Abstract

Abstract
In this work we present XmuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. xMuS er's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequence miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems.

Abstract
This work presents an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. XMuSer framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the most interesting sequential patterns into a new table, the sequence relation. In the last step of our framework, we use an ILP algorithm to learn a classification theory on the enlarged relational database that consists of the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems and map each one of three different types of sequential patterns: frequent, closed or maximal. The experiments show that our ILP based framework gains both from the descriptive power of the ILP algorithms and the efficiency of the sequential miners. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHRED framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp events are HMM states and discretized wind speed data are HMM observations. The discretization of the historical data is obtained by running the SAX algorithm over the first order variations in the original signal. SHRED updates the HMM using the most recent historical data and includes a forgetting mechanism to model natural time dependence in wind patterns. To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur. We compare SHRED framework against Persistence baseline in predicting ramp events occurring in short-time horizons, ranging from 30 minutes to 90 minutes. SHRED consistently exhibits more accurate and cost-effective results than the baseline. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
The exponential growth of mobile devices, like smart phones and tablets, has led to a growing ubiquitous computing paradigm, in which computing is distributed and available anytime, anywhere and supported by different devices. The document and workflow management in organizations is made through computers connected to one or several servers via a networking infrastructure. The emergence of ubiquitous computing paradigm leads those solutions to be adapted for mobile platforms. Thus, users tasks can be done in a more efficient way due to the availability of information wherever they are using a mobile device, improving both the time needed to complete tasks and organizations' efficiency. In this paper we present the mobile version of edoclink document and workflow management solution. Edoclink system was developed by Link Consulting c and is widely implemented in several healthcare institutions. (C) 2012 Published by Elsevier Ltd. Selection and/or peer review under responsibility of CENTERIS/SCIKA - Association for Promotion and Dissemination of Scientific Knowledge

Abstract
The Artificial Immune Systems (AIS) constitute an emerging and very promising area of research that historically have been falling within two main theoretical immunological schools of thought: those based on Negative selection (NS) or those inspired on Danger theory (DT). Despite their inherent strengths and well known promising results, both deployed AIS have documented difficulties on dealing with gradual dynamic changes of self behavior through time. In this paper we propose and describe the development of an AIS framework for anomaly detection based on a rather different immunological theory, which is the Grossman's Tunable Activation Thresholds (TAT) theory for the behaviour of T-cells. The overall framework has been tested with artificially generated stochastic data sets based on a real world phenomena and the results thus obtained have been compared with a non-evolutionary Support Vector Machine (SVM) classifier, thus demonstrating TAT's performance and competitiveness for anomaly detection. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.