Urban Archaeology in Madrid. Experiences and Opportunities to Understand Historical City

In 2015, it will be thirty years since Spanish Historic Heritage Law from 1985 was approved. The results after three decades under this law are necessarily positive and witness how the complex autonomous regional legislation has been promoted, guided and organized in this Heritage field. In addition, the law enforcement has brought into the scene how the numerous public and private initiatives involved in caring, managing, protecting and restoring our cultural heritage have been channeled and regulated, as well as monitoring the impact these initiatives produce on urban archaeology. During this long period of Spanish recent history, cultural heritage -understood as an important development tool, especially when related to cultural tourism- has succeeded in channeling resources for developing the historical research projects, both documental and archaeological, that the Spanish monumental urban ensembles were requiring. In this context, the case of the city of Madrid is a clear example of the significant development that urban Historical Archaeology has experienced in Spain over the last thirty years, especially when dealing with the study of the Middle Ages (8th to 15th centuries) and the Modern Age (16th to 18th centuries). Given the number of interventions and the important results obtained by many of them, Madrid urban archaeology is an extraordinary example of the consequences of implementing new management models, changing criteria and operating procedures, and also, of course, of the conflicts and debates raised regarding heritage, as well as the importance these interventions have implied, which is the main aim of this work.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.