Adapting autonomously classification data mining algorithms for ubiquitous devices

• Autores: Andrea Zanda
• Directores de la Tesis: Ernestina Menasalvas (dir. tes.), Santiago Eibe (dir. tes.)
• Lectura: En la Universidad Politécnica de Madrid ( España ) en 2012
• Idioma: español
• Tribunal Calificador de la Tesis: Covadonga Fernandez (presid.), José María Peña Sánchez (secret.), María Amparo Vila Miranda (voc.), Cristóbal Romero Morales (voc.), Yucel Saygin (voc.), Sebastián Ventura Soto (voc.)
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Bancos de datos
• Enlaces
  • Tesis en acceso abierto en: Archivo Digital UPM
• Resumen

  • Abstract Due to recent scientific and technological advances in information sys¬tems, it is now possible to perform almost every application on a mobile device. The need to make sense of such devices more intelligent opens an opportunity to design data mining algorithm that are able to autonomous execute in local devices to provide the device with knowledge. The problem behind autonomous mining deals with the proper configuration of the algorithm to produce the most appropriate results. Contextual information together with resource information of the device have a strong impact on both the feasibility of a particu¬lar execution and on the production of the proper patterns. On the other hand, performance of the algorithm expressed in terms of efficacy and efficiency highly depends on the features of the dataset to be analyzed together with values of the parameters of a particular implementation of an algorithm. However, few existing approaches deal with autonomous configuration of data mining algorithms and in any case they do not deal with contextual or resources information. Both issues are of particular significance, in particular for social net¬works application. In fact, the widespread use of social networks and consequently the amount of information shared have made the need of modeling context in social application a priority. Also the resource consumption has a crucial role in such platforms as the users are using social networks mainly on their mobile devices. This PhD thesis addresses the aforementioned open issues, focusing on i) Analyzing the behavior of algorithms, ii) mapping contextual and resources information to find the most appropriate configuration iii) applying the model for the case of a social recommender. Four main contributions are presented: - The EE-Model: is able to predict the behavior of a data mining algorithm in terms of resource consumed and accuracy of the mining model it will obtain. - The SC-Mapper: maps a situation defined by the context and resource state to a data mining configuration. - SOMAR: is a social activity (event and informal ongoings) recommender for mobile devices. - D-SOMAR: is an evolution of SOMAR which incorporates the configurator in order to provide updated recommendations. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation.