Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques

The goal of QoS-aware service composition is to generate optimal composite services that satisfy the QoS requirements defined by clients. However, when compositions contain more than one execution path (i.e., multiple path's compositions), it is difficult to generate a composite service that simultaneously optimizes all the execution paths involved in the composite service at the same time while meeting the QoS requirements. This issue brings us to the challenge of solving the QoS-aware service composition problem, so called an optimization problem. A further research challenge is the determination of the QoS characteristics that can be considered as selection criteria. In this thesis, a smart QoS-aware service composition approach is proposed. The aim is to solve the above-mentioned problems via an optimization mechanism based upon the combination between runtime path prediction method and heuristic algorithms. This mechanism is performed in two steps. First, the runtime path prediction method predicts, at runtime, and just before the actual composition, execution, the execution path that will potentially be executed. Second, both the constructive procedure (CP) and the complementary procedure (CCP) heuristic algorithms computed the optimization considering only the execution path that has been predicted by the runtime path prediction method for criteria selection, eight QoS characteristics are suggested after investigating related works on the area of web service and web service composition. Furthermore, prioritizing the selected QoS criteria is suggested in order to assist clients when choosing the right criteria. Experiments via WEKA tool and simulation prototype were conducted to evaluate the methods used. For the runtime path prediction method, the results showed that the path prediction method achieved promising prediction accuracy, and the number of paths involved in the prediction did not affect the accuracy. For the optimization mechanism, the evaluation was conducted by comparing the mechanism with relevant optimization techniques. The simulation results showed that the proposed optimization mechanism outperforms the relevant optimization techniques by (1) generating the highest overall QoS ratio solutions, (2) consuming the smallest computation time, and (3) producing the lowest percentage of constraints violated number

決策樹形式知識之線上預測系統架構 | An On-Line Decision Tree-Based Predictive System Architecture

<p>頁次：60-76</p><p class="MsoNormal" style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;"><span style="font-size: small;"><span style="font-family: "新細明體","serif"; mso-ascii-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-fareast;">本研究提出一個決策樹形式知識的線上預測系統架構，其主要的目在於提供一個</span><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"><span style="font-family: Times New Roman;">Web-Based</span></span><span style="font-family: "新細明體","serif"; mso-ascii-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-fareast;">的知識發掘</span><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"><span style="font-family: Times New Roman;">(Knowledge Discovery, KD)</span></span><span style="font-family: "新細明體","serif"; mso-ascii-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-fareast;">及線上預測系統，而我們藉由使用這個系統可以進行歸納學習出決策樹形式的知識，並且在線上使用決策樹的知識來做分類和預測的工作。它的組成元件包含三個子系統：知識學習子系統、合併選擇決策樹子系統、線上預測子系統；三個儲存庫：決策樹知識法則庫、例子資料庫、和歷史知識法則庫；以及三個導入知識法則的介面：上傳例子集資料介面、輸入決策樹知識法則介面、及轉換決策樹</span><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"><span style="font-family: Times New Roman;">PMML(Predictive Model Markup Language)</span></span><span style="font-family: "新細明體","serif"; mso-ascii-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-fareast;">文件模組等。就整體系統運作流程而言，在知識學習方面，我們首先上傳例子集，接著使用知識學習子系統來發掘出知識，然後直接儲存於知識法則庫內。而在知識使用方面，我們可以利用線上預測子系統來存取知識法則庫內的知識以進行分類和預測的工作。在知識溝通方面，本系統提供一個轉換</span><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"><span style="font-family: Times New Roman;">PMML</span></span><span style="font-family: "新細明體","serif"; mso-ascii-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-fareast;">格式文件的模組，方便導入其他採礦工具所歸納學習出之決策樹形式的知識。而在知識整合方面，本系統使用合併選擇決策樹子系統來合併多棵決策樹形式的知識而成一棵決策樹。運用這個子系統有助於維護決策樹法則知識庫內的知識，而讓決策樹形式的知識在保有簡單樹狀結構下，進行知識法則的擴充，並且簡單樹狀結構有助於線上預測子系統對於系統預測結果之解釋和說明。有關後續研究方面，本研究擬實作此架構的元件，且對於合併決策樹方面，提出一些修剪策略來提昇決策樹之預測準確度，以及如何有效維護決策樹知識法則庫內的知識等課題。</span><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"></span></span></p><p class="MsoNormal" style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;"><span style="mso-fareast-font-family: 新細明體; mso-font-kerning: 0pt; mso-fareast-theme-font: minor-fareast;" lang="EN-US"><span style="font-size: small;"><span style="font-family: Times New Roman;">This paper presents an on-line decision tree-based predictive system architecture. The architecture contains nine components, including a database of the examples, a learning system of the decision trees, a knowledge base, a historical knowledge base, a maintaining interface of the decision trees, an interface to upload training and testing examples, a PMML (Predictive Model Markup Language) translator, an on-line predictive system, and a merging optional decision trees system. There are three channels to import knowledge in the architecture; the developers can upload the examples to the learning system to induce the decision tree, directly input the information of decision trees through the user interface, or import the decision trees in PMML format. In order to integrate the knowledge of the decision trees, we added the merging optional decision trees system into this architecture. The merging optional decision trees system can combine multiple decision trees into a single decision tree to integrate the knowledge of the trees. In the future research, we will implement this architecture as a real system in the web-based platform to do some empirical analyses. And in order to improve the performance of the merging decision trees, we will also develop some pruning strategies in the merging optional decision trees system.</span></span></span></p&gt

Bagged Randomized Conceptual Machine Learning Method

Formal concept analysis (FCA) is a scientific approach aiming to investigate, analyze and represent the conceptual knowledge deduced from the data in conceptual structures (lattice). Recently many researchers are counting on the potentials of FCA to resolve or contribute addressing machine learning problems. However, some of these heuristics are still far from achieving this goal. In another context, ensemble-learning methods are deemed effective in addressing the classification problem, in addition, introducing randomness to ensemble learning found effective in certain scenarios. We exploit the potentials of FCA and the notion of randomness in ensemble learning, and propose a new machine learning method based on random conceptual decomposition. We also propose a novel approach for rule optimization. We develop an effective learning algorithm that is capable of handling some of learning problem aspects, with results that are comparable to other ensemble learning algorithms

Answering complex questions : supervised approaches

x, 108 leaves : ill. ; 29 cmThe term “Google” has become a verb for most of us. Search engines, however, have certain limitations. For example ask it for the impact of the current global financial crisis in different parts of the world, and you can expect to sift through thousands of results for the answer. This motivates the research in complex question answering where the purpose is to create summaries of large volumes of information as answers to complex questions, rather than simply offering a listing of sources. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, this task is accomplished by the query-focused multidocument summarization systems. In this thesis we apply different supervised learning techniques to confront the complex question answering problem. To run our experiments, we consider the DUC-2007 main task. A huge amount of labeled data is a prerequisite for supervised training. It is expensive and time consuming when humans perform the labeling task manually. Automatic labeling can be a good remedy to this problem. We employ five different automatic annotation techniques to build extracts from human abstracts using ROUGE, Basic Element (BE) overlap, syntactic similarity measure, semantic similarity measure and Extended String Subsequence Kernel (ESSK). The representative supervised methods we use are Support Vector Machines (SVM), Conditional Random Fields (CRF), Hidden Markov Models (HMM) and Maximum Entropy (MaxEnt). We annotate DUC-2006 data and use them to train our systems, whereas 25 topics of DUC-2007 data set are used as test data. The evaluation results reveal the impact of automatic labeling methods on the performance of the supervised approaches to complex question answering. We also experiment with two ensemble-based approaches that show promising results for this problem domain

Action Recognition in Still Images: Confluence of Multilinear Methods and Deep Learning

Motion is a missing information in an image, however, it is a valuable cue for action recognition. Thus, lack of motion information in a single image makes action recognition for still images inherently a very challenging problem in computer vision. In this dissertation, we show that both spatial and temporal patterns provide crucial information for recognizing human actions. Therefore, action recognition depends not only on the spatially-salient pixels, but also on the temporal patterns of those pixels. To address the challenge caused by the absence of temporal information in a single image, we introduce five effective action classification methodologies along with a new still image action recognition dataset. These include (1) proposing a new Spatial-Temporal Convolutional Neural Network, STCNN, trained by fine-tuning a CNN model, pre-trained on appearance-based classification only, over a novel latent space-time domain, named Ranked Saliency Map and Predicted Optical Flow, or RankSM-POF for short, (2) introducing a novel unsupervised Zero-shot approach based on low-rank Tensor Decomposition, named ZTD, (3) proposing the concept of temporal image, a compact representation of hypothetical sequence of images and then using it to design a new hierarchical deep learning network, TICNN, for still image action recognition, (4) introducing a dataset for STill image Action Recognition (STAR), containing over 1M images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images, exposing the intrinsic difficulty of action recognition through its realistic scene and action complexity. Moreover, TSSTN, a two-stream spatiotemporal network, is introduced to model the latent temporal information in a single image, and using it as prior knowledge in a two-stream deep network, (5) proposing a parallel heterogeneous meta- learning method to combine STCNN and ZTD through a stacking approach into an ensemble classifier of the proposed heterogeneous base classifiers. Altogether, this work demonstrates benefits of UCF-STAR as a large-scale still images dataset, and show the role of latent motion information in recognizing human actions in still images by presenting approaches relying on predicting temporal information, yielding higher accuracy on widely-used datasets

Personalized Medicine Support System for Chronic Myeloid Leukemia Patients

Personalized medicine offers the most effective treatment protocols to the individual Chronic Myeloid Leukemia (CML) patients. Understanding the molecular biology that causes CML assists in providing efficient treatment. After the identification of an activated tyrosine kinase BCR-ABL1 as the causative lesion in CML, the first-generation Tyrosine Kinase inhibitors (TKI) imatinib (Glivec®), were developed to inhibit BCR-ABL1 activity and approved as a treatment for CML. Despite the remarkable increase in the survival rate of CML patients treated with imatinib, some patients discontinued imatinib therapy due to intolerance, resistance or progression. These patients may benefit from the use of secondgeneration TKIs, such as nilotinib (Tasigna®) and dasatinib (Sprycel®). All three of these TKIs are currently approved for use as frontline treatments. Prognostic scores and molecularbased predictive assays are used to personalize the care of CML patients by allocating risk groups and predicting responses to therapy. Although prognostic scores remain in use today, they are often inadequate for three main reasons. Firstly, since each prognostic score may generate conflicting prognoses for the risk index and it can be difficult to know how to treat patients with conflicting prognoses. Secondly, since prognostic score systems are developed over time, patients can benefit from newly developed systems and information. Finally, the earlier scores use mostly clinically oriented factors instead of those directly related to genetic or molecular indicators. As the current CML treatment guidelines recommend the use of TKI therapy, a new tool that combines the well-known, molecular-based predictive assays to predict molecular response to TKI has not been considered in previous research. Therefore, the main goal of this research is to improve the ability to manage CML disease in individual CML patients and support CML physicians in TKI therapy treatment selection by correctly allocating patients to risk groups and predicting their molecular response to the selected treatment. To achieve this objective, the research detailed here focuses on developing a prognostic model and a predictive model for use as a personalized medicine support system. The system will be considered a knowledge-based clinical decision support system that includes two models embedded in a decision tree. The main idea is to classify patients into risk groups using the prognostic model, while the patients identified as part of the high-risk group should be considered for more aggressive imatinib therapy or switched to secondgeneration TKI with close monitoring. For patients assigned to the low-risk group to imatinib should be predicted using the predictive model. The outcomes should be evaluated by comparing the results of these models with the actual responses to imatinib in patients from a previous medical trial and from patients admitted to hospitals. Validating such a predictive system could greatly assist clinicians in clinical decision-making geared toward individualized medicine. Our findings suggest that the system provides treatment recommendations that could help improve overall healthcare for CML patients. Study limitations included the impact of diversity on human expertise, changing predictive factors, population and prediction endpoints, the impact of time and patient personal issues. Further intensive research activities based on the development of a new predictive model and the method for selecting predictive factors and validation can be expanded to other health organizations and the development of models to predict responses to other TKIs.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201