Sami Faïz

Clustering Algorithm for Network Constraint Trajectories

Spatial data mining is an active topic in spatial databases. This paper proposes a new clustering method for moving object trajectories databases. It applies specifically to trajectories that only lie on a predefined network. The proposed algorithm (NETSCAN) is inspired from the well-known density based algorithms. However, it takes advantage of the network constraint to estimate the object density. Indeed, NETSCAN first computes dense paths in the network based on the moving object count, then, it clusters the sub-trajectories which are similar to the dense paths. The user can adjust the clustering result by setting a density threshold for the dense paths, and a similarity threshold within the clusters. This paper describes the proposed method. An implementation is reported, along with experimental results that show the effectiveness of our approach and the flexibility allowed by the user parameters.

Characterizing Traffic Density and Its Evolution through Moving Object Trajectories

Managing and mining data derived from moving objects have become an important issue in recent years. In this paper, we are interested in mining trajectories of moving objects, such as vehicles in the road network. We propose a method for discovering dense routes by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatiotemporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph connecting dense routes over consecutive time periods. This graph is labeled by a degree of evolution. We have implemented and tested the proposed algorithms, which have shown their effectiveness and efficiency.

Exploration techniques of the spatial data warehouses: overview and application to incendiary domain

Data warehouses have appeared in answer to the new requirements of database technologies. With them appeared some automated techniques like knowledge discovery in databases (KDD), data mining (DM) and online analytical processing (OLAP), which have registered a great demand in relational, transactional and finally in geographical databases. We give a survey of the state of the art of this research field. We mention the principles of data warehouses defining the context of geographical information, and present models used to build the data warehouse. Then, the tools permitting exploration of data warehouses and considered as tools for decision making are introduced. An evaluation of the technique of generalization is carried out using incendiary data. On the other hand, we expose a suggestion to manage incendiary data by cubic representation.

Text thematic fragmentation and identification

Given the exponential growth of online information, one of the primary difficulties facing users is the information overload. Huge amounts of these electronic information are mainly encapsulated in text documents. So, the researchers are striving to develop robust methods to provide users and managers with prominent solutions needed for text analysis. This allows maximizing profits by saving time and money devoted to manage the increasing amount of information. In this context and among others we find text segmentation and theme identification. In this paper and by resting on the existing well known approaches and advances in the text segmentation and theme identification fields, we propose new solutions to reach the objectives behind achieving these two techniques more accurately.

A Bridge between Document Management System and Geographical Information System

The power of geographic information system is to help managers make critical decisions they face daily. The ability to make sound decisions relies upon the availability of relevant information. Typically, spatial databases do not contain much information that could support the decision making process in all situations. To extend the available dataset, we propose an approach for the enrichment of geographical databases (GDB) and especially their semantic component. This enrichment is performed by providing knowledge extracted from web documents to supplement the aspatial data of the GDB. The knowledge extraction process is reached through the generation of condensed representation of the relevant information derived from a web corpus. This process is carried out in a distributed fashion that complies with the multi-agents paradigm.

Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d'objets mobiles

Managing and mining data derived from moving objects is becoming an important issue in the last years. In this paper, we are interested in mining trajectories of moving objects such as vehicles in the road network. We propose a method for dense route discovery by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatiotemporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph of route connection over consecutive time periods. This graph is labelled by a degree of evolution. We have implemented and tested the proposed algorithms, which have shown their effectiveness and efficiency.