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

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120 CHAPTER 5. COMPUTER SCIENCE GRID STRATEGIES Figure 5.3.7: Performance of GridFTP with OS-default buffer size (64KB, left) versus Performance of GridFTP with dynamically adapted buffer sizes (right). Shown are the average bandwidth values depending on the size of the transferred file (16, 32, 64, 128, 256, 512 MB). BSopt = 0.08sec · 100 Mbit sec = 8Mbit sec Since with a transmission rate of 1 Mbit/Sec one can transfer 0.125 MByte/Sec we get: BSopt = 8 Mbit /8 = 1MByte sec Therefore, the TCP buffer size for this connection should be set to 1 MByte. Now, in reality the assumption made at the beginning are not very likely to hold. First, if packet loss starts to occur (Mathis et al., 1997) have shown that throughput is then bounded by the Maximum Segment size (MSS), which is Maximum Transmission Unit12 (MTU) minus TCP/IP headers (in practice default values are: MTU: 1500bytes, TCP/IP Header: 40bytes and therefore MSS=1460bytes). T hroughput ∝∼ 0.7 · MSS RT T · √ packet loss This problem can be tackled by increasing the frame size to about 8KByte (Chase et al., 2001), that is using so-called Jumbo Frames. Since the maximum frame size is set by switches in the Internet on the path from sender to receiver we cannot do anything about this. The second problem we face is that the bandwidth delay (RTT values) can fluctuate quite wildly over lifetime as shown in Figure 5.3.6. Following (Thulasidasan et al., 2003) for the transmission part we have implemented dynamically adaptive TCP buffer sizes to allow TCP flow control to adapt to changing high-speed WAN environments, especially when transmitting large files. Figure 5.3.7 shows the effect of this tuning in a concrete example where this technique was used in GridFTP. It can be easily seen that performance approximately quadruplicates. User Management In the QAD Grid a user is modeled as a database object with the following properties: Account information: This can be a local account or a network account. In both cases a user name is stored. In the local case an additional 12 Size of the largest packet that a network protocol can transmit.
5.3. QAD GRID PLATFORM SERVER 121 password (actually its MD5 hash value) is stored as well, which is used for authentication. In the network case authentication is done by querying a network authentication server, e.g. via LDAP or Kerberos protocol. Group membership: A users can be member of one or many groups (e.g. an institution) which can be used to give many users at once access to particular objects, such as data or functions. Using this feature it is also possible to implement some kind of hierarchy, e.g. simple user or administrator groups. Account details: Further (optional) information such as real name, institutional affiliation, e-mail address and so forth can also be stored. Billing details: Since system usage logs are collected automatically (such as CPU time used for computation) these information can be used to implement some kind of billing. To be able to login to the QAD Grid a user needs to be registered at the platform server. This is done through a special web-site new users can access. At this site they enter their user details which then needs to be reviewed by an administrator. After successful registration a user can log into the webbased front-end of the platform server and use its services, such as start of new computations or analyses (see section 5.5) or view results of previous runs. After successful login a fine-grained access control list (ACL) system (see section 5.3.1) is used to determine (1) what parts of the system the user can see, what (2) functions he can use and (3) what data and results he is allowed to see. (1) Access to web-sites: When a user accesses a web-page the web-server checks during the on-load sequence of this site whether there exists an ACL entry that allows this user to see this site. (2) Use of functions: As in the web-site case each time a user requests the use of a function, e.g. start of a computation, the web-server first checks if the users has appropriate credentials to use this. (3) Access to data: There are many scenarios when a user needs to access data, e.g. visualization of results, computations that need data or just display of raw data. Again, there exist ACL entries for each dataset available in the system that is checked if data is requested either through the web-server or directly from the database. Worker Management A worker is modeled as a database object with the following properties: Account information: This includes a user name and a password and is mainly used to log into the database. Public key: The public key is used by the platform server to encrypt communication (initialized by the server) with this worker. Certificate: The worker-specific certificate is issued the first time a worker successfully registers at the platform server. It contains a MD5 digest of a combination of the service id this worker offers and the public key of this worker. It is used for authentication purposes.
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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
Page 75 and 76: 4.2. STATISTICAL REMARKS 69 1. Vali
Page 77 and 78: 4.2. STATISTICAL REMARKS 71 Molar v
Page 79 and 80: 4.2. STATISTICAL REMARKS 73 � Fir
Page 81 and 82: 4.2. STATISTICAL REMARKS 75 Let ˆ
Page 83 and 84: 4.2. STATISTICAL REMARKS 77 the boo
Page 85 and 86: 4.3. STUDY RESULTS 79 Figure 4.3.3:
Page 87 and 88: 4.3. STUDY RESULTS 81 Figure 4.3.4:
Page 89 and 90: 4.3. STUDY RESULTS 83 � kNN (gen.
Page 91 and 92: 4.3. STUDY RESULTS 85 d(x, θi) =
Page 93 and 94: 4.3. STUDY RESULTS 87 pairs of obje
Page 95 and 96: 4.3. STUDY RESULTS 89 � “Peptid
Page 97 and 98: 4.4. IDENTIFICATION OF PROTEOMIC FI
Page 107 and 108: 4.6. BIOLOGICAL APPLICATIONS 101 4.
Page 109 and 110: Chapter 5 Computer Science Grid Str
Page 111 and 112: 5.1. INTRODUCTION 105 � A node is
Page 113 and 114: 5.1. INTRODUCTION 107 of and config
Page 115 and 116: 5.1. INTRODUCTION 109 particular pr
Page 117 and 118: 5.2. THE QUASI AD-HOC (QAD) GRID 11
Page 119 and 120: 5.2. THE QUASI AD-HOC (QAD) GRID 11
Page 121 and 122: 5.3. QAD GRID PLATFORM SERVER 115 F
Page 123 and 124: 5.3. QAD GRID PLATFORM SERVER 117 j
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Page 135 and 136: 5.4. QAD GRID WORKER 129 field. A w
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Page 139 and 140: 5.4. QAD GRID WORKER 133 Figure 5.4
Page 141 and 142: 5.4. QAD GRID WORKER 135 Checkpoint
Page 143 and 144: 5.4. QAD GRID WORKER 137 database b
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Page 149 and 150: 5.6. QAD GRID WORKFLOWS 143 Service
Page 151 and 152: 5.6. QAD GRID WORKFLOWS 145 Figure
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Page 155 and 156: 5.7. RELATED WORK 149 Table 5.7.1 -
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Page 159 and 160: 6.2. CASE STUDIES 153 6.2 Case Stud
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Page 165 and 166: 6.2. CASE STUDIES 159 The peak pick
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Page 169 and 170: 6.2. CASE STUDIES 163 Approach Base
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Page 173 and 174: Chapter 7 Related Work In this chap
Page 175 and 176: 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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