SEEK · Semantic EnrichmEnt of trajectory Knowledge discovery
FP7 — People (Marie Curie Actions)
- Duration
- 2012-03-01 → 2015-08-31
- EU contribution
- €352,800
- Participants
- 3
- Scheme
- MC-IRSES
Lines connect the coordinator with its partners.
Results in brief
Semantic EnrichmEnt of trajectory Knowledge discovery
The main objective of the SEEK project is to envisage a new semantic enriched knowledge discovery process. In contrast to the classical knowledge discovery process for relational data, SEEK tries to formulate a new vision where the semantic aspect (in the sense of the meaning of the movement) is embedded in each step. The challenge is to propose a new process that, taking advantage from the semantic information during the discovery process, may produce exploitable results in several application domains. This can be done at several levels. The basic step in the semantic enriched knowledge discovery process is the Semantic Trajectory representation, storage and OLAP analysis - objective of Working Package 2. This task is about the investigation of semantic representations of trajectories and techniques for cleaning, transforming, enriching, storing and analysing movement data by using a specialised Data Warehouse. Our goal has been to tailor methods for the newly defined semantic trajectory warehouse, often done exploiting ontologies. Another step in the discovery process is the “Semantic Knowledge Discovery” - Working Package 3: the objective here has been to investigate knowledge discovery methods that take into account the semantics at several levels: (1) studying methods that use the contextual information to mine different properties of a trajectory (2) proposing post-processing and visualization methods to give a context-dependent meaning to the extracted patterns. A fourth Working Package is called “New challenges: social aspects of the movement”. Here we traces some ideas for future directions that can benefit from the semantically enriched trajectories, like the use of Linked Open Data for the enrichment, the use of complex networks to represent semantically enriched movement data or the analysis of social networks to predict traffic related problems. The project produced a total of 40 published or accepted papers, while several other works are ongoing or waiting notification. These papers are published at international high level peer review journals and conferences. Among them, we count three “best papers” in international conferences. The list of publications is available and updated at the project web site: http://www.seek-project.eu. Among many other activities, the project also organised seven workshops and created the opportunity for two PhD students co-tutela between project partners
Data: CORDIS, © European Union
Project objective
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.The fundamental hypothesis is that it is possible, in principle, to aid citizens in their mobile activities by analysing the traces of their past activities by means of data mining techniques. For instance, behavioural patterns derived from mobile trajectories may allow inducing traffic flow information, capable to help people travelling efficiently, to help public administrations in traffic-related decision making for sustainable mobility and security management.Behavioral patterns can be extracted through a knowledge discovery process where positioning data collected from mobile devices are first transformed in semantically enriched trajectory data stored in a database. Then, these data are loaded in a data warehouse and analysed with OLAP operations that allow summarization of the trajectories features. Mobility patterns, the most common movements emerging from data, are computed with suitable spatio-temporal data mining algorithms. A further semantic enrichment step is needed to give context-dependent meaning to the discovered patterns.The goal of the project is to investigate methods to extract meaningful knowledge from large amount of movement data by defining techniques for an advanced semantic-rich knowledge discovery process.
Original text from CORDIS.
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Data: CORDIS, © European Union