting time for a client into the queue (w) = 0.0365

    Average waiting time into the system (wq) = 0.0032

    P_0 = 0.2550368777
    P_1 = 0.340049170234300
    P_2 = 0.226699446822867
    P_3 = 0.100755309699052
    P_4 = 0.044780137644023
    P_5 = 0.019902283397344
    P_6 = 0.008845459287708
    P_7 = 0.003931315238981
    [Total Probability: 1.0]

Elapsed time: 0.00025105

Once we have calculated the best-fit values for our system, it is time to present our service based on a Wikipedia Semantic Graph. The next picture shows the main structure creating relations between articles and categories:

So, in first instance our service will perform lookup queries in order to identify Entities onto a text. We can see the result of a query to our service:

Up to this point, we have calculated several parameters for our system: Incoming Lambda (λ)Service Mu (μ)c (parallel servers) and K (queue length). To ensure the system holds these several constrains we should implement a two layers throttle system.

  1. IPTABLES filter: Several clients will try to access to our system, however only a portion of them will succeed.
  2. LOGIC filter: Is a software based filter and perform this throttle by means of user tokens. It applies temporal restrictions handling  the incoming rate of each user.

Therefore, the following software help us to implement these restrictions:

  • Iptables filter: Using Iptables ( we can restrict the incoming connections avoiding denial-of-service attack (DoS).
  • Logic filter: Using a time control and token manager script we can deal with this problem.
  • Several parallel servers and queue system: We set up Gunicorn to run several tornado servers to implement the queue restrictions.
<span style="font-size: 11.000000pt; font-family: 'SFTT1095';">nohup gunicorn <strong> --workers 3 </strong><strong>--backlog 7</strong></span><span style="font-size: 11.000000pt; font-family: 'SFTT1095';">
                --limit-request-line 4094  </span><span style="font-family: SFTT1095; font-size: 11pt;">--limit-request-fields 4  </span><span style="font-family: SFTT1095; font-size: 11pt;">-b</span><span style="font-family: SFTT1095; font-size: 11pt;">-k egg:gunicorn#tornado server:app &</span>

A sample tornado server scaffold for our service could be:

># -*- coding: utf-8 -*-
import tornado.ioloop
from tornado.web import Application, RequestHandler, asynchronous
from tornado.ioloop import IOLoop
# Main class
class NerService(tornado.web.RequestHandler):
    def get(self):
# run application
app = tornado.web.Application([
    (r"/", NerService, dict(...parameters...),
# To test single server file"

Finally, after applying this configuration we have simulated several incoming rates (testing sundry numbers of clients too) getting the next service performance statistics represented in the picture below:

Summing up:

  • Using wikipedia categories and articles, we are able to detect a huge range of Entities.
  • Wikipedia is always updated in real time, therefore we have a updated NER (Name Entities Recognition).
  • We can use Gunicorn to run and manage serveral service instances.
  • We have implemented a throttle system to restrict the maximum number of requests per second. Also the way to restrict the general incoming rate by means of iptables is provided.
  • It is proven to be neccessary to simulate different invocations of our services using Queuing Theory formulae to find the best-fit paramaters like λ, μ, ρ, L, Lq, W, Wq.


Openinfluence is an open-metric developed at Paradigmalabs and tries to define the relevance of each user in Twitter. It is open because you can see the formula and contribute to improve it. You can see the formula in the picture below:

As you can see, the formula has two main components “Popularity” and “Influence“. Popularity is related to static properties of your social network. It’s some kind of “potential influence”, the beforehand capability of getting your tweets spread.Influence is related to the propagation and repercussion of each of your tweets, the effective reach of your messages.

We have applied successfully this metric in several analysis, e.g.: during the Andalusian elections campaign or UX Spain Conference.

Currently we can represent this formula with the next plot:

We are involved in trying to improve this metric, because the two main parts have the same weight in the formula. However, is this metric more related to Influence? Is the formula below better?

We have tested Openinfluence with the next dataset. In the picture below, you can see the number of followers degree of each user in the sample (in logarithmic scale):

The correlation between Popularity and Influence (dataset) shows that the main stream of people has more or less the same Popularity and Influence. By means of the structure of this formula, some users have 0 of influence and n>0 popularity however they have not null relevance.

Suggest us your point of view !! We are expecting to improve it!!

Retweet Monitor plugin for Gephi

With this Plugin for Gephi, ParadigmaLabs wants to provide the community with an useful tool to analyze Twitter information. We have encapsulated all the complexity behind a simple button. A retweet is one of the main actions for information propagation, and now you can make your own analysis in real time by means of Gephi and the Retweet Monitor plugin.

It´s internal mechanisms are fairly simple. The software will connect to the TwitterStream, then apply(if desired) a content filter. All the information gathered will be displayed by Gephi, and you can then apply the standard algorithms and layouts in order to create a representative visualization.

read more…

15th October on Twitter: Global Revolution ‘Mapped’

#15oct and #ows

15th October 2011 was a world-level milestone day: Millions of people aroud the globe occupied the streets to protest against global financial crisis, influenced in a great measure by the power of social networks, essentially Twitter. The protest movement, tagged as #15o and #15oct was heavily based upon #15m (Spain) and #ows (“Occupy Wall Street”), social movements around the notion that 99% of the people is NOT responsible of the ‘financial games’ played by a minor 1% that get rich in the process of sucking their wealth from the remaining 99% (#weare99)

The Process

We present evolution through time of related Twitter activity, around 15th October 2011. Taking a Dataset of 1.2 million tweets (ranging from 13th October to 18th October), we worked to offer some global (geolocated) visualizations, local visualizations (centered around New York, San Francisco, Barcelona and Madrid) and, lastly, a visualization about how did the associated hashtags evolved in that time frame.
read more…

Information Propagation in Twitter’s Network

It’s well-known that Twitter’s most powerful use is as tool for real-time journalism. Trying to understand its social connections and outstanding capacity to propagate information, we have developed a mathematical model to identify the evolution of a single tweet.

The way a tweet is spread through the network is closely related with Twitter’s retweet functionality, but retweet information is fairly incomplete due to the fight for earning credit/users by means of being the original source/author. We have taken into consideration this behavior and our approach uses text similarity measures as complement of retweet information. In addition, #hashtags and urls are included in the process since they have an important role in Twitter’s information propagation. read more…

We contribute!

Paradigma Labs is glad to announce its contribution to Apache Mahout’s project!

In our developments we realized that NoSQL databases were not supported natively by the machine learning library, so, trying to fill this “gap”, we decided to create a DataModel for MongoDB support. After the favorable results obtained, we determined to share it with Mahout’s community (and yeah!, the code have been accepted).

Unfortunately, this DataModel will be released with Mahout’s version 0.6, but you can access the code at Mahout’s SVN.

As bonus, we have been developed a recommender system based on this DataModel, all wrapped by a REST service. Source code, installation/configuration and more can be found at GitHub.
Hope you enjoy it!

Related reading: Mahout in Action
Related viewing: Social Recommendations

Paradigma Labs is glad to announce its contribution to Apache Mahout‘s project!

In our developments we realized that NoSQL databases were not supported natively by the machine learning library, so, trying to fill this “gap”, we decided to create a DataModel for MongoDB support. After the favorable results obtained, we determined to share it with Mahout’s community (and yeah!, the code have been accepted). read more…

Howto: Hadoop Streaming with Ruby and Wukong


It’s been a while since Google introduced their MapReduce framework for distributed computing on large data sets on clusters of computers. Paradigma Labs was thinking of trying Apache Hadoop to run this kind of tasks, so it was the proper choice to run some web scraping we have in our hands. I was the designated developer for the task, and my love for Ruby on Rails led me to give Hadoop Streaming a try so I could avoid Java to write the scripts. Many will agree on the virtues of Ruby, specially considering gems like Mechanize for web scraping and Rails gems like ActiveResource for accessing REST web services and ActiveRecord for ORM with a database like MySQL.

All the howto steps after the jump.
read more…