General Computer Science 320201 GenCS I & II Lecture ... - Kwarc

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$Mathematical Documents want to Active Digital Math ... - Kwarc$

Web search engines usually operate in four phases/components 1. Data Acquisition: a web crawler finds and retrieves (changed) web pages 2. Search in Index: write an index and search there. 3. Sort the hits: e.g. by importance 4. Answer composition: present the hits (and add advertisement) c○: Michael Kohlhase 372 We will now go through the four phases in turn and discuss specifics Data Acquisition for Web Search Engines: Web Crawlers Definition 565 A web crawler or spider is a computer probram that browses the WWWeb in an automated, orderly fashion for the purpose of information gathering. Web crawlers are mostly used for data acquisition of web search engines, but can also automate web maintenance jobs (e.g. link checking). The WWWeb changes: 20% daily, 30% monthly, 50% never A Web crawler cycles over the following actions 213
1. reads web page 2. reports it home 3. finds hyperlinks 4. follows them Note: you can exclude web crawlers from your web site by configuring robots.txt. c○: Michael Kohlhase 373 Even though the image of a crawling insect suggests that, a web crawler is a program that lives on a host and stays there, downloading selected portions of the WWWeb. Actually, the picture we paint above is quite simplified, a web crawler has to be very careful not to download web pages multiple times to make progress. Recall that – seen as directed graphs – hypertexts may very well be cyclic. Additionally, much of the WWWeb content is only generated by web applications on user request, therefore modern web crawlers will try to generate queries that generate pages they can crawl. The input for a web search engine is a query, i.e. a string that describes the set of documents to be referenced in the answer set. There are various types of query languages for information retrieval; we will only go into the most common ones here Web Search: Queries Definition 566 A web search query is a string that describes a set of document (fragments). Example 567 Most web search engines accept multiword queries, i.e. multisets of strings (words). Example 568 Many web search engines also accept advanced query operators and wild cards ? (e.g. science? means search for the keyword “science” but I am not sure of the spelling) * (wildcard, e.g. comput* searches for keywords starting with comput combined with any word ending) AND (both terms must be present) OR (at least one of the terms must be present) Also: Searches for various information formats & types, e.g. image search, scholarly search (require specialized query languages) c○: Michael Kohlhase 374 We now come to the central component of a web search engine: the indexing component. The main realization here is that with the size of the current web it is impossible to search the web linearly by comparing the query to the crawled documents one-by-one. So instead, the web search 214
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General Computer Science 320201 Gen
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Java programmer” on the practical
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Contents 1 Preface i 2 Representati
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4 Search and Declarative Computatio
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Fundamental Algorithms and Data str
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To earn an audit you have to take t
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i.e. to function as a member of the
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a factor two in speed. This ability
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“Applets, Not Craplets tm ” (-
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c○: Michael Kohlhase 17 Can be re
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Example 10 (Kruskal’s algorithm,
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n 100n µs 7n 2 µs 2 n µs 1 100
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2.2 Elementary Discrete Math 2.2.1
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Axiom 19 (P 1) “ ” (aka. “zer
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Theorem 36 is a very useful fact to
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Definition 43 The unary product ope
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y stating element-hood (a ∈ S) or
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Idea: We need a notion of “counti
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Example 64 On sets of persons, the
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Definition 82 We say that a set A i
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clean enough to learn important con
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Definition 90 anonymous variables (
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c○: Michael Kohlhase 66 Defining
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- fun both_plus (x:int,y:int) = fn
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+(〈n, o〉) = n (base) and +(〈m
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the axiom says that any object that
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epresent them as a data type, where
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Example 127 〈{N}, {[o: N], [s: N
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The central idea here is what we ha
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Substitutions Definition 149 Let A
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Idea: Well-formed parts of construc
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Other programming languages chose a
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generally: fn+1 := fn + fn−1 plus
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input/output: the interesting bit a
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exception NaN; (* Not a Number *) f
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Example 191 If A = {a, b, c}, then
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Example 210 The Morse Code in the t
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The first 32 characters are control
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Idea: Unicode supports multiple enc
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2.5 Boolean Algebra We will now loo
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What a mess! Iϕ((x1 + x2) + (x1
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c○: Michael Kohlhase 140 The defi
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(f ≤a g), iff there is an n0 ∈
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P.1.2.3 then there are ei ∈ Ebool
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2.5.4 The Quine-McCluskey Algorithm
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the disjunctive normal form, and th
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Proof: by contradiction: let p /∈
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So, the minimal polynomial of f is
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2.6 Propositional Logic 2.6.1 Boole
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Idea: Import semantics from Boolean
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which we can always do, since we ha
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e quite difficult to establish in g
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H 0 axioms are valid Lemma 316 The
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c○: Michael Kohlhase 188 The enta
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The deduction theorem and the entai
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Inference with local hypotheses [A
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2.7 Machine-Oriented Calculi Now we
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Thus the tableau procedure can be u
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A ∨ BT AT BT A ⇒ BT AF BT
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Proof: P.1 It is easy to see tahat
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(P ⇒ Q ⇒ R) ⇒ (P ⇒ Q) ⇒ P
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⎧ ⎪⎨ We represented the maze
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defined a directed graph to be a se
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Paths in Graphs Definition 373 Giv
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This allows us to view Boolean expr
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Computing with Combinational Circui
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Corollary 399 A fully balanced tree
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X 1 X 2 X 3 X n = if L i =X i if L
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3.2 Arithmetic Circuits 3.2.1 Basic
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S S ψ ψ −1 fS = ψ −1 ◦ fT
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The Full Adder Definition 415 The
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first summand 3 4 7 9 8 3 4 7 9 2 s
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c○: Michael Kohlhase 249 The anal
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Problems of Sign-Bit Systems Gener
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generate the n-bit binary number re
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and an + bn + (icn(a, b, c)) = 〈
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Summary: We have built a combinatio
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To understand the operation of the
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Example 443 (Address decoder logic
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3.4 Computing Devices and Programmi
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Our notion of time is in this const
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instructions LOADIN 1 and LOADIN 2
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Definition 457 An ASM program VM is
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instruction effect VPC peek i push
Page 169 and 170: c○: Michael Kohlhase 289 With the
Page 171 and 172: Imperative Stack Operations: peek l
Page 173 and 174: A SW program (see the next slide fo
Page 175 and 176: arguments to arithmetic operations
Page 177 and 178: µML, a very simple Functional Prog
Page 179 and 180: call pushes the return address (of
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Page 183 and 184: [proc 2 26, con 0, arg 2, leq, cjp
Page 185 and 186: eturn takes the current frame from
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Page 189 and 190: Compiling µML Expressions (Continu
Page 191 and 192: c○: Michael Kohlhase 325 We want
Page 193 and 194: State Machine: 1 1,R 1 1,R 1 1,L 1
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Page 197 and 198: the turing function uses will_halt
Page 199 and 200: Terabyte (T B) 1,000,000,000,000 by
Page 201 and 202: Layers in TCP/IP: TCP/IP uses encap
Page 203 and 204: name comment 4 version IPv4 or IPv6
Page 205 and 206: Domain names must be registered to
Page 207 and 208: That was almost all, but we close t
Page 209 and 210: c○: Michael Kohlhase 353 Note tha
Page 211 and 212: Definition 529 HTTP is used by a cl
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Page 215 and 216: Server-Side Scripting: Programming
Page 217 and 218: c○: Michael Kohlhase 367 Indeed,
Page 219: presentation tools), but can also i
Page 223 and 224: Definition 570 Let A be a web page
Page 225 and 226: can combine both to falsify communi
Page 227 and 228: Candidates for one-way/trapdoor fun
Page 229 and 230: on a UNIX system, you can create a
Page 231 and 232: conceptually: transfer of directed
Page 233 and 234: Definition 591 The XML path languag
Page 235 and 236: Resources: Globally Identified by U
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Page 239 and 240: Problem solving Problem: Find algo
Page 241 and 242: Single-state Problem: Start in 5
Page 243 and 244: States integer locations of tiles A
Page 245 and 246: Implementation: States vs. nodes A
Page 247 and 248: Breadth-First Search c○: Michael
Page 249 and 250: Breadth-First Search: Romania c○:
Page 251 and 252: Note: Equivalent to breadth-first s
Page 253 and 254: A B C D E F G H I J K L M N O Depth
Page 255 and 256: A B C D E F G H I J K L M N O Depth
Page 257 and 258: Iterative deepening search Depth-l
Page 259 and 260: Breadth-first search Iterative deep
Page 261 and 262: Sibiu 253 Greedy Search: Romania Ar
Page 263 and 264: P.2 Let n be an unexpanded node on
Page 265 and 266: A ∗ search: Properties Complete Y
Page 267 and 268: Definition 618 (n-queens problem) P
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GAs = evolution: e.g., real genes e
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Definition 632 A query is a list of
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(autoload ’run-prolog "prolog" "S
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Example 638 Computing the the n th
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Deduction: knowledge extension Abd
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[Koh06] Michael Kohlhase. OMDoc - A
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astarSearch search, 255 asymmetric-
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infinite, 32 counter program, 152,
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functional variable, 170 gate, 122
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media access control address, 194 M
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procedure abstract, 54 procedure de
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function, 30 spanning tree, 13 spro
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name, 70 variable functional, 170 v
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General Computer Science 320201 GenCS I & II Lecture ... - Kwarc

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