Abstract
We present a framework designed to mine sequential temporal patterns from multi-relational databases. In order to exploit logic-relational information without using aggregation methodologies, we convert the multi-relational dataset into what we name a multi-sequence database. Each example in a multi-relational target table is coded into a sequence that combines intra-table and inter-table relational temporal information. This allows us to find heterogeneous temporal patterns through standard sequence miners. Our framework is grounded in the excellent results achieved by previous propositionalization strategies. We follow a pipelined approach, where we first use a sequence miner to find frequent sequences in the multi-sequence database. Next, we select the most interesting findings to augment the representational space of the examples. The most interesting sequence patterns are discriminative and class correlated. In the final step we build a classifier model by taking an enlarged target table as input to a classifier algorithm. We evaluate the performance of this work through a motivating application, the hepatitis multi-relational dataset. We prove the effectiveness of our methodology by addressing two problems of the hepatitis dataset.

Abstract
The study of evolution has become an important research issue; especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a elms of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory; and a transition detection algorithm. To demonstrate its feasibility and applicability we present; real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions.

Abstract
Sensor data is usually represented by streaming time series. Current state-of-the-art systems for visualization include line plots and three-dimensional representations, which most of the time require screen resolutions that are not available in small transient mobile devices. Moreover, when data presents cyclic behaviors, such as in the electricity domain, predictive models may tend to give higher errors in certain recurrent points of time, but the human-eye is not trained to notice this cycles in a long stream. In these contexts, information is usually hard to extract from visualization. New visualization techniques may help to detect recurrent faulty predictions. En this paper we inspect visualization techniques in the scope of a real-world sensor network, quickly dwelling into future trends in visualization in transient mobile devices. We propose a simple dense pixel display visualization system, exploiting the benefits that it may represent on detecting and correcting recurrent faulty predictions. A case study is also presented, where a simple corrective strategy is studied in the context of global electrical load demand, exemplifying the utility of the new visualization method when compared with automatic detection of recurrent errors.

Abstract

Abstract

Abstract