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

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146 CHAPTER 5. COMPUTER SCIENCE GRID STRATEGIES 3. The workflow execution service stores the state of all current variables and the new active node to the database. This allows for resuming from this point after a system crash. 4. The workflow execution service sets the returned node to active and starts over with step (1). Experiments have shown that the workflow execution service is idle most of the time during execution. Therefore, we have implemented this service as a multithreaded service: The main thread handles all database requests including scanning the database for new workflows to execute. If a new request is found and the overall system load is not too high a child-thread is created that handles this workflow. The main advantage of this approach is that we save connections to the database, since only the main thread needs to have an active database connection. This is beneficial since each open connection slows down the database server (see e.g. (Huddleston et al., 2006)). 5.7 Related Work As mentioned in the previous sections, most systems that are used for building distributed computing systems or computational grids have evolved to quite complex software frameworks. In Table 5.7.1 we list some exemplary Grid projects that are currently (2007/2008) active and have been studied by us 21 . There are roughly three categories these projects fall into: 1. e-Infrastructure: This refers to a research environment in which a researcher has shared access to scientific facilities (such as data, computing or sensors), regardless of their type and location in the world. 2. Middleware: A middleware connects software applications enabling exchange of data. Thus, it organizes and integrates the resources in a Grid. One of its main purpose is to automate the required machine to machine negotiations, such as negotiating the exchange of resources on behalf of Grid users and resource providers. It also provides the core foundation (basic services) for grid applications including areas such as: security, resource management, information services and data management. 3. Application: These are projects within the context of specific scientific fields that are devoted to explore and harness grid technology. Depending on its state (fully operational vs. experimental) these projects are called application or testbed. Since there exists neither the exemplary Grid system nor a standard for Grid systems (although systems like Globus seem to become the de-facto standard) we will not describe any system in great detail nor give a detailed comparison to our system 22 . Rather, we use the table to illustrates two things: (a) there is a need for distributed computing systems in many application areas and (b) there is a large variety of systems with different scales, aims, approaches and technological foundations. However, as far as our analyses have shown none of them manages to provide a powerful and at the same time easy 21 Another even more exhaustive list can be found here (GridInfoware, 2008)). 22 For a detailed comparison of four large Grid systems see e.g. (Asadzadeh et al., 2006) and references therein.
5.7. RELATED WORK 147 to set-up system that can be used in an (quasi) ad-hoc manner, as our system is designed to operate. Further, there does not seem to be a fully equipped framework that provides all layers such as middleware, workflow system, Grid monitor and web portal. All of these layers have been incorporated in the system introduced in this thesis. Table 5.7.1: Overview of several Grid projects Project Category Description @neurIST Application ACGT Application Grid platform for management, integration and processing of data associated with cerebral aneurysm and subarachnoid haemorrage. Grid platform to support exchanges of clinic and genetic information with a particular focus on breast cancer data. BioinfoGRID Middleware This project aims to connect European computer centers to offer computational services especially in BIOPATTERN Application the area of Bioinformatics. Within this project the members want to create a pan-European platform enabling sharing and analysis people’s bioprofiles. This should allow to combat major diseases such as cancer and brain diseases. Condor Middleware Condor is a specialized workload management system for compute-intensive jobs. Similar to other batch systems, Condor provides a job queueing, several scheduling policies and priority schemas, resource monitoring and management. The Condor- G extension is fully interoperable with resources DataGrid Application DataMining Grid Application D-Grid Infrastructure DutchGrid Application Continued on Next Page. . . managed by the Globus system. This was one of the first projects (and predecessor to the EGEE project) that aimed to enable intensive computation and analysis of shared large-scale databases across widely distributed scientific communities. This consortium develops generic and sectorindependent data-mining tools and services for the grid. The German national Grid initiative wants to establish a sustainable Grid infrastructure for e- Science in Germany by (a) strengthening existing Grid user communities and (b) integrating and extending existing but dispersed middleware testbeds. The DutchGrid is the platform for Grid Computing and Technology in the Netherlands.
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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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3.8. EXTRACTING FINGERPRINTS 59 FID
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3.8. EXTRACTING FINGERPRINTS 61 Dim
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3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Chapter 4 (Bio-)Medical Application
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4.1. DATA USED 67 4.1.2 Serum Data
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4.2. STATISTICAL REMARKS 69 1. Vali
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4.2. STATISTICAL REMARKS 71 Molar v
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4.2. STATISTICAL REMARKS 73 � Fir
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4.2. STATISTICAL REMARKS 75 Let ˆ
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4.2. STATISTICAL REMARKS 77 the boo
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4.3. STUDY RESULTS 79 Figure 4.3.3:
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4.3. STUDY RESULTS 81 Figure 4.3.4:
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4.3. STUDY RESULTS 83 � kNN (gen.
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4.3. STUDY RESULTS 85 d(x, θi) =
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4.3. STUDY RESULTS 87 pairs of obje
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4.3. STUDY RESULTS 89 � “Peptid
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4.4. IDENTIFICATION OF PROTEOMIC FI
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4.4. IDENTIFICATION OF PROTEOMIC FI
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Page 183 and 184: References Aebersold, R. and Mann,
Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
Page 187 and 188: REFERENCES 181 Downard, K. M. and M
Page 189 and 190: REFERENCES 183 Gillette, M. A., Man
Page 191 and 192: REFERENCES 185 Huyghe, E., Muller,
Page 193 and 194: REFERENCES 187 Kuijpens, J. L. P.,
Page 195 and 196: REFERENCES 189 McLachlan, S. M. and
Page 197 and 198: REFERENCES 191 Platt, J. C. (1999).
Page 199 and 200: REFERENCES 193 Stone, M. (1974). Cr
Page 201 and 202: REFERENCES 195 Washburn, M. P., Wol
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