Past Projects


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.

Selected publications:

  • 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):

  • i4Sea forecasting demo (in Greek)
  • i4Sea monitoring demo

Selected source code:


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.

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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.


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.


EICOS – Foundations of Personalized Cooperative Information Ecosystems

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.

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GeoPKDD – Geographic Privacy-aware Knowledge Discovery and Delivery

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.

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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.


SEEK – SEmantic Enrichment of trajectory Knowledge discovery

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.