Blogs

20
Jul
From catastrophe to action: how Twitter can save lives
  • Synergic Partners
  • 37 Views
  • 0 Comment
  • catástrofes . detección de catástrofes . Geolocalización . redes sociales . twitter .

On April 16, 2016, at 6:52 p.m., the coastal population of northwestern Ecuador was devastated by an earthquake measuring 7.6 on the Richter scale. In 30 minutes, the main infrastructures were destroyed, resulting in a huge hit on tourism and the economy for the region, in addition to the hundreds of dead and injured left by the disaster. In this article, we analyze the complex context of the hours following the earthquake and propose a powerful tool for disaster management, based on Twitter, that may be of use in similar future cases. Hashtags as the first alerts In the hours following the earthquake, a state of emergency was declared, causing an unusual amount of activity on social networks, specifically Facebook…

30
Jun
New Opportunities Generated by the End of Roaming
  • Álvaro Alegría
  • 311 Views
  • 0 Comment
  • Big Data . Data Monetization . Data Value . Roaming .

On June 15th 2017, the elimination of roaming surcharges went into effect as established by the European Parliament and Council, decided November 25th 2015 by EU Regulation 2015/2120. This means that from this day forward, users of mobile telephone and internet services will not have to pay any additional costs for said services within the EU, known as RLAH (Roam Like At Home). From the users' point of view, it's all advantages. However, from the operators' point of view this measure implicates a huge loss in revenue. In the "International Roaming Benchmark Data Report” released by the Body of European Regulators for Electronic Communications (BEREC), the average monthly cost for the user (including pre-pay and postpaid users) is detailed. For…

30
May
Improving Effectiveness at a Call Center with Machine Learning
  • Víctor Gonzalez
  • 779 Views
  • 0 Comment
  • Algorithms . Big Data . Data Science . Machine Learning . Procesamiento de Lenguaje Natural .

Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company. The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques…

18
May
Predicting crime: fact or fiction?
  • Santiago González
  • 956 Views
  • 0 Comment
  • Big Data . Crime Predicction . Innovation . Predicción de crímenes .

According to the OMS, one of the main worries of global society today is violence and crime, making avoiding crime and creating centers of intelligence to do so the main objective for police and judicial systems. In this sense, as director of innovation and influenced by science fiction, I set out to analyze this situation. Using the traditional scientific method, the first step was to ask questions and hypothesize: Is it possible to predict the place, date and time of a crime? And if so, to what extent, in what kind of detail? Do we have experience with this? Who can offer us information? Has anyone done this before and, if so, what were the results? Thanks to our previous…

26
Apr
Internet of Things: In my fridge?
  • Pedro de Gregorio
  • 1198 Views
  • 0 Comment
  • Big Data . Data . Data Analytcics . internet of things . IoT .

The Internet of Things is a concept that is often tied to a vision of the future, mid- and long-term horizons, reflections on what can be achieved and an infinity of possibilities that can open up in a moment that never quite seems to arrive. There are many high expectations attached to this concept, leading, inevitably, to skepticism on the part of those who do not see the returns that they had in mind or, worse yet, of those who were never excited about it and gave up with a simple “Why would I want the internet in my fridge?”. When a concept or a paradigm such as the Internet of Things appears on the scene, it is difficult to…

19
Jan
An economist… working on the good side
  • Sergio Mayor
  • 2268 Views
  • 2 Comments
  • Big Data . Big Data & Business . Big Data Consulting . Big Data Value . Carme Artigas . Fraude Fiscal . Synergic Partners .

Before I start, I would like to introduce myself to better contextualize this post. My name is Sergio Mayor Martin, I am an economist and I am currently working as a Business Consultant at Synergic Partners, pioneer in the world of Big Data in Spain. For us, the students of Economic Sciences, the name of our discipline has always been under discussion, with the second half of the title of those studies often being left off and leaving us with just plain “Economics.” This is due to traditional thinking, based on everything the term “Science” carries along with it, what exactly it should be and why economics is not one. This affirmation with reference to the world of economics is…

22
Nov

If I ask about the best way to manage the storeroom of a neighborhood shop, I am sure that we would quickly reach the conclusion that there are certain aspects that are absolutely necessary, such as, for example, that it be organized (in order to find the product when a customer asks for it), that it have an inventory (in order to know how many units I have, which models, their conditions and whether or not they are close to expiration) and that it be locked (in order to avoid someone coming and stealing the merchandise or simply destroying everything so that we cannot sell it). If, instead of the neighborhood shop, we were to discuss Amazon, no one would…

08
Nov
A Brief History of Machine Learning
  • Víctor Gonzalez
  • 2944 Views
  • 0 Comment
  • Big Data . ciencia de los datos . Data Science . Machine Learning .

As members of the Machine Learning community, it would be a good idea for us all to have an idea of the history of the sector we work in. Although we are currently living through an authentic boom in Machine Learning, this field has not always been so prolific, going through periods of high expectations and advances as well as “winters” of severe stagnation. Birth [1952 - 1956] 1950 — Alan Turing creates the “Turing Test” to determine whether or not a machine is truly intelligent. In order to pass the test, the machine must be capable of making a human believe that it is another human instead of a computer. 1952 — Arthur Samuel writes the first computer program…

18
Oct
IoT: When Material Acquires Intelligence
  • David_Morillas
  • 3121 Views
  • 0 Comment
  • No tags

The term IoT refers to the Internet of Things, a sort of interaction between the digital world and the physical one, a platform for connecting people, objects and environments. In the environment of IoT, the moment in which something connects, it becomes capable of accessing the power of computation of the cloud or, in other words, it becomes intelligent. The implementation of this technology and the trend towards automatization is unstoppable; each second an average of 127 things establishes a connection with the internet for the first time. All of this is possible thanks to what Moore’s law has not been able to accomplish, which says that as electronic computation grows in potential, it decreases in price exponentially. These improvements…

17
Sep
Warnings about normalizing data
  • Santiago Morante
  • 3135 Views
  • 2 Comments
  • No tags

For many machine learning algorithms, normalizing the data of analysis is a must. A supervised example would be neural networks. It is known that normalizing the input data to the networks improve the results. If you don't believe me it's ok (no offense taken), but you may prefer to believe Yann Le Cunn (Director of AI Research in Facebook and founding father of convolutional networks) by checking section 4.3 of this paper. You can catch up the idea with the first sentence of the section: Convergence [of backprop] is usually faster if the average of each input variable over the training set is close to zero. Among other things, one reason is that when the neural network tries to correct…

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