Abstract
This paper presents an approach to analyse the application of tactics in architectural patterns. We define and illustrate the approach using ARCHERY, a language for specifying, analysing and verifying architectural patterns. The approach consists of characterising the design principles of an architectural pattern as constraints, expressed in the language, and then, establishing a refinement relation based on their satisfaction. The application of tactics preserving refinement ensures that the original design principles, expressed themselves as constraints, still hold in the resulting architectural pattern. The paper focuses on fault-tolerance tactics, and identifies a set of requirements for a semantic framework characterising them. The application of tactics represented as model transformations is then discussed and illustrated using two case studies.

Abstract
Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in the data mining communities to evaluate extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique-statistical tests-to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. © 2012 Wiley Periodicals, Inc.

Abstract
In this paper we propose a framework for defining and discovering optimal association rules involving a numerical attribute A in the consequent. The consequent has the form of interval conditions (A < x, A >= x or A is an element of I where I is an interval or a set of intervals of the form [x(l), x(u))). The optimality is with respect to leverage, one well known association rule interest measure. The generated rules are called Maximal Leverage Rules (MLR) and are generated from Distribution Rules. The principle for finding the MLR is related to the Kolmogorov-Smirnov goodness of fit statistical test. We propose different methods for MLR generation, taking into account leverage optimallity and readability. We theoretically demonstrate the optimality of the main exact methods, and measure the leverage loss of approximate methods. We show empirically that the discovery process is scalable.

Abstract
In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
Existing bidirectional languages are either state-based or operation-based, depending on whether they represent updates as mere states or as sequences of edit operations. In-between both worlds are delta-based frameworks, where updates are represented using alignment relationships between states. In this paper, we formalize delta lenses over inductive types using dependent type theory and develop a point-free delta lens language with an explicit separation of shape and data. In contrast with the already known issue of data alignment, we identify the new problem of shape alignment and solve it by lifting standard recursion patterns such as folds and unfolds to delta lenses that use alignment to infer meaningful shape updates. © Bidirectional Transformations 2012.

Abstract