Change point detection methods offer a crucial ca-pability in modern data analysis tasks characterized by evolving time series data in the form of data streams. Recent interest in lifelong learning showed the importance of acquiring knowledge and identifying new occurring tasks in a continually evolving environment. Although this setting could benefit from a timely identification of changes, existing change point detection methods are unable to recognize recurring tasks, which is a necessary condition in lifelong learning. In this paper, we attempt to fill this gap by proposing LIFEWATCH, a novel Wasserstein-based change point detection approach with memory capable of modeling multiple data distributions in a fully unsupervised manner. Our method does not only detect changes, but discriminates between changes characterized by the appearance of a new task and changes that rather describe a recurring or previously seen task. An extensive experimental evaluation involving a large number of benchmark datasets shows that LIFEWATCH outperforms state-of-the-art methods for change detection while exploiting the characterization of detected changes to correctly identify tasks occurring in complex scenarios characterized by recurrence in lifelong consolidation settings.

Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings. Change point detection methods have the ability to discover changes in an unsupervised fashion, which represents a desirable property in the analysis of unbounded and unlabeled data streams. However, one limitation of most of the existing approaches is represented by their limited ability to handle multivariate and high-dimensional data, which is frequently observed in modern applications such as traffic flow prediction, human activity recognition, and smart grids monitoring. In this paper, we attempt to fill this gap by proposing WATCH, a novel Wasserstein distance-based change point detection approach that models an initial distribution and monitors its behavior while processing new data points, providing accurate and robust detection of change points in dynamic high-dimensional data. An extensive experimental evaluation involving a large number of benchmark datasets shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.

Most approaches to classifying data streams either divide the stream into fixed-size chunks or use gradual forgetting. Due to evolving nature of data streams, finding a proper size or choosing a forgetting rate without prior knowledge about time-scale of change is not a trivial task. These approaches hence suffer from a trade-off between performance and sensitivity. Existing dynamic sliding window based approaches address this problem by tracking changes in classifier error rate, but are supervised in nature. We propose an efficient semi-supervised framework in this paper which uses change detection on classifier confidence to detect concept drifts, and to determine chunk boundaries dynamically. It also addresses concept evolution problem by detecting outliers having strong cohesion among themselves. Experiment results on benchmark and synthetic data sets show effectiveness of the proposed approach.

To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity of labeled data. In our previous work, we presented a semi-supervised framework SAND, which uses change detection on classifier confidence to detect a concept drift. Unlike most approaches, it requires only a limited amount of labeled data to detect chunk boundaries and to update the classifier. However, SAND is expensive in terms of execution time due to exhaustive invocation of the change detection module. In this paper, we present an efficient framework, which is based on the same principle as SAND, but exploits dynamic programming and executes the change detection module selectively. Moreover, we provide theoretical justification of the confidence calculation, and show effect of a concept drift on subsequent confidence scores. Experiment results show efficiency of the proposed framework in terms of both accuracy and execution time.

A graphical model represents the data distribution of a data generating process and inherently captures its feature relationships. This stochastic model can be used to perform inference, to calculate posterior probabilities, in various applications such as classification. Exact inference algorithms are known to be intractable on large networks due to exponential time and space complexity. Approximate inference algorithms are instead widely used in practice to overcome this constraint, with a trade off in accuracy. Stochastic sampling is one such method where an approximate probability distribution is empirically evaluated using various sampling techniques. However, these algorithms may still suffer from scalability issues on large and complex networks. To address this challenge, we have designed and implemented several MapReduce based distributed versions of a specific type of approximate inference algorithm called Adaptive Importance Sampling (AIS). We compare and evaluate the proposed approaches using benchmark networks. Experimental result shows that our approach achieves significant scaleup and speedup compared to the sequential algorithm, while achieving similar accuracy asymptotically.

Classifying instances in evolving data stream is a challenging task because of its properties, e.g., infinite length, concept drift, and concept evolution. Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in m

Abstract Estimating the number of defects in a software product is an important and challenging problem. A multitude of estimation techniques have been proposed for defect prediction. However, not all techniques are applicable in all cases. The selection of the proper approach to use depends on multiple factors: the features of the approach; the availability of resources; and the goals for using the estimated defect data. In this paper a survey of existing estimation techniques and a decision support approach for selecting the most suitable defect estimation technique for a project, with specific goals, is proposed. The results of the ranking are a clear indication that no estimation technique provides a single, comprehensive solution; the selection must be done according to a given scenario. Copyright © 2009 John Wiley & Sons, Ltd.

The application of the CDM model has proven to be effective and accurate. The piecewise approximation used relies on the proper identification of re-calibration points. These points are identified based on the observation of the testing process. However, not all change points are easily identifiable. In this paper, we present a new technique for the automatic identification of change points which increases the applicability and accuracy of 'the CDM model. In addition, identification of change points can also be used to increase the understanding of the general behavior of a testing process by providing an expected signature for future projects