This paper applies the Behavior Pattern Learning (BPL) approach to automatically discover the conditions previous to specific macroeconomic events from the historical evolution of macroeconomic variables, as well as historical events. Every time the target macroeconomic event (e.g. Consumer Price Index (CPI) inflation greater than 5.5%) takes place, BPL constructs several snapshots at specific previous times. Each snapshot, called a behavior summary, includes information about its present, past, and future. The set of behavior summaries is used by a regression tree approach to discover the conditions for the target event to take place. In this paper, we used BPL to automatically discover the conditions for the occurrence of specific events associated with the CPI inflation and Gross domestic product (GDP) deflactor using macroeconomic data, and war and political events. Comparisons with other prediction models on the same problem show that the BPL provides automatic forecasts that are similar in performance as those forecasts done by models assisted by experts.

A new algorithm for incremental construction of binary regression trees is presented. This algorithm, called SAIRT, adapts the induced model when facing data streams involving unknown dynamics, like gradual and abrupt function drift, changes in certain regions of the function, noise, and virtual drift. It also handles both symbolic and numeric attributes. The proposed algorithm can automatically adapt its internal parameters and model structure to obtain new patterns, depending on the current dynamics of the data stream. SAIRT can monitor the usefulness of nodes and can forget examples from selected regions, storing the remaining ones in local windows associated to the leaves of the tree. On these conditions, current regression methods need a careful configuration depending on the dynamics of the problem. Experimentation suggests that the proposed algorithm obtains better results than current algorithms when dealing with data streams that involve changes with different speeds, noise levels, sampling distribution of examples, and partial or complete changes of the underlying function.

In order to discover behavior patterns, current algorithms only analyze historical data in terms of performance data or fault events, ignoring the temporal correlation among different types of information, including the configuration changes. A method is presented that can discover recurrent patterns from multiple flows of events, such as alarms and configuration events, as well as discrete information, such as traffic and usage, taking into account static and dynamic information concerning observed objects and their environments. This method can filter out theoretically useless patterns, using a novel technique for detecting chaos in sequences of events. The prediction accuracy of the discovered patterns has been measured using objects with dynamic behavior controlled by known and complex differential equations. The proposed mining method has been used for discovering and predicting alarms in a computer network composed of several Internet servers taking into account the alarm and configuration events history, as well as static information about these servers.

The ever-increasing complexity of the elements that constitute a telecommunications network as well as the amount of knowledge and experience needed to operate and maintain these elements, demand maintenance tools that are both powerful enough to perform these tasks and easy to learn and modify. Thus, the use of a knowledge-based expert systems (KBES) methodology is required. This paper presents UFMS12 and UFMAXE, two real-time expert systems for performing automatic corrective maintenance of large digital exchanges: Ericsson's AXE and Alcatel's S12. These expert systems can repair automatically a fault dialoguing with the affected exchange or they can guide the operator in manual interventions. These developments produced a shell, called SIGMA (Sistema Generico para Mantenimiento), based on a causal model. This paper presents the functionality of UFMAXE and UFMS12, and a description of the architecture of the SIGMA shell, designed for representing the maintenance knowledge of large and complex digital systems.>