Big mobility data management techniques and analytics

Transcription

1 Big mobility data management techniques and analytics Assoc Prof Dimitrios Zissis!,#! Dept. of Product& Systems Design Engineering, University of the Aegean # MarineTraffic

2 Outline MarineTraffic & AIS Challenges Research efforts

3 Automatic Identification System (AIS) The AIS is a collaborative, self-reporting system that allows marine vessels to broadcast their information to nearby vessels and on-ground base stations. It uses digital radio signals to exchange real time information between vessels and shore based stations on dedicated VHF frequencies. o Collision detection o Although mandatory for large commercial vessels to carry device, it is not mandatory to use it. o Not a replacement of radar as it cannot detect land masses, navigation beacons and vessels not equipped with AIS

4 Big Data Management Volume o o 25GB per day (synopsis!) 50 bytes per message Velocity o o o 1 message per minute per vessel >0.5 Billion messages per day >150,000 streams Variety o Increasing data sources leads to complex processing, management and preservation of data

5 Research at MarineTraffic MarineTraffic Research is an example of an industrial research lab where research outcomes affect millions of users in no time. Researchers are focused on discovering novel and innovative ways of making shipping safer, greener and more efficient through smarter technologies. Build systems and algorithms which can exploit uncertainty and partial truth to achieve robustness and high performance in real world conditions. expertise are within the areas of artificial intelligence, distributed and parallel computing, decision support systems, soft computing, evolutionary computing, data management.

6 Recent Collaborative Research Projects BigDataOcean - Exploiting Ocean's of Data for Maritime Applications (H2020 ICT-14-Big Data) SELIS towards a Shared European Logistics Intelligent Information Space (H2020-MG- 2015) Master Plan for OPS in Spanish Ports Evaluation Study of Ship Electric Supply Implementation in the Spanish Port System (CEF-Transport-2015) MoS 24-ICT based Co-modality Promotion Center for integrating PP24 into Mediterranean Motorways Of the Sea (Co-financed by EU TEN-T) PORTOPIA aims at creating an integrated knowledge base and management system of port performance to serve the industry s stakeholders in improving the sustainability and competitiveness of the European Port System (FP7) MARPOCS aims to develop and implement an integrated operational framework for preparedness and response to oil and HNS spills in marine waters (DG-ECHO-2015)

7 BigDataOcean at a glance Biological Data Open, Social Data Open data Sensor/IoT data Secondary Data Historic Data Scientific Data To enable maritime big data scenarios for EU-based companies, organisations and scientists, through a multisegment platform that will combine data of different velocity, variety and volume under an inter-linked, trusted, multilingual engine to produce a big-data repository of value and veracity back to the participants and local communities.

8 Enable 4 Pilot Cases Fault Prediction & Proactive Maintenance Mare Protection 1 2 Enable Wave Power as Clean Energy Source 3 4 Maritime Security & Anomaly Detection

9 Define port-to-port trade routes we use a dataset of approximately 5 billion messages (i.e., 525 GB) recorded from January to December 2016

10 Define port-to-port trade routes Routes extracted for cargo vessels for 2016

11 Define port boundaries and operation regions

12 Scalable and distributed sea port operational areas estimation from AIS data processing 3.4 million AIS messages adaptation of the well-known KDE algorithm to the map-reduce paradigm

13 Mining Vessel Trajectory Data for Patterns of Search and Rescue The overall aim of this work is to explore the possibility of automatically detecting Search And Rescue (SAR) activity, even when a distress call has on yet been received. no reliance on external sources of information (e.g. coast guard reports). a parallelised, nonparametric statistical method (Random Forests), which has proved capable of achieving prediction accuracy rates higher than 77%.

14 For more information 1. G Spiliopoulos, K. Chatzikokolakis, D. Zissis, E. Biliri, D. Papaspyros, G Tsapelas, S. Mouzakitis, Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data, IEEE International Conference on Big Data (IEEE Big Data 2017), December 11-14, 2017, Boston, MA, USA. 2. G Spiliopoulos, D Zissis, K Chatzikokolakis, A Big Data Driven Approach to Extracting Global Trade Patterns, Mobility Analytics for Spatio-temporal and Social Data with VLDB A Valsamis, K Tserpes, D Zissis, D Anagnostopoulos, T Varvarigou, Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction, Journal of Systems and Software, Elsevier, 127, , L. M. Millefiori, D. Zissis, L. Cazzanti, G. Arcieri, Scalable and Distributed Sea Port Operational Areas Estimation from AIS data, IEEE International Conference on Data Mining (ICDM 2016), Dec Barcelona, Spain. 5. L. M. Millefiori, D. Zissis, L. Cazzanti and G. Arcieri, "A distributed approach to estimating sea port operational regions from lots of AIS data" IEEE International Conference on Big Data 2016 (IEEE Big Data 2016), Washington, DC, 2016, pp

15 Thank you! Questions?