i4Sea: SURVEILLANCE AND ANALYSIS OF MARINE AREAS MOVEMENT USING BIG DATA
The vision of the i4Sea project was to efficiently process, integrate and analyze large-scale marine surveillance data in order to create a combined historical and real-time view of marine surveillance. Towards this goal, the project developed innovative software solutions utilizing cutting-edge research in the field of big mobility data management and analytics.
- P. Tampakis, E. Chondrodima, A. Pikrakis, et al. (2020) Sea area monitoring and analysis of fishing vessels activity: the i4sea big data platform. 21st IEEE International Conference on Mobile Data Management (MDM), pp. 275-280, DOI: 10.1109/MDM48529.2020.00063.
- A. Tritsarolis, G.S. Theodoropoulos, Y. Theodoridis (2021) Online discovery of co-movement patterns in mobility data. International Journal of Geographical Information Science, 35:4, 819-845, DOI: 10.1080/13658816.2020.1834562.
- A. Tritsarolis, C. Doulkeridis, N. Pelekis, Y. Theodoridis (2021) ST_VISIONS: a python library for interactive visualization of spatio-temporal data. 22nd IEEE International Conference on Mobile Data Management (MDM), pp. 244-247, DOI: 10.1109/MDM52706.2021.00048.
- A. Tritsarolis, Y. Kontoulis, N. Pelekis, Y. Theodoridis (2021) MaSEC: discovering anchorages and co-movement patterns on streaming vessel trajectories. 17th International Symposium on Spatial and Temporal Databases (SSTD), pp. 170-173, DOI: 10.1145/3469830.3470909.
- P. Tampakis, E. Chondrodima, A. Tritsarolis, et al. (2021) i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activity. Geo-spatial Information Science, DOI: 10.1080/10095020.2021.1971055
Selected presentations (credits to partner Athena RC):
Selected source code:
- EvolvingClusters: Online Discovery of Group Patterns in Mobility Data
- ST_Visions: A Python-based library for interactive spatio-temporal data visualization
- MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories
The i4Sea project (grant T1EDK-03268) was funded by the European Regional Development Fund of the EU and Greek national funds (through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Research-Create-Innovate).
More details about i4Sea are available in the project’s official web site.
Track & Know – Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
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.
OPTIMA – Computational Intelligence Methods for Big Mobility DataThe main objective of this research project is the development of new Computational Intelligence (CI) methodologies, with emphasis on Artificial Neural Networks (ANNs), Evolutionary computation (EC) and Swarm Intelligence (SI), which will be inherently designed tο address the particular characteristics of big mobility data, with the aim of improving public transport services and as a result to enhance people life quality and to maximize environmental benefits. This research project aims to design “attractive” for passengers, and simultaneously “efficient” and “economical” public transports. In order to accomplish this, it is necessary to study tasks such as: bus capacity / passenger load prediction, bus arrival time prediction, bus timetable optimization (scheduling), bus predictive maintenance, bus Eco-driving behavior, safety and collision risk on intelligent transportation sy
SoundScapes – A Toolkit for the Analysis and Synthesis of Soundscapes
The goal of the SOUNDSCAPES project is to develop a framework and tools for the design and production of sound scenes and related sound effects and ambiences. To that end, the project develops a suite of Acoustic Scene classification, Audio Event Detection and Audio Scene synthesis tools aiming at: (i) providing a framework and solutions for the cost-efficient production of static and living soundscapes, background sound textures and foreground sound effects with the aid of cutting-edge ICT and (ii) overcoming obstacles that prevent individuals from incorporating high-quality sound scenes and ambiences into their products or services. SOUNDSCAPES relies on cutting-edge machine learning research with an emphasis on deep neural networks and reinforcement learning algorithms to develop solutions that deal with the research challenges of the exciting field of automatic scene analysis and production.
SPADES: Spatio-textual Data Exploration at Scale
SPADES aims to address the limitations of spatio-textual data analysis and processing when applied in the context of Big Spatial Data, as witnessed by the lack of existing systems and techniques for this purpose. Achievement of this goal constitutes a substantial step forward in dealing with challenges emerging from management of Big Spatial Data. At a practical level, the research outcome will benefit applications such as spatio-textual search and retrieval, mining of spatio-textual data, next generation location-based services, and tourism-oriented applications to name a few.
CHOROLOGOS: Semantic Spatio-textual Data Analysis and Processing
CHOROLOGOS aims at advancing the state-of-the-art in spatio-temporal-textual query processing, by introducing a novel framework that tightly combines spatio-textual and spatio-temporal querying with semantic retrieval, focusing on expressive query formulation beyond syntactical matching, efficient indexing and query processing, and scalable analysis of massive spatio-textual data.
MODAP – Mobility, Data Mining, and Privacy
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.
CloudIX – Cloud-based Indexing and Query Processing
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.
DATASIM – Data Science for Simulating the Era of Electric Vehicles
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.