Track & Know is a Horizon2020 project, with a focus on Big Data. More specifically, Track & Know will research, develop and exploit a new software framework that aims at increasing the efficiency of Big Data. This will be applied in the transport, mobility, motor insurance and health sectors. Track & Know aims to introduce innovative software stacks and Toolboxes addressing new emerging cross-sector markets related to automotive transportations and urban mobility in general: commercial IoT services; car insurance; and, healthcare management. The addressed markets have significant industrial and commercial impacts for EU enterprises.
The project will design and develop an innovative platform for the collection and analysis of large marine surveillance data. The vision of the project is to efficiently and quickly process, integrate and analyze these data in order to create (a) a “Combined Real-time Business Image” and (b) a “Combined Historic Image” of Marine Surveillance. The project aspires to further develop already developed data processing techniques and to develop innovative software solutions utilizing cutting-edge research to be carried out within the project on in-memory databases, algorithms and parallel programming models and analytical methods data (big data analytics).
With GPS enabled devices and other positioning systems, mobility behavior of individuals is captured for online or historical data analysis. For example, car insurance companies have started to issue policies with respect to the driving behavior which is captured through a GPS device installed under a special agreement.
The aim of the CloudIX project is to conduct innovative research on indexing and advanced query processing in the cloud, focusing mainly in the MapReduce programming model. CloudIX aims to develop a unifying framework that treats multidimensional data in the cloud as “first-class” citizens, by providing built-in support for storage, effective access and efficient query processing, without compromising the salient features of MapReduce. The key objective of CloudIX is to increase the performance of MapReduce jobs significantly, by providing mechanisms for selective access to data, avoidance of wasteful processing, and support of early termination during query processing.
DATA SIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility, grounded on massive amounts of Big data of various types and from various sources, e.g. GPS, mobile phones and social networking sites, with the goal to forecast the nation-wide consequences.
The aim of EICOS is to provide the methodology, the theoretical and modeling foundations as well as the algorithmic techniques and the necessary software architecture that will facilitate the personalization, integration, and evolution management facilities for information ecosystems that operate over a decentralized infrastructure for a large variety of data types.
The general goal of the GeoPKDD project is to develop theory, techniques and systems for knowledge discovery and delivery, based on new automated privacy-preserving methods for extracting user-consumable forms of knowledge from large amounts of raw data referenced in space and in time.
SemaGrow – Data intensive techniques to boost the real-time performance of global agricultural data infrastructures
SemaGrow (a) develops scalable and robust semantic storage and indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; (b) develops query decomposition, source selection, and distributed querying methods that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation; and (c) rigorously tests its components and overall architecture over real, complex, interconnected datasets comprising data and document collections, sensor data, and GIS data. Our involvement in SemaGrow is on ontology alignment, emphasizing on user-related aspects and scalability issues.
A flood of data pertinent to moving objects is available today, and will be more in the near future, particularly due to the automated collection of data from personal devices such as mobile phones and other location-aware devices. Such wealth of data, referenced both in space and time, may enable novel classes of applications of high societal and economic impact, provided that the discovery of consumable and concise knowledge out of these raw data is made possible.