The EU SatCen organised on Thursday 4th of May 2017 (11.00-12.00 CET) an Hangout session on “Big Data for Secure Societies” in the framework of the BigDataEurope project and with regard to the “Secure Societies” Horizon 2020 Societal Challenge. The Hangout was the fourth of a scheduled series in the BigDataEurope project for the Secure Societies domain and it has been followed by attendees coming from EC, EU entities, ESA, private companies, universities and other public entities from a variety of domains in Secure Societies.
Posts Tagged ‘Satellite’
The 3rd ESA International Security Symposium (ISS), held in Frascati (Rome) on 13th and 14th of February, focused on how challenges related to security of Big Data can impact the activities and security of international and national organisations. The event brought together public and private stakeholders of the space sectors such as national and international space organizations, industry and academia, providing an excellent chance for networking and exchanging information.
The EU Satellite Centre (SatCen) participated with an oral presentation entitled “Information Security Challenges in the Big Data Era” given by Alexis Letulier, Head of the SatCen IT Division.
After having introduced the SatCen mission and its involvement in different Big Data projects (e.g. BigDataEurope), the presentation elaborated on how to face the Security challenges and threats in the Big Data era. To this aim, SatCen has already several tools (from physical to digital security) in place at operational level, based on best practises and customized to its specific aims.
Besides the key technical points, it was also highlighted the importance of the Value of the data. Such V is becoming one of the most significant amongst the Vs of the Big Data paradigm (widely defined as Volume, Variety, Velocity, Veracity and indeed Value): thus, the development of information extraction techniques (e.g. Machine Learning) is crucial to really benefit from Big Data technologies. These techniques should be developed and deployed using European infrastructures, in order to boost the digital society and the economic growth in Europe.
With the term spatial (or geospatial) data we describe data or information identified by a geographic location on Earth. Spatial data are, therefore, described with coordinates and the information contained; this characteristic allow them to be mapped, visualized and analysed with applications like Geographic Information Systems (GIS).
Common spatial data are generally divided in two categories: raster and vector. A raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid), where each cell contains a value representing information. One of the most common example of a raster is a satellite image, where each pixel is described by a number and a specific location on the surface. Therefore, raster data are used to present continuous information, e.g. land cover, temperature and height. Vector data are used to represent discrete features through points, lines or polygons. Features like rivers, lakes, boundaries and punctual information are generally represented by vectors data.
With the Big Data revolution, massive amounts of geospatial data are being collected at a rate that increases every day so that a new term was coined to describe the union of Big Data and Spatial Data: Spatial Big Data (SBD).
Datasets for security applications do comply with the SBD definition. In more detail, the rapidly increasing volume, velocity, variety, veracity and value of data coming from spatial sources raises new issues such as the management of extremely large and complex datasets and their exploitation. Regarding the volume, the Sentinel satellites, the main European Earth Observation satellites, will deliver each day images on the order of terabytes (the sole Sentinel 1 and Sentinel 2 will deliver 2.6 Tb of images per day)
Data for security are not restricted to satellite images. Every data which can be associated to a geographic position can be used: aerial imagery (e.g. from Remotely Piloted Vehicles), intelligence sources, GPS data, media, public data, web-based communities, user-generated content, video sharing sites, wikis, blogs, other publicly available sources, etc.
On top of these data, applications like evacuation route planning, monitoring of critical infrastructures, surveillance and tracking for border security and maritime control are now possible. New challenges in infrastructure development, analytics capabilities and insights processes have therefore to be tackled in order to acquire, store, manage, query, analyse and disseminate this bulk of information.
Recent advances in technology aim to address these challenges. The emphasis is placed on developing generic, scalable and fault-tolerant systems that support distributed processing. The state-of-the-art in this direction is the lambda architecture, which is robust against hardware failures and human mistakes, while being able to serve a wide range of workloads and use cases. In essence, it supports two procedures: batch processing, which parallelizes off-line applications that handle large volumes of immutable data stored on disk, and speed processing, which extracts the most essential information from a stream of data in real time (i.e. with low latency). Many implementations of this architecture are publicly available on the Web, such as Apache Spark. The goal of BigDataEurope is to get the best of the available solutions and to combine them in an easy-to-use, versatile and robust infrastructure.