Resumen de Disseny i modelització d'un sistema de gestió multiresolució de sèries temporals
Nowadays, it is possible to acquire a huge amount of data, mainly due to the fact that it is easy to build monitoring systems together with big sensor networks. However, data has to be managed accordingly, which is not so trivial. Furthermore. The storage for all this data also has to be considered. On the one hand, time series is the formalisation for the process of acquiring values from a variable along time. There is a great deal of algorithms and methodologies for analysing time series which describe how information can be extracted from data. On the other hand, Data Base Management Systems (DBMS) are the formalisation for the systems that store and manage data. That is, these computer systems are devoted to infer the information that a given user may query. These systems are formally defined by logic models from which the relational model is the main reference. This thesis is a dissertation on the hypothesis to store only parts of original data which contain selected information. This information selection involves summarising data with different resolutions, mainly by aggregating data at periodic time intervals. We name multiresolution to this technique. Multiresolution is operated on time series. The results are time subseries that have bounded size and summaries of information. Particular DBMS are used for managing time series, then they are called Time Series Management Systems (TSMS). In this context, we define TSMS with multiresolution capabilities (MTSMS). Similarly to how it is done for DBMS, we formalise a model for TSMS and for MTSMS. The acquisition of time series presents troublesome properties owing to the fact of variable acquired along time. In MTSMS we consider some of this properties such as: the clock synchronisation for different acquisition systems, unknown data when data has not been acquired or when it is erroneous, a huge amount of data to be manage. Moreover it increases as more data gets acquired, or queries with data that has not been acquired regularly along time. MTSMS are defined as systems to store data by selecting information and so by discarding data that is not considered important. Therefore, the parameters for selecting information must be decided previously to storing data. The information theory is the base for measuring the quality of theses systems, which depends on the parameters chosen. Regarding this, multiresolution can be considered as a lossy compression technique. We introduce some hypothesis on measuring the error caused by multiresolution in comparison with the case of having all the original data. Paraphrasing a current opinion in DBMS, the same system can not be adequate for all the different contexts. In addition, systems must consider performance in a variety of resources apart from computing time, such as energy consumption, storage capacity or network transmission. Concerning this, we design different implementations for the model of MTSMS. These implementations experiment with various computing methodologies: incremental computing along the data stream, parallel computing and relational databases computing. Summarising, in this thesis we formalise a model for MTSMS. MTSMS are useful for storing time series in bounded capacity systems and in order to precompute the multiresolution. In this way they can achieve immediate queries and graphical visualisations for summarised time series. However, they imply an information selection that has to be considered previously to the storage. In this thesis we consider the limits for the multiresolution technique.
