As a popular technique in cloud computing, multi-tenancy (MT) can significantly ease software maintenance, and improve resource utilization. To make use of the MT technique, an application may need to be transformed to be MT-enabled. This process involves finding and processing a special kind of data entities named global isolation points (GIPs). Practically, finding all GIPs of an application is challenging. Traditional method involves manually browsing the application code, requiring a great deal of human effort. To solve this problem, we introduce a toolkit named Auto-MT to help find and process GIPs of an application. Auto-MT is able to find new GIPs based on their relations to known GIPs. To characterize the relation, a novel graph called value flow graph (VFG) is introduced, which models the value flows of data entities. It can also be used in other scenarios, like taint analysis. We have implemented Auto-MT as an Eclipse Plug-in, and applied it to transform Roller, a widely used Java application. Experimental results show that Auto-MT saves substantial human effort, and accelerates the process of transforming applications to be MT-enabled.

Detecting anomalous events from spatial data has important applications in real world. The spatial scan statistic methods are popular in this area. With maximizing the spatial statistical discrepancy by comparing observed data with a given baseline data distribution, significant spatial overdensity and underdensity can be detected. In reality, the spatial discrepancy is often irregularly shaped and has a structure of multiple spatial scales. However, a large-scale discrepancy pattern may not be significant when conducting fine granularity analysis. Meanwhile, local irregular boundaries of a maximized discrepancy cannot be well approximated with a coarse granularity analysis. Existing methods mostly work either on a fixed granularity, or with a regularly shaped scanning window. Thus, they have difficulties in characterizing such flexible spatial discrepancies. To solve the problem, in this paper we propose a novel discrepancy maximization algorithm, RefineScan. A grid hierarchy encoding multi-scale information is employed, making the algorithm capable of maximizing spatial discrepancies with multi-scale structures and irregular shapes. Experiments on a wide range of datasets demonstrate the advantages of RefineScan over the state-of-the-art algorithms: It always finds the largest discrepancy scores and remarkably better characterizes multi-scale discrepancy boundaries. Theoretical and empirical analyses also show that RefineScan has a moderate computational complexity and a good scalability.

As an important and fundamental methodology in the fields of pattern recognition and image processing, learning middle level feature has attracted increasing interest during the recent years, where generative feature mapping has shown highly completive performance in diverse applications. In this paper, a middle level feature representation is proposed based on Deep Boltzmann Machine (DBM) and sufficient statistics (SS) feature mapping for detection. In the approach, DBM is employed to model data distribution and the hidden information inferred by DBM together with other informative variables are then exploited by SS to form the middle level features. The features, learnt from data, can be fed to standard classifiers for classification. In order to evaluate the performance of our method, we apply our feature mapping method to two challenging tasks: (1) contour detection through distinguishing border and non-border pixels, (2) sales pipeline prediction, which predicts the winning propensity of the ongoing sales opportunity in the pipeline. In comparison with other leading methods in the literature on the Berkeley Segmentation Dataset and Sales Pipeline Database (SPDB), our proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness and efficiency.

Pipe breaks in urban water distribution network lead to significant economical and social costs, putting the service quality as well as the profit of water utilities at risk. To cope with such a situation, scheduled preventive maintenance is desired, which aims to predict and fix potential break pipes proactively. Physical models developed for understanding and predicting the failure of pipes are usually expensive, thus can only be used on a limited number of trunk pipes. As an alternative, statistical models that try to predict pipe breaks based on historical data are far less expensive, and therefore have attracted a lot of interests from water utilities recently. In this paper, we report a novel data mining prediction system that has been built for a water utility in a big Chinese city. Various aspects of how to build such a system are described, including problem formulation, data cleaning, model construction, as well as evaluating the importance of attributes according to the requirements of end users in water utilities. Satisfactory results have been achieved by our prediction system. For example, with the system trained on the available dataset at the end of 2010, the water utility would avoid 50% of pipe breaks in 2011 by examining only 6.98% of its pipes in advance. During the construction of the system, we find that the extremely skew distribution of break and non-break pipes, interestingly, is not an obstacle. This lesson could serve as a practical reference for both academical studies on imbalanced learning as well as future explorations on pipe failure prediction problems.

This paper proposes a novel gender recognition method based on the head-shoulder part of human body. The head-shoulder area contains much information that could be cues to infer the gender of a person, such as hair-style, face, neckline style and so on. A rich high-dimensional feature descriptor is designed to extract gradient, texture and orientation information from the head-shoulder area, then Partial Least Squares (PLS) is employed to learn a very low dimensional discriminative subspace. Features are projected into the low dimensional subspace and linear SVM is employed to learn an efficient classification model between the male and female categories. Experimental results on a large real-world dataset demonstrate the effectiveness of the proposed method.