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

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142 CHAPTER 5. COMPUTER SCIENCE GRID STRATEGIES Figure 5.6.10: An example workflow shown on the left. On the right hand side the parameters of the selected icon (highlight in yellow) are shown. The gray and purple bars at the top are the toolbars containing the available workflow components. create large pipelines to solve complex problems, e.g. a combination of many preprocessing steps followed by a multi-level analysis of data. A big advantage of workflows over manual execution of different tools is that a workflow is designed once and then applied to many different datasets (even in parallel). For a workflow system we need two basic components: (a) the workflow designer and (b) a workflow execution module, which are described below. In the QAD Grid system each workflow component is either (1) a QAD Grid service which will be handled by a worker (e.g a particular analyses), (2) a basic programming language function (such as an if/then condition or a variable assignment) or (3) an extended workflow function (such as sending an email or deleting a file). In the QAD grid system workflows are modeled as rooted directed graphs. Since we allow (conditional) jumps (see below) it can contain cycles. Figure 5.6.10 shows a small section of an example workflow. The left hand side of the figure shows the workflow in the workflow editor. Each icon represents one component of the workflow. At the right hand side, the parameters of the selected item (dyed in yellow) are shown: since this component is a “send email” item one needs to specify the receiver address and a mail body. Other items would presumably have more parameters. An important concept in the QAD Grid workflow system is the usage of user defined variables. These variables allow the assignment of values to workflow variables at execution time. That means, a workflow can be designed and variables, such as the dataset to be analyzed or particular algorithm parameters, can be set by the user when the workflow is executed. 5.6.1 Workflow Elements The following paragraphs describe the elements that can be used to build a workflow.
5.6. QAD GRID WORKFLOWS 143 Services Each registered service (see section 5.5.1) can be used in a workflow. During the service registration process the needed input parameters and the output format are defined. When a service is included in a workflow for each of these input parameters a source must be specified. This can be a fixed parameter (e.g. a numeric value for an algorithm), the result of a database query, such as the result of a preceding workflow step or a user defined parameter that will be fixed at execution time (see below). In addition to these Grid services there exist further system services that require client/server interaction. For example, services that copy files from a server to a client. Programming Language Elements A QAD Grid workflow can contain so-called programming language elements. These are constructs known from imperative programming languages. The QAD Grid system knows the following constructs: Variables: The QAD Grid workflow system provides two kinds of variables: (a) workflow variables that are initialized and modified during the workflow design phase and (b) user defined variables that are initialized by the user at the time of execution and can be modified during workflow execution. Usually at least one user defined variable is used within a workflow that defined the dataset that is used. Assignment statements: Each variable can be assigned a value. Internally the value is represented as a string object and converted to numbers if required during workflow execution. Evaluation of expressions: Simple expressions (such as basic arithmetics or relational operators) can be evaluated at runtime. For example a variable “VAR1” can be assigned a value “VAR2 * 5” that would be first evaluated to five times the current value of the variable “VAR2” and then assigned to “VAR1”. Unconditional branching statements: This is commonly known as jump or goto. This item has a parameter that contains the target item the workflow execution process will jump to. Only special items (tags) can be used as jump targets. Conditional branching statements: When the workflow execution process hits this item type it first evaluates the condition parameter which must evaluate to “false” or “true”. For example a valid condition might be “VAR1 ≥ 5”. For each outcome (“true or false”) a jump target can be defined where the process will branch to. If none is given the process will continue with the next item. There are also higher conditional branching elements available for the user, such as readily implemented checks whether all jobs of a particular kind of this particular workflow are finished or whether a task finished with an error. This gets very handy for example if during pre-processing a 2D spectrum is to be split up into its 1D components. Then, this item can be used to check whether all of this splitting jobs have finished so the next workflow stage can be started.
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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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Page 181 and 182: Appendix B Curriculum Vitae Name Ti
Page 183 and 184: References Aebersold, R. and Mann,
Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
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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).
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REFERENCES 193 Stone, M. (1974). Cr
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REFERENCES 195 Washburn, M. P., Wol
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