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Identifying worst-case user scenarios for performance testing of web applications using Markov-chain workload models

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 87卷 910-920页

作者： Ahmad, Tanwir Truscan, Dragos Porres, Ivan Abo Akad Univ Fac Sci & Engn Vattenborgsvagen 5 Turku 20500 Finland

The poor performance of web-based systems can negatively impact the profitability and reputation of the companies that rely on them. Finding those user scenarios which can significantly degrade the performance of a web application is very important in order to take necessary countermeasures, for instance, allocating additional resources. Furthermore, one would like to understand how the system under test performs under increased workload triggered by the worst-case user scenarios. In our previous work, we have formalized the expected behavior of the users of web applications by using probabilistic workload models and we have shown how to use such models to generate load against the system under test. As an extension, in this article, we suggest a performance space exploration approach for inferring the worst-case user scenario in a given workload model which has the potential to create the highest resource utilization on the system under test with respect to a given resource. We propose two alternative methods: one which identifies the exact worst-case user scenario of the given workload model, but it does not scale up for models with a large number of loops, and one which provides an approximate solution which, in turn, is more suitable for models with a large number of loops. We conduct several experiments to show that the identified user scenarios do provide in practice an increased resource utilization on the system under test when compared to the original models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Performance testing Markov chain Genetic algorithms graph-search algorithms

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Synergistic arc-weight estimation for interactive image segmentation using graphs

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COMPUTER VISION AND IMAGE UNDERSTANDING 2010年第1期114卷 85-99页

作者： de Miranda, P. A. V. Falcao, A. X. Udupa, J. K. Univ Estadual Campinas LIV Inst Comp BR-13084851 Campinas SP Brazil MIPG Philadelphia PA 19104 USA

We introduce a framework for synergistic arc-weight estimation, where the user draws markers inside each object (including background), arc weights are estimated from image attributes and object information (pixels under the markers), and a visual feedback guides the user's next action. We demonstrate the method in several graph-based segmentation approaches as a basic step (which should be followed by some proper approach-specific adaptive procedure) and show its advantage over methods that do not exploit object information and over methods that recompute weights during delineation, which make the user to lose control over the segmentation process. We also validate the method using medical data from two imaging modalities (CT and MRI-T1). (C) 2009 Elsevier Inc. All rights reserved.

关键词： Image foresting transform graph-cut segmentation Relative-fuzzy connectedness Watershed transform Live-wire segmentation Contour tracking Interactive segmentation graph-search algorithms kappa-Connected segmentation

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Interactive volume segmentation with differential image foresting transforms

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2004年第9期23卷 1100-1108页

作者： Falcao, AX Bergo, FPG Univ Estadual Campinas Inst Comp BR-13084851 Campinas SP Brazil

The absence of object information very often asks for considerable human assistance in medical image segmentation. Many interactive two-dimensional and three-dimensional (3-D) segmentation methods have been proposed, but their response time to user's actions should be considerably reduced to make them viable from the practical point of view. We circumvent this problem in the framework of the image foresting transform (IFT)-a general tool for the design of image operators based on connectivity-by introducing a new algorithm (DIFT) to compute sequences of IFTs in a differential way. We instantiate the DIFT algorithm for watershed-based and fuzzy-connected segmentations under two paradigms (single-object and multiple-object) and evaluate the efficiency gains of both approaches with respect to their linear-time implementation based on the nondifferential IFT. We show that the DIFT algorithm provides efficiency gains from 10 to 17, reducing the user's waiting time for segmentation with 3-D visualization on a common PC from 19-36 s to 2-3 s. We also show that the multiple-object approach is more efficient than the single-object paradigm for both segmentation methods.

关键词： Dijkstra's algorithm fuzzy connectedness graph-search algorithms image analysis image foresting transform medical imaging minimum-cost path forest 3-D visualization user-assisted image segmentation watershed transform

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Path-Value Functions for Which Dijkstra's Algorithm Returns Optimal Mapping

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2018年第7期60卷 1025-1036页

作者： Ciesielski, Krzysztof Chris Falcao, Alexandre Xavier Miranda, Paulo A. V. West Virginia Univ Dept Math Morgantown WV 26506 USA Univ Penn Dept Radiol MIPG Philadelphia PA 19104 USA Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil Univ Sao Paulo Inst Math & Stat BR-05508090 Sao Paulo SP Brazil

Dijkstra's algorithm (DA) is one of the most useful and efficient graph-search algorithms, which can be modified to solve many different problems. It is usually presented as a tool for finding a mapping which, for every vertex v, returns a shortest-length path to v from a fixed single source vertex. However, it is well known that DA returns also a correct optimal mapping when multiple sources are considered and for path-value functions more general than the standard path-length. The use of DA in such general setting can reduce many image processing operations to the computation of an optimum-path forest with path-cost function defined in terms of local image attributes. In this paper, we describe the general properties of a path-value function defined on an arbitrary finite graph which, provably, ensure that Dijkstra's algorithm indeed returns an optimal mapping. We also provide the examples showing that the properties presented in a 2004 TPAMI paper on the image foresting transform, which were supposed to imply proper behavior of DA, are actually insufficient. Finally, we describe the properties of the path-value function of a graph that are provably necessary for the algorithm to return an optimal mapping.

关键词： Dijkstra's algorithm graph-search algorithms Image foresting transform Connectivity functions

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Automatic image segmentation by tree pruning

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2007年第2-3期29卷 141-162页

作者： Bergo, Felipe P. G. Falcao, Alexandre X. Miranda, Paulo A. V. Rocha, Leonardo M. Univ Estadual Campinas LIV Inst Comp BR-13083970 Campinas SP Brazil Univ Estadual Campinas DECOM FEEC BR-13083970 Campinas SP Brazil

The Image Foresting Transform (IFT) is a tool for the design of image processing operators based on connectivity, which reduces image processing problems into an optimum-path forest problem in a graph derived from the image. A new image operator is presented, which solves segmentation by pruning trees of the forest. An IFT is applied to create an optimum-path forest whose roots are seed pixels, selected inside a desired object. In this forest, object and background are connected by optimum paths (leaking paths), which cross the object's boundary through its "most weakly connected" parts (leaking pixels). These leaking pixels are automatically identified and their subtrees are eliminated, such that the remaining forest defines the object. Tree pruning runs in linear time, is extensible to multidimensional images, is free of ad hoc parameters, and requires only internal seeds, with little interference from the heterogeneity of the background. These aspects favor solutions for automatic segmentation. We present a formal definition of the obtained objects, algorithms, sufficient conditions for tree pruning, and two applications involving automatic segmentation: 3D MR-image segmentation of the human brain and image segmentation of license plates. Given that its most competitive approach is the watershed transform by markers, we also include a comparative analysis between them.

关键词： image segmentation graph-search algorithms image foresting transform image processing watershed transform

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Automatic image segmentation by tree pruning

Automatic image segmentation by tree pruning

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19th Brazilian Symposium on Computer graphics and Image Processing (SIBGRAPI 2006)

关键词： image segmentation graph-search algorithms image foresting transform image processing watershed transform

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A discrete approach for supervised pattern recognition

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12th International Workshop on Combinatorial Image Analysis

作者： Papa, Joao P. Falcao, Alexandre X. Suzuki, Celso. T. N. Mascarenhas, Nelson D. A. Univ Estadual Campinas Inst Comp Av Albert Einstein 1216 Campinas SP Brazil Univ Fed Sao Carlos Dept Comp BR-13560 Sao Carlos SP Brazil

ISBN: (纸本)9783540782742

We present an approach for supervised pattern recognition based on combinatorial analysis of optimum paths from key samples (prototypes), which creates a discrete optimal partition of the feature space such that any unknown sample can be classified according to this partition. A training set is interpreted as a complete graph with at least one prototype in each class. They compete among themselves and each prototype defines an optimum-path tree, whose nodes are the samples more strongly connected to it than to any other. The result is an optimum-path forest in the training set. A test sample is assigned to the class of the prototype which offers it the optimum path in the forest. The classifier is designed to achieve zero classification errors in the training set, without over-fitting, and to learn from its errors. A comparison with several datasets shows the advantages of the method in accuracy and efficiency with respect to support vector machines.

关键词： supervised learning optimum-path forest image foresting transform pattern recognition graph-search algorithms

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Supervised Pattern Classification Based on Optimum-Path Forest

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2009年第2期19卷 120-131页

作者： Papa, J. P. Falcao, A. X. Suzuki, C. T. N. Univ Estadual Campinas Inst Comp Campinas SP Brazil

We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimum-cost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 120-131, 2009;Published online in Wiley InterScience (***). DOI 10.1002/ima.20188

关键词： supervised learning image foresting transform pattern recognition image analysis graph-search algorithms

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