New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

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110 CHAPTER 5. COMPUTER SCIENCE GRID STRATEGIES 2. Pipeline oriented: A large workflow consisting of many different and dependent tasks is created and for each step different jobs are submitted to the queue According to the definition given by Workflow Management Coalition, a workflow is: “The automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules” (WfMC, 2007). A business process is the set of procedures required for obtaining a given result, therefore workflow is the automation of a set of operations that allows obtaining a given result, with the exchange of information among involved entities and with respect to defined procedural rules. The operations involved by a workflow are called activities; an activity is a part of the entire work and it represents a logical step in the process. Since in the data-oriented case the problem can be reduced to how to split up the data the pipeline-oriented case is considerably more difficult. Here, the workflow must be defined, implemented, and executed. This issue of execution in a given order of complex computational tasks in a Grid environment has even been discussed by the “Grid Computing Environment Working Group”, in the Global Grid Forum (Hugh P. Bivens, 2001). To run a workflow into a Computational Grid it is necessary to define a language to be interpreted and manipulated automatically from a management system, which should allow defining a set of activities, their relation, involved entities (i.e. Applications, data resources, etc.) and some criteria for determining the start and end of the processes. However, there are many available workflow manager that fulfill these tasks (such as Nimrod, Triana, Taverna, Pegasus, Proteus) but they need to be installed on top of an existing Grid platform and are usually not part of them (a nice exception is the myGrid project (Stevens et al., 2003)). 5.1.3 Problems of Todays Grid Systems Nowadays most Grid systems are run by large research organizations, companies or governmental institutions such as NASA (Information Power Grid), US Department of Energy together with IBM (Science Grid) or the European Union (EGEE). These organizations have dedicated staff who set up, configure, administrate and manage these projects. This is still far away from trivial, making these administrators vital to the task. As in the case of the Internet boom in the late 1990’s what is really is needed to fulfill the “everyone can use the Grid” vision is � Significantly reduce the complexity of installing and maintaining a Grid system. � Provide easy to use client software that can compute jobs on Grid resources. � Allow for easy participation in a Grid system either as user who wants to compute a large problem or to provide resources - this includes things such as registration and security models. � Merge the needed components such as the Grid platform, workflow manager and scheduler into some consistent product. � Allow for easy enabling of Grid technology to existing algorithms.
5.2. THE QUASI AD-HOC (QAD) GRID 111 5.2 The Quasi Ad-hoc (QAD) Grid This section describes our quasi ad-hoc approach. Before we dive into details section 5.2.2 will give a broad overview of the basic modules. These are the building blocks of this approach. Subsequently, section 5.2.3 describes details of the underlying concepts which differ from traditional Grid solutions. 5.2.1 Why Another Grid Approach ? � No need for real client software � Heterogeneity: embedding of specialized hardware � Unreliability of nodes: specialized checkpoints and migration possibilities � Merging the grid and cluster idea � Hot deployment of services into a running system 5.2.2 Modules This section introduces the main building blocks of the quasi ad-hoc Grid from a users’ perspective (see Figure 5.2.2): Figure 5.2.2: Grid - from the users’ perspective. Elements in blue are standard Grid components; green elements are extensions. User: The User can login to the (web-based) platform server for managing and control purposes. Depending on his credentials he might only have limited access to data, clients and Grid services / functions.
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New Statistical Algorithms for the
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Contents Acknowledgments . . . . .
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New Statistical Algorithms for the
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Extended Abstract English Version M
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German Version Das Gebiet der Prote
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Chapter 1 Introduction and Survey 1
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1.2. GOALS, OBJECTIVES AND TASKS 7
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1.2. GOALS, OBJECTIVES AND TASKS 9
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Chapter 2 Preliminaries 2.1 Topic O
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2.1. TOPIC OVERVIEW 13 Figure 2.1.1
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2.1. TOPIC OVERVIEW 15 completeness
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2.2. AN EXAMPLE 17 (a) Opera A (b)
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2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
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2.2. AN EXAMPLE 21 successes. We ca
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Chapter 3 Mathematical Modeling and
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3.2. INTRODUCTION TO MALDI TOF MS 2
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3.3. PREPROCESSING 31 mix (external
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3.3. PREPROCESSING 33 Figure 3.3.5:
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3.3. PREPROCESSING 35 Figure 3.3.7:
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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3.5. PEAK DETECTION IN 2D MAPS 43
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3.6. PEAK REGISTRATION (ALIGNMENT)
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3.7. IDENTIFYING POTENTIAL FEATURES
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3.8. EXTRACTING FINGERPRINTS 57 Fig
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Page 89 and 90: 4.3. STUDY RESULTS 83 � kNN (gen.
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6.2. CASE STUDIES 161 first entry i
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6.2. CASE STUDIES 163 Approach Base
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6.2. CASE STUDIES 165 Figure 6.2.5:
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Chapter 7 Related Work In this chap
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Chapter 8 Conclusion and Future Dir
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8.3. FROM BIOMARKERS TO BIOPRINTS 1
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Appendix A Implementation Details T
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Appendix B Curriculum Vitae Name Ti
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References Aebersold, R. and Mann,
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REFERENCES 179 Breiman, L. (2001).
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REFERENCES 181 Downard, K. M. and M
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REFERENCES 183 Gillette, M. A., Man
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REFERENCES 185 Huyghe, E., Muller,
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REFERENCES 187 Kuijpens, J. L. P.,
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REFERENCES 189 McLachlan, S. M. and
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REFERENCES 191 Platt, J. C. (1999).
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REFERENCES 193 Stone, M. (1974). Cr
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REFERENCES 195 Washburn, M. P., Wol
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