Due to the increasing amount of information present on the Web, Automatic Document Classification (ADC) has become an important research topic. ADC usually follows a standard supervised learning strategy, where we first build a model using pre-classified documents and then use it to classify new unseen documents. One major challenge for ADC in many scenarios is that the characteristics of the documents and the classes to which they belong may change over time. However, most of the current techniques for ADC are applied without taking into account the temporal evolution of the collection of documents.In this work, we perform a detailed study of the temporal evolution in the ADC, introducing an analysis methodology. We discuss that temporal evolution may be explained by three factors: 1) class distribution; 2) term distribution; and 3) class similarity. We employ metrics and experimental strategies capable of isolating each of these factors in order to analyze them separately, using two very different document collections: the ACM Digital Library and the Medline medical collections. Moreover, we present some preliminary results of potential gains that could be obtained by varying the training set to find the ideal size that minimizes the time effects. We show that by using just 69% of the ACM database, we are able to have an accuracy of 89.76%, and with only 25% of the Medline, an accuracy of 87.57%, which means gains of up to 20% in accuracy with much smaller training sets.

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In software development, migration from a Data Base Management System (DBMS) to another, especially with distinct characteristics, is a challenge for programmers and database administrators. Changes in the application code in order to comply with new DBMS are usually vast, causing migrations infeasible. In order to tackle this problem, we present NoSQLayer, a framework capable to support conveniently migrating from relational (i.e., MySQL) to NoSQL DBMS (i.e., MongoDB). This framework is presented in two parts: (1) migration module; and, (2) mapping module. The first one is a set of methods enabling seamless migration between DBMSs (i.e. MySQL to MongoDB). The latter provides a persistence layer to process database requests, being capable to translate and execute these requests in any DBMS, returning the data in a suitable format as well. Experiments show NoSQLayer as a handful solution suitable to handle large volume of data (e.g., Web scale) in which traditional relational DBMS might be inept in the duty.

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