Index construc-on. Friday, 8 April 16 1
|
|
- Mitchell Walsh
- 6 years ago
- Views:
Transcription
1 Index construc-on Informa)onal Retrieval By Dr. Qaiser Abbas Department of Computer Science & IT, University of Sargodha, Sargodha, 40100, Pakistan Friday, 8 April 16 1
2 4.1 Index construction How do we construct an index? What strategies can we use with limited main memory? Hardware Basics Many design decisions in information retrieval are based on the characteristics of hardware We begin by reviewing hardware basics 2
3 Hardware basics Access to data in memory is much faster than access to data on disk. Disk seeks: No data is transferred from disk while the disk head is being positioned. Therefore: Transferring one large chunk of data from disk to memory is faster than transferring many small chunks. Disk I/O is block-based: Reading and writing of entire blocks (as opposed to smaller chunks). Block sizes: 8KB to 256 KB. 3
4 Hardware basics Servers used in IR systems now typically have several GB of main memory, sometimes tens of GB. Available disk space is several (2 3)orders of magnitude larger. Fault tolerance is very expensive: It s much cheaper to use many regular machines rather than one fault tolerant machine. 4
5 Hardware basics 5
6 4.2 Recall Inverted Index Friday, 8 April 16 6
7 Earlier approach Pass through the collec)on and assemble all term docid pairs. Sort the pairs with the term as the dominant key and docid as the secondary key. Finally, organize the docids for each term into a pos)ngs list and compute sta)s)cs like term and document frequency. For small collec)ons, all this can be done in memory. However, we will describe methods for large collec)ons that require the use of secondary storage. To make index construc)on more efficient, we represent terms as termids (instead of strings as we did in Figure 1.4), as a unique serial number. Friday, 8 April 16 7
8 Reuters-RCV1 collec<on The corpus we ll use isn t really large enough, but it s publicly available and is at least a more plausible example. As an example for applying index construction algorithms, we will use the Reuters RCV1 collection (Approx. 1GB). This is one year of Reuters newswire (part of 1996 and 1997) 8
9 A Reuters RCV1 document 9
10 Reuters RCV1 statistics
11 Issue in Indexing Reuters-RCV1 has 100 million tokens. Collec)ng all termid docid pairs of the collec)on using 4 bytes each for termid and docid therefore requires 0.8 GB of storage. Typical collec)ons today are ozen one or two orders of magnitude larger than Reuters-RCV1. You can easily see how such collec)ons overwhelm (bury) even large computers if we try to sort their termid docid pairs in memory. If the size of the intermediate files during index construc)on is within a small factor of available memory, then the compression techniques introduced in Chapter 5 can help; however, the pos)ngs file of many large collec)ons cannot fit into memory even azer compression.
12 Issue in Indexing With main memory insufficient, we need to use an external sor+ng algorithm, that is, one that uses disk. For acceptable speed, the central requirement of such an algorithm is that it minimize the number of random disk seeks during sor)ng sequen)al disk reads are far faster than seeks as we explained in Sec)on 4.1. One solu)on is the blocked sort-based indexing algorithm or BSBI in Figure 4.2.
13 BSBI Algorithm
14 BSBI Algorithm The algorithm parses documents into termid docid pairs and accumulates the pairs in memory un)l a block of a fixed size is full (PARSENEXTBLOCK in Figure 4.2). We choose the block size to fit comfortably into memory to permit a fast in-memory sort. The block is then inverted and wrigen to disk. Inversion involves two steps. First, we sort the termid docid pairs. Next, we collect all termid docid pairs with the same termid into a pos)ngs list, where a pos+ng is simply a docid. The result, an inverted index for the block we have just read, is then wrigen to disk.
15 BSBI Algorithm Applying this to Reuters-RCV1 and assuming we can fit 10 million termid docid pairs into memory, we end up with ten blocks, each an inverted index of one part of the collec)on. In the final step, the algorithm simultaneously merges the ten blocks into one large merged index. An example with two blocks is shown in Figure 4.3. To do the merging, we open all block files simultaneously, and maintain small read buffers for the ten blocks we are reading and a write buffer for the final merged index we are wri)ng.
16 BSBI Algorithm
17 BSBI Algorithm Complexity How expensive is BSBI? Its )me complexity is Θ(T log T) because the step with the highest )me complexity is sor)ng and T is an upper bound for the number of items (i.e., the number of termid docid pairs).
18 Class Exercise Exercise 4.1 If we need T log T comparisons (where T is the number of termid docid pairs) and 2 two disk seeks for each comparison, how much )me would index construc)on for Reuters-RCV1 take if we used disk instead of memory for storage and an unop)mized sor)ng algorithm (i.e., not an external sor)ng algorithm)? Use the system parameters in Table 4.1.
19 Solu<on Disk seek )me = 5x10-3 s 2 x (5x10-3 ) seconds per comparison Transfer )me = 2 x 10-8 s per byte Low level opera)ons = 10-8 seconds How long would it take to make T(log₂T) comparisons with 2 disk seeks per comparison? T(log₂T) x 2(5x10-3 s)...consider transfer )me and any low level opera)ons
20 Class Exercise Exercise 4.2 [ ] How would you create the dic)onary in blocked sort-based indexing on the fly to avoid an extra pass through the data? Solu<on: If you skipped the ini)al step of sor)ng the termids and docids and created a pos)ngs list on the fly whenever you encountered a new termid then created new pos)ngs in that pos)ngs list for each new incidences of termids would you avoid an extra pass through the data and would it s)ll be blocked sort-based indexing?
Index construc-on. Friday, 8 April 16 1
Index construc-on Informa)onal Retrieval By Dr. Qaiser Abbas Department of Computer Science & IT, University of Sargodha, Sargodha, 40100, Pakistan qaiser.abbas@uos.edu.pk Friday, 8 April 16 1 4.3 Single-pass
More informationIntroduc)on to. CS60092: Informa0on Retrieval
Introduc)on to CS60092: Informa0on Retrieval Ch. 4 Index construc)on How do we construct an index? What strategies can we use with limited main memory? Sec. 4.1 Hardware basics Many design decisions in
More informationInforma(on Retrieval
Introduc*on to Informa(on Retrieval CS276: Informa*on Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 4: Index Construc*on Plan Last lecture: Dic*onary data structures Tolerant retrieval
More informationInformation Retrieval and Organisation
Information Retrieval and Organisation Dell Zhang Birkbeck, University of London 2015/16 IR Chapter 04 Index Construction Hardware In this chapter we will look at how to construct an inverted index Many
More informationInformation Retrieval
Introduction to Information Retrieval Lecture 4: Index Construction Plan Last lecture: Dictionary data structures Tolerant retrieval Wildcards This time: Spell correction Soundex Index construction Index
More informationInformation Retrieval
Introduction to Information Retrieval Lecture 4: Index Construction 1 Plan Last lecture: Dictionary data structures Tolerant retrieval Wildcards Spell correction Soundex a-hu hy-m n-z $m mace madden mo
More informationInformation Retrieval
Information Retrieval Suan Lee - Information Retrieval - 04 Index Construction 1 04 Index Construction - Information Retrieval - 04 Index Construction 2 Plan Last lecture: Dictionary data structures Tolerant
More informationIndex construction CE-324: Modern Information Retrieval Sharif University of Technology
Index construction CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford) Ch.
More information3-2. Index construction. Most slides were adapted from Stanford CS 276 course and University of Munich IR course.
3-2. Index construction Most slides were adapted from Stanford CS 276 course and University of Munich IR course. 1 Ch. 4 Index construction How do we construct an index? What strategies can we use with
More informationindex construct Overview Overview Recap How to construct index? Introduction Index construction Introduction to Recap
to to Information Retrieval Index Construct Ruixuan Li Huazhong University of Science and Technology http://idc.hust.edu.cn/~rxli/ October, 2012 1 2 How to construct index? Computerese term document docid
More informationIndex construction CE-324: Modern Information Retrieval Sharif University of Technology
Index construction CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2016 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford) Ch.
More informationIndex Construction 1
Index Construction 1 October, 2009 1 Vorlage: Folien von M. Schütze 1 von 43 Index Construction Hardware basics Many design decisions in information retrieval are based on hardware constraints. We begin
More informationIndex construction CE-324: Modern Information Retrieval Sharif University of Technology
Index construction CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2017 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford) Ch.
More informationIntroduction to. CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan. Lecture 4: Index Construction
Introduction to Information Retrieval CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 4: Index Construction 1 Plan Last lecture: Dictionary data structures
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval CS276: Information Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Hamid Rastegari Lecture 4: Index Construction Plan Last lecture: Dictionary data structures
More informationInformation Retrieval
Introduction to CS3245 Lecture 5: Index Construction 5 Last Time Dictionary data structures Tolerant retrieval Wildcards Spelling correction Soundex a-hu hy-m n-z $m mace madden mo among amortize on abandon
More informationCourse work. Today. Last lecture index construc)on. Why compression (in general)? Why compression for inverted indexes?
Course work Introduc)on to Informa(on Retrieval Problem set 1 due Thursday Programming exercise 1 will be handed out today CS276: Informa)on Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan
More informationCS60092: Informa0on Retrieval
Introduc)on to CS60092: Informa0on Retrieval Sourangshu Bha1acharya Last lecture index construc)on Sort- based indexing Naïve in- memory inversion Blocked Sort- Based Indexing Merge sort is effec)ve for
More informationIndex Construction. Slides by Manning, Raghavan, Schutze
Introduction to Information Retrieval ΕΠΛ660 Ανάκτηση Πληροφοριών και Μηχανές Αναζήτησης ης Index Construction ti Introduction to Information Retrieval Plan Last lecture: Dictionary data structures Tolerant
More informationInformation Retrieval
Introduction to CS3245 Lecture 5: Index Construction 5 CS3245 Last Time Dictionary data structures Tolerant retrieval Wildcards Spelling correction Soundex a-hu hy-m n-z $m mace madden mo among amortize
More informationCSCI 5417 Information Retrieval Systems Jim Martin!
CSCI 5417 Information Retrieval Systems Jim Martin! Lecture 4 9/1/2011 Today Finish up spelling correction Realistic indexing Block merge Single-pass in memory Distributed indexing Next HW details 1 Query
More informationIntroduction to Information Retrieval (Manning, Raghavan, Schutze)
Introduction to Information Retrieval (Manning, Raghavan, Schutze) Chapter 3 Dictionaries and Tolerant retrieval Chapter 4 Index construction Chapter 5 Index compression Content Dictionary data structures
More informationINDEX CONSTRUCTION 1
1 INDEX CONSTRUCTION PLAN Last lecture: Dictionary data structures Tolerant retrieval Wildcards Spell correction Soundex a-hu hy-m n-z $m mace madden This time: mo among amortize Index construction on
More informationIndex Construction Introduction to Information Retrieval INF 141 Donald J. Patterson
Index Construction Introduction to Information Retrieval INF 141 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Index Construction Overview Introduction Hardware
More informationCS6200 Informa.on Retrieval. David Smith College of Computer and Informa.on Science Northeastern University
CS6200 Informa.on Retrieval David Smith College of Computer and Informa.on Science Northeastern University Indexing Process Indexes Indexes are data structures designed to make search faster Text search
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 4: Index Construction Hinrich Schütze Center for Information and Language Processing, University of Munich 2014-04-16 1/54 Overview
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 4: Index Construction Hinrich Schütze Center for Information and Language Processing, University of Munich 2014-04-16 Schütze:
More informationPV211: Introduction to Information Retrieval
PV211: Introduction to Information Retrieval http://www.fi.muni.cz/~sojka/pv211 IIR 4: Index construction Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk University,
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 4: Index Construction Hinrich Schütze, Christina Lioma Institute for Natural Language Processing, University of Stuttgart 2010-05-04
More informationBuilding an Inverted Index
Building an Inverted Index Algorithms Memory-based Disk-based (Sort-Inversion) Sorting Merging (2-way; multi-way) 2 Memory-based Inverted Index Phase I (parse and read) For each document Identify distinct
More informationCSE 7/5337: Information Retrieval and Web Search Index construction (IIR 4)
CSE 7/5337: Information Retrieval and Web Search Index construction (IIR 4) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Schütze Institute for Natural
More informationReuters collection example (approximate # s)
BSBI Reuters collection example (approximate # s) 800,000 documents from the Reuters news feed 200 terms per document 400,000 unique terms number of postings 100,000,000 BSBI Reuters collection example
More informationInforma(on Retrieval
Introduc)on to Informa)on Retrieval CS3245 Informa(on Retrieval Lecture 7: Scoring, Term Weigh9ng and the Vector Space Model 7 Last Time: Index Construc9on Sort- based indexing Blocked Sort- Based Indexing
More informationIndex Construction Introduction to Information Retrieval INF 141/ CS 121 Donald J. Patterson
Index Construction Introduction to Information Retrieval INF 141/ CS 121 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Index Construction Overview Introduction
More informationSearch Engines. Informa1on Retrieval in Prac1ce. Annotations by Michael L. Nelson
Search Engines Informa1on Retrieval in Prac1ce Annotations by Michael L. Nelson All slides Addison Wesley, 2008 Indexes Indexes are data structures designed to make search faster Text search has unique
More informationIndex Construction. Dictionary, postings, scalable indexing, dynamic indexing. Web Search
Index Construction Dictionary, postings, scalable indexing, dynamic indexing Web Search 1 Overview Indexes Query Indexing Ranking Results Application Documents User Information analysis Query processing
More informationInformation Retrieval
Information Retrieval Suan Lee - Information Retrieval - 05 Index Compression 1 05 Index Compression - Information Retrieval - 05 Index Compression 2 Last lecture index construction Sort-based indexing
More informationBehrang Mohit : txt proc! Review. Bag of word view. Document Named
Intro to Text Processing Lecture 9 Behrang Mohit Some ideas and slides in this presenta@on are borrowed from Chris Manning and Dan Jurafsky. Review Bag of word view Document classifica@on Informa@on Extrac@on
More informationLecture 3 Index Construction and Compression. Many thanks to Prabhakar Raghavan for sharing most content from the following slides
Lecture 3 Index Construction and Compression Many thanks to Prabhakar Raghavan for sharing most content from the following slides Recap of the previous lecture Tokenization Term equivalence Skip pointers
More informationInformation Retrieval
Introduction to Information Retrieval CS3245 Information Retrieval Lecture 6: Index Compression 6 Last Time: index construction Sort- based indexing Blocked Sort- Based Indexing Merge sort is effective
More informationGes$one Avanzata dell Informazione Part A Full- Text Informa$on Management. Full- Text Indexing
Ges$one Avanzata dell Informazione Part A Full- Text Informa$on Management Full- Text Indexing Contents } Introduction } Inverted Indices } Construction } Searching 2 GAvI - Full- Text Informa$on Management:
More informationInformation Retrieval. Danushka Bollegala
Information Retrieval Danushka Bollegala Anatomy of a Search Engine Document Indexing Query Processing Search Index Results Ranking 2 Document Processing Format detection Plain text, PDF, PPT, Text extraction
More informationInforma(on Retrieval
Introduc)on to Informa)on Retrieval CS3245 Informa(on Retrieval Lecture 7: Scoring, Term Weigh9ng and the Vector Space Model 7 Last Time: Index Compression Collec9on and vocabulary sta9s9cs: Heaps and
More informationWeb Information Retrieval. Lecture 4 Dictionaries, Index Compression
Web Information Retrieval Lecture 4 Dictionaries, Index Compression Recap: lecture 2,3 Stemming, tokenization etc. Faster postings merges Phrase queries Index construction This lecture Dictionary data
More informationInforma)on Retrieval and Map- Reduce Implementa)ons. Mohammad Amir Sharif PhD Student Center for Advanced Computer Studies
Informa)on Retrieval and Map- Reduce Implementa)ons Mohammad Amir Sharif PhD Student Center for Advanced Computer Studies mas4108@louisiana.edu Map-Reduce: Why? Need to process 100TB datasets On 1 node:
More informationEfficiency. Efficiency: Indexing. Indexing. Efficiency Techniques. Inverted Index. Inverted Index (COSC 488)
Efficiency Efficiency: Indexing (COSC 488) Nazli Goharian nazli@cs.georgetown.edu Difficult to analyze sequential IR algorithms: data and query dependency (query selectivity). O(q(cf max )) -- high estimate-
More informationCS60092: Informa0on Retrieval. Sourangshu Bha<acharya
CS60092: Informa0on Retrieval Sourangshu Bha
More informationAdministrative. Distributed indexing. Index Compression! What I did last summer lunch talks today. Master. Tasks
Administrative Index Compression! n Assignment 1? n Homework 2 out n What I did last summer lunch talks today David Kauchak cs458 Fall 2012 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture5-indexcompression.ppt
More informationIntroduc)on to Informa)on Retrieval. Index Construc.on. Slides by Manning, Raghavan, Schutze
Index Construc.on Slides by Manning, Raghavan, Schutze 1 Plan Last lecture: Dic.onary data structures Tolerant retrieval Wildcards Spell correc.on Soundex a-hu hy-m n-z $m mace madden mo among amortize
More informationTransistor: Digital Building Blocks
Final Exam Review Transistor: Digital Building Blocks Logically, each transistor acts as a switch Combined to implement logic functions (gates) AND, OR, NOT Combined to build higher-level structures Multiplexer,
More informationMain Points. File systems. Storage hardware characteris7cs. File system usage Useful abstrac7ons on top of physical devices
Storage Systems Main Points File systems Useful abstrac7ons on top of physical devices Storage hardware characteris7cs Disks and flash memory File system usage pa@erns File System Abstrac7on File system
More informationData-analysis and Retrieval Boolean retrieval, posting lists and dictionaries
Data-analysis and Retrieval Boolean retrieval, posting lists and dictionaries Hans Philippi (based on the slides from the Stanford course on IR) April 25, 2018 Boolean retrieval, posting lists & dictionaries
More informationCS347. Lecture 2 April 9, Prabhakar Raghavan
CS347 Lecture 2 April 9, 2001 Prabhakar Raghavan Today s topics Inverted index storage Compressing dictionaries into memory Processing Boolean queries Optimizing term processing Skip list encoding Wild-card
More informationToday s topics CS347. Inverted index storage. Inverted index storage. Processing Boolean queries. Lecture 2 April 9, 2001 Prabhakar Raghavan
Today s topics CS347 Lecture 2 April 9, 2001 Prabhakar Raghavan Inverted index storage Compressing dictionaries into memory Processing Boolean queries Optimizing term processing Skip list encoding Wild-card
More informationRecap: lecture 2 CS276A Information Retrieval
Recap: lecture 2 CS276A Information Retrieval Stemming, tokenization etc. Faster postings merges Phrase queries Lecture 3 This lecture Index compression Space estimation Corpus size for estimates Consider
More informationChapter 12: Query Processing
Chapter 12: Query Processing Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Basic Steps in Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation 12.2
More informationAnalyzing the performance of top-k retrieval algorithms. Marcus Fontoura Google, Inc
Analyzing the performance of top-k retrieval algorithms Marcus Fontoura Google, Inc This talk Largely based on the paper Evaluation Strategies for Top-k Queries over Memory-Resident Inverted Indices, VLDB
More informationInforma(on Retrieval. Administra*ve. Sta*s*cal MT Overview. Problems for Sta*s*cal MT
Administra*ve Introduc*on to Informa(on Retrieval CS457 Fall 2011! David Kauchak Projects Status 2 on Friday Paper next Friday work on the paper in parallel if you re not done with experiments by early
More informationRecall from Tuesday. Our solution to fragmentation is to split up a process s address space into smaller chunks. Physical Memory OS.
Paging 11/10/16 Recall from Tuesday Our solution to fragmentation is to split up a process s address space into smaller chunks. Physical Memory OS Process 3 Process 3 OS: Place Process 3 Process 1 Process
More informationInforma(on Retrieval
Introduc)on to Informa(on Retrieval cs160 Introduction David Kauchak adapted from: h6p://www.stanford.edu/class/cs276/handouts/lecture1 intro.ppt Introduc)ons Name/nickname Dept., college and year One
More informationECE331: Hardware Organization and Design
ECE331: Hardware Organization and Design Lecture 29: an Introduction to Virtual Memory Adapted from Computer Organization and Design, Patterson & Hennessy, UCB Overview Virtual memory used to protect applications
More informationIndex Compression. David Kauchak cs160 Fall 2009 adapted from:
Index Compression David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture5-indexcompression.ppt Administrative Homework 2 Assignment 1 Assignment 2 Pair programming?
More informationPart 2: Boolean Retrieval Francesco Ricci
Part 2: Boolean Retrieval Francesco Ricci Most of these slides comes from the course: Information Retrieval and Web Search, Christopher Manning and Prabhakar Raghavan Content p Term document matrix p Information
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 5: Index Compression Hinrich Schütze Center for Information and Language Processing, University of Munich 2014-04-17 1/59 Overview
More informationDeveloping MapReduce Programs
Cloud Computing Developing MapReduce Programs Dell Zhang Birkbeck, University of London 2017/18 MapReduce Algorithm Design MapReduce: Recap Programmers must specify two functions: map (k, v) * Takes
More informationAlgorithms Lecture 11. UC Davis, ECS20, Winter Discrete Mathematics for Computer Science
UC Davis, ECS20, Winter 2017 Discrete Mathematics for Computer Science Prof. Raissa D Souza (slides adopted from Michael Frank and Haluk Bingöl) Lecture 11 Algorithms 3.1-3.2 Algorithms Member of the House
More informationAdvanced Database Systems
Lecture IV Query Processing Kyumars Sheykh Esmaili Basic Steps in Query Processing 2 Query Optimization Many equivalent execution plans Choosing the best one Based on Heuristics, Cost Will be discussed
More informationEECS 395/495 Lecture 3 Scalable Indexing, Searching, and Crawling
EECS 395/495 Lecture 3 Scalable Indexing, Searching, and Crawling Doug Downey Based partially on slides by Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze Announcements Project progress report
More informationThe Right Read Optimization is Actually Write Optimization. Leif Walsh
The Right Read Optimization is Actually Write Optimization Leif Walsh leif@tokutek.com The Right Read Optimization is Write Optimization Situation: I have some data. I want to learn things about the world,
More informationMap-Reduce. Marco Mura 2010 March, 31th
Map-Reduce Marco Mura (mura@di.unipi.it) 2010 March, 31th This paper is a note from the 2009-2010 course Strumenti di programmazione per sistemi paralleli e distribuiti and it s based by the lessons of
More informationMain Points. File systems. Storage hardware characteris7cs. File system usage Useful abstrac7ons on top of physical devices
Storage Systems Main Points File systems Useful abstrac7ons on top of physical devices Storage hardware characteris7cs Disks and flash memory File system usage pa@erns File Systems Abstrac7on on top of
More informationDocument Representation : Quiz
Document Representation : Quiz Q1. In-memory Index construction faces following problems:. (A) Scaling problem (B) The optimal use of Hardware resources for scaling (C) Easily keep entire data into main
More informationECS 165B: Database System Implementa6on Lecture 3
ECS 165B: Database System Implementa6on Lecture 3 UC Davis April 4, 2011 Acknowledgements: some slides based on earlier ones by Raghu Ramakrishnan, Johannes Gehrke, Jennifer Widom, Bertram Ludaescher,
More informationQuery and Join Op/miza/on 11/5
Query and Join Op/miza/on 11/5 Overview Recap of Merge Join Op/miza/on Logical Op/miza/on Histograms (How Es/mates Work. Big problem!) Physical Op/mizer (if we have /me) Recap on Merge Key (Simple) Idea
More informationChapter 12: Query Processing. Chapter 12: Query Processing
Chapter 12: Query Processing Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 12: Query Processing Overview Measures of Query Cost Selection Operation Sorting Join
More informationWhy Sort? Data requested in sorted order. Sor,ng is first step in bulk loading B+ tree index. e.g., find students in increasing GPA order
External Sor,ng Outline Exam will be graded a5er everyone takes it There are two,mes to be fair on an exam, when it s wriaen and when it s graded you only need to trust that I ll be fair at one of them.
More informationCS 61C: Great Ideas in Computer Architecture Direct- Mapped Caches. Increasing distance from processor, decreasing speed.
CS 6C: Great Ideas in Computer Architecture Direct- Mapped s 9/27/2 Instructors: Krste Asanovic, Randy H Katz hdp://insteecsberkeleyedu/~cs6c/fa2 Fall 2 - - Lecture #4 New- School Machine Structures (It
More informationNOTE: sorting using B-trees to be assigned for reading after we cover B-trees.
External Sorting Chapter 13 (Sec. 13-1-13.5): Ramakrishnan & Gehrke and Chapter 11 (Sec. 11.4-11.5): G-M et al. (R2) OR Chapter 2 (Sec. 2.4-2.5): Garcia-et Molina al. (R1) NOTE: sorting using B-trees to
More informationLecture 8: Memory Management
Lecture 8: Memory Management CSE 120: Principles of Opera>ng Systems UC San Diego: Summer Session I, 2009 Frank Uyeda Announcements PeerWise ques>ons due tomorrow. Project 2 is due on Friday. Milestone
More informationQuery Evaluation Strategies
Introduction to Search Engine Technology Term-at-a-Time and Document-at-a-Time Evaluation Ronny Lempel Yahoo! Labs (Many of the following slides are courtesy of Aya Soffer and David Carmel, IBM Haifa Research
More informationLecture 5: Information Retrieval using the Vector Space Model
Lecture 5: Information Retrieval using the Vector Space Model Trevor Cohn (tcohn@unimelb.edu.au) Slide credits: William Webber COMP90042, 2015, Semester 1 What we ll learn today How to take a user query
More informationInforma/on Retrieval. Text Search. CISC437/637, Lecture #23 Ben CartereAe. Consider a database consis/ng of long textual informa/on fields
Informa/on Retrieval CISC437/637, Lecture #23 Ben CartereAe Copyright Ben CartereAe 1 Text Search Consider a database consis/ng of long textual informa/on fields News ar/cles, patents, web pages, books,
More informationCMSC424: Database Design. Instructor: Amol Deshpande
CMSC424: Database Design Instructor: Amol Deshpande amol@cs.umd.edu Databases Data Models Conceptual representa1on of the data Data Retrieval How to ask ques1ons of the database How to answer those ques1ons
More informationCPSC 330 Computer Organization
CPSC 33 Computer Organization Lecture 7c Memory Adapted from CS52, CS 6C and notes by Kevin Peterson and Morgan Kaufmann Publishers, Copyright 24. Improving cache performance Two ways of improving performance:
More informationInformation Retrieval II
Information Retrieval II David Hawking 30 Sep 2010 Machine Learning Summer School, ANU Session Outline Ranking documents in response to a query Measuring the quality of such rankings Case Study: Tuning
More informationInformation Retrieval
Introduction to Information Retrieval Boolean retrieval Basic assumptions of Information Retrieval Collection: Fixed set of documents Goal: Retrieve documents with information that is relevant to the user
More informationCS6200 Information Retrieval. David Smith College of Computer and Information Science Northeastern University
CS6200 Information Retrieval David Smith College of Computer and Information Science Northeastern University Indexing Process!2 Indexes Storing document information for faster queries Indexes Index Compression
More informationText Analytics. Index-Structures for Information Retrieval. Ulf Leser
Text Analytics Index-Structures for Information Retrieval Ulf Leser Content of this Lecture Inverted files Storage structures Phrase and proximity search Building and updating the index Using a RDBMS Ulf
More informationComputer Systems C S Cynthia Lee Today s materials adapted from Kevin Webb at Swarthmore College
Computer Systems C S 0 7 Cynthia Lee Today s materials adapted from Kevin Webb at Swarthmore College 2 Today s Topics TODAY S LECTURE: Caching ANNOUNCEMENTS: Assign6 & Assign7 due Friday! 6 & 7 NO late
More informationDistributed computing: index building and use
Distributed computing: index building and use Distributed computing Goals Distributing computation across several machines to Do one computation faster - latency Do more computations in given time - throughput
More informationINFO 4300 / CS4300 Information Retrieval. slides adapted from Hinrich Schütze s, linked from
INFO 4300 / CS4300 Information Retrieval slides adapted from Hinrich Schütze s, linked from http://informationretrieval.org/ IR 6: Index Compression Paul Ginsparg Cornell University, Ithaca, NY 15 Sep
More informationMore on indexing CE-324: Modern Information Retrieval Sharif University of Technology
More on indexing CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford) Plan
More informationIndexing. UCSB 290N. Mainly based on slides from the text books of Croft/Metzler/Strohman and Manning/Raghavan/Schutze
Indexing UCSB 290N. Mainly based on slides from the text books of Croft/Metzler/Strohman and Manning/Raghavan/Schutze All slides Addison Wesley, 2008 Table of Content Inverted index with positional information
More informationIntroduction to Database Systems CSE 444, Winter 2011
Version March 15, 2011 Introduction to Database Systems CSE 444, Winter 2011 Lecture 20: Operator Algorithms Where we are / and where we go 2 Why Learn About Operator Algorithms? Implemented in commercial
More informationEmbedded Systems Dr. Santanu Chaudhury Department of Electrical Engineering Indian Institute of Technology, Delhi
Embedded Systems Dr. Santanu Chaudhury Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 13 Virtual memory and memory management unit In the last class, we had discussed
More informationMemory Management. Kevin Webb Swarthmore College February 27, 2018
Memory Management Kevin Webb Swarthmore College February 27, 2018 Today s Goals Shifting topics: different process resource memory Motivate virtual memory, including what it might look like without it
More informationCS160 - Assignment 2 Due: Friday Sept. 25, 6pm
CS160 - Assignment 2 Due: Friday Sept. 25, 6pm For the next step in our IR system we re going to be adding functionality to do boolean queries. For our purposes a boolean query consists of an expression.
More informationQuery Evaluation Strategies
Introduction to Search Engine Technology Term-at-a-Time and Document-at-a-Time Evaluation Ronny Lempel Yahoo! Research (Many of the following slides are courtesy of Aya Soffer and David Carmel, IBM Haifa
More informationMul$media Techniques in Android. Some of the informa$on in this sec$on is adapted from WiseAndroid.com
Mul$media Techniques in Android Some of the informa$on in this sec$on is adapted from WiseAndroid.com Mul$media Support Android provides comprehensive mul$media func$onality: Audio: all standard formats
More informationIndexing. CS6200: Information Retrieval. Index Construction. Slides by: Jesse Anderton
Indexing Index Construction CS6200: Information Retrieval Slides by: Jesse Anderton Motivation: Scale Corpus Terms Docs Entries A term incidence matrix with V terms and D documents has O(V x D) entries.
More information