The Social Network of Java Classes Power-law for Dummies
|
|
|
- Estella Reed
- 5 years ago
- Views:
Transcription
1 The Social Network of Java Classes Power-law for Dummies Diego Puppin Institute for Information Sciences and Technology Pisa, Italy March 24, 2006 Diego Puppin (ISTI-CNR) LabDay March 24, / 41
2 Outline Today s Menu 1 Today s Menu 2 Appetizer: Choice of Mixed Topics 3 First Courses Social Networks and Power Law Java Software Engineering 4 Second Course: Salad of Good Ideas and Boiled Theories From Here to Ranking Software Experiments 5... the bill, please! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
3 Today s Menu Why in English? Anybody speaking good French or German? Are these slides going to be on-line somewhere/someday? I need to get ready for SAC 2006 :-) Diego Puppin (ISTI-CNR) LabDay March 24, / 41
4 Today s Menu Quick Check-pointing Document-partitioning using the query logs Peer-to-peer discovery systems Scheduling on SUN machines Pretty sweet languages Diego Puppin (ISTI-CNR) LabDay March 24, / 41
5 ...and then... Today s Menu Hold on! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
6 ...and then... Today s Menu Hold on! Awesome findings on power laws and Java! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
7 Outline Appetizer: Choice of Mixed Topics 1 Today s Menu 2 Appetizer: Choice of Mixed Topics 3 First Courses Social Networks and Power Law Java Software Engineering 4 Second Course: Salad of Good Ideas and Boiled Theories From Here to Ranking Software Experiments 5... the bill, please! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
8 Appetizer: Choice of Mixed Topics Document-partitioning using the query logs WHY: Started as a problem related to the scalability of the discovery system WHAT FOR: General applicability to Web search engine, Big research potential WHAT: We identified a novel approach to partition documents using query logs, so to minimize the number of queried partitions To be presented at INFOSCALE 2006, raised the interest of Prof. Ricard Baeza-Yates Diego Puppin (ISTI-CNR) LabDay March 24, / 41
9 Appetizer: Choice of Mixed Topics Document-partitioning using the query logs WHY: Started as a problem related to the scalability of the discovery system WHAT FOR: General applicability to Web search engine, Big research potential WHAT: We identified a novel approach to partition documents using query logs, so to minimize the number of queried partitions To be presented at INFOSCALE 2006, raised the interest of Prof. Ricard Baeza-Yates and of Dr. Fabrizio Silvestri... I almost forgot Diego Puppin (ISTI-CNR) LabDay March 24, / 41
10 Appetizer: Choice of Mixed Topics Peer-to-peer discovery systems WHAT FOR: In the context of XtreemOS WHAT: Distributed solutions for an information service We have one tirocinio going on (Thx Hanien) Part of the syllabus of the class Topic in Grid Computing at the Indian Institute of Science, Bangalore: vss/courses/gc2005/ Diego Puppin (ISTI-CNR) LabDay March 24, / 41
11 Appetizer: Choice of Mixed Topics Scheduling on SUN machines WHY: My MS thesis at MIT does not want to die WHAT FOR: Maybe we can show it off to SUN WHAT: So far, we have one student tirocinio and one student thesis, plus Marco Pasquali Diego Puppin (ISTI-CNR) LabDay March 24, / 41
12 Sweet sausages Appetizer: Choice of Mixed Topics Diego Puppin (ISTI-CNR) LabDay March 24, / 41
13 Sweet sausages Appetizer: Choice of Mixed Topics I meant... languages. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
14 Appetizer: Choice of Mixed Topics Sweet sausages I meant... languages. WHY: different ways of doing components WHAT: I studied Aspect Programming and Design-by-Contract Along the way, Python and Ruby (pretty sweet) WHAT FOR: Diego Puppin (ISTI-CNR) LabDay March 24, / 41
15 Appetizer: Choice of Mixed Topics Sweet sausages I meant... languages. WHY: different ways of doing components WHAT: I studied Aspect Programming and Design-by-Contract Along the way, Python and Ruby (pretty sweet) WHAT FOR: When all you have got is a hammer, everything looks like a nail Diego Puppin (ISTI-CNR) LabDay March 24, / 41
16 Appetizer: Choice of Mixed Topics If you want a full serving of these appetizers... just ask! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
17 Outline First Courses 1 Today s Menu 2 Appetizer: Choice of Mixed Topics 3 First Courses Social Networks and Power Law Java Software Engineering 4 Second Course: Salad of Good Ideas and Boiled Theories From Here to Ranking Software Experiments 5... the bill, please! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
18 First Courses Social Networks and Power Law Social Networks Behavior emerging from linked entities Ranging from biology, to computer science, to economics First studies in late 1930s Then, studies on random graph by Erdos Recently, good summary by Barabazi Diego Puppin (ISTI-CNR) LabDay March 24, / 41
19 First Courses Social Networks and Power Law Emerging Behavior Rich Web pages getting richer Stocks raising or falling quickly Ants building nests Diego Puppin (ISTI-CNR) LabDay March 24, / 41
20 First Courses Social Networks and Power Law Emerging Behavior Rich Web pages getting richer Stocks raising or falling quickly Ants building nests Traffic jams Diego Puppin (ISTI-CNR) LabDay March 24, / 41
21 First Courses Social Networks and Power Law Emerging Behavior Rich Web pages getting richer Stocks raising or falling quickly Ants building nests Traffic jams When counting links, visitors, references in a natural network, large events are rare, but small ones are quite common. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
22 First Courses Social Networks and Power Law Zipf, Pareto, Power Law Zipf s law: the size y of an occurrence of an event is inversely proportional to its rank : y r b, with b close to unity. Pareto: the number of events larger than x is an inverse power of x: P[X > x] x k. Power law: the number of events equal to x: P[X = x] x (k+1) = x a. Also called scale-free distribution, because the distribution doesn t change with scale. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
23 First Courses Social Networks and Power Law Diego Puppin (ISTI-CNR) LabDay March 24, / 41
24 First Courses Social Networks and Power Law...may I have some more? Read Barabasi s Linked (in our library). Seminal paper by Faloutsos (x3) (1999). Diego Puppin (ISTI-CNR) LabDay March 24, / 41
25 First Courses Java Software Engineering Power-Law from Engineering Choices From Power Law Distribution in Class Relationships. Interesting power-laws were found in the design of the Java Development Kit. Coherent SW engineering choices (modularity, orthogonality, interface contracts) lead to a scale-free network with power-law distributions: number of sub-classes, number of fields, number of methods, number of constructors. Also in Scale-free Networks from Optimal Design. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
26 First Courses Java Software Engineering Diego Puppin (ISTI-CNR) LabDay March 24, / 41
27 First Courses Java Software Engineering Power-Law from Social Behavior We want to study the behavior that is naturally emerging from the social network, at the level of static references. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
28 First Courses Java Software Engineering Scale Free Geometry in OO Programs Authors get a snapshot of the memory at run time They counts the number of references (link) among objects Number of inlinks and outlinks are distributed with power law Diego Puppin (ISTI-CNR) LabDay March 24, / 41
29 First Courses Java Software Engineering Scale Free Geometry in OO Programs Diego Puppin (ISTI-CNR) LabDay March 24, / 41
30 First Courses Java Software Engineering Scale Free Geometry in OO Programs No typical size of an object Few objects are very important Not CLASS, but object Interesting, but not useful for developers Dynamic analysis of code: Interesting results from dynamic analysis (profiling, optimization) [GGMS03, vdab03] BUT dynamic data can be commercial secrets Diego Puppin (ISTI-CNR) LabDay March 24, / 41
31 Good for ACM First Courses Java Software Engineering Clustering Object-Oriented Software Systems using Spectral Graph Partitioning, by Spiros Xanthos, second place at the 2005 ACM Student Research Competition. Diego Puppin (ISTI-CNR) LabDay March 24, / 41
32 Outline Ranking 1 Today s Menu 2 Appetizer: Choice of Mixed Topics 3 First Courses Social Networks and Power Law Java Software Engineering 4 Second Course: Salad of Good Ideas and Boiled Theories From Here to Ranking Software Experiments 5... the bill, please! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
33 Ranking From Here to Ranking Software How to Rank Software Similar to Google PageRank Static analysis of code links INTERFACES ONLY Every time a class is used, there is a rank boost No source code is needed No runtime information is needed Only public interfaces Diego Puppin (ISTI-CNR) LabDay March 24, / 41
34 Ranking From Here to Ranking Software Why only interfaces? Commercial component will hide source code Will also hide runtime profile information Interfaces must be public, in order to use a component We suggest to base ranking on this A composed application should also make public the composition structure use/provide ports Diego Puppin (ISTI-CNR) LabDay March 24, / 41
35 Ranking From Here to Ranking Software Why composition should be public? To improve trust To become more popular To support a standard Open Source... Compare with digital libraries bib. references VS full-text Diego Puppin (ISTI-CNR) LabDay March 24, / 41
36 Ranking From Here to Ranking Software Class Graph: Static vs Dynamic Abstract static information Progr. Interface:Class Rank Executable information Program Code:Comp. Rank Dynamic information Process:Scale Free Diego Puppin (ISTI-CNR) LabDay March 24, / 41
37 A Model for Ranking Ranking From Here to Ranking Software Assumptions: Avoid ontologies An ontology for the whole Grid? Heavy emphasis on LINKS Positive experience on the Web No use of source code and dynamic data Hidden in commercial apps Use only public interfaces Maybe semantic info can be used Sub/super-types... Diego Puppin (ISTI-CNR) LabDay March 24, / 41
38 Ranking Experiments Initial Experiments Java classes, simple composition model Strong documentation (Java Docs) Unique ID (Package names) Class links are very easy to see We collected classes We parsed and built a social graph Diego Puppin (ISTI-CNR) LabDay March 24, / 41
39 Ranking Experiments Social Network in Java Classes Java programmer component user S/he chooses most general and useful classes Power-law behavior Web pages, blogs, social networks sociali etc see On Power-Law Relationships of the Internet Topology, by Faloutsos et al. component usage Web linking!!! Component search Web Search Diego Puppin (ISTI-CNR) LabDay March 24, / 41
40 Ranking Experiments Diego Puppin (ISTI-CNR) LabDay March 24, / 41
41 Ranking Experiments Diego Puppin (ISTI-CNR) LabDay March 24, / 41
42 Class Rank Ranking Experiments To determine the rank of a class C, we iterate the following formula: rank C = λ + (1 λ) i inlinks C rank i #outlinks i where inlinks C is the set of classes that use C (with a link into C), #outlinks i is the number of classes used by i (number of links out of i), and λ a small factor, usually around Diego Puppin (ISTI-CNR) LabDay March 24, / 41
43 Ranking Experiments Top-ranking classes String, Object, Class, Exception #7: Apache MessageResources #11: Tomcat CharChunk #14: DBXML Value #73: JXTA ID Diego Puppin (ISTI-CNR) LabDay March 24, / 41
44 Ranking Experiments GRIDLE 0.1 Ranking using two metrics: TF.IDF (term frequency times inverted document frequency) GRIDLE Rank Bells and whistles: Snippets, Links and Reverse Links Diego Puppin (ISTI-CNR) LabDay March 24, / 41
45 Ranking Experiments A search engine for SW components GRIDLE: Google-like Ranking, Indexing and Discovery service for a Link-based Eco-system of software components Diego Puppin (ISTI-CNR) LabDay March 24, / 41
46 Ranking Experiments Diego Puppin (ISTI-CNR) LabDay March 24, / 41
47 Outline... the bill, please! 1 Today s Menu 2 Appetizer: Choice of Mixed Topics 3 First Courses Social Networks and Power Law Java Software Engineering 4 Second Course: Salad of Good Ideas and Boiled Theories From Here to Ranking Software Experiments 5... the bill, please! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
48 ... the bill, please! Conclusions Usage of Java classes follows the pattern of links among Web pages We can rank classes according to this finding...towards a search engine for software Diego Puppin (ISTI-CNR) LabDay March 24, / 41
49 ... the bill, please!...going to SAC 2006! Please, give me feedback!!! Thanks for coming!! Diego Puppin (ISTI-CNR) LabDay March 24, / 41
50 ... the bill, please! Acknowledgments MIUR CNR Strategic Project L 499/ (5%) NextGrid CoreGRID Università degli Studi di Pisa ISTI-CNR Diego Puppin (ISTI-CNR) LabDay March 24, / 41
Query-Driven Document Partitioning and Collection Selection
Query-Driven Document Partitioning and Collection Selection Diego Puppin, Fabrizio Silvestri and Domenico Laforenza Institute for Information Science and Technologies ISTI-CNR Pisa, Italy May 31, 2006
SIGIR Workshop Report. The SIGIR Heterogeneous and Distributed Information Retrieval Workshop
SIGIR Workshop Report The SIGIR Heterogeneous and Distributed Information Retrieval Workshop Ranieri Baraglia HPC-Lab ISTI-CNR, Italy ranieri.baraglia@isti.cnr.it Fabrizio Silvestri HPC-Lab ISTI-CNR, Italy
CC PROCESAMIENTO MASIVO DE DATOS OTOÑO Lecture 7: Information Retrieval II. Aidan Hogan
CC5212-1 PROCESAMIENTO MASIVO DE DATOS OTOÑO 2017 Lecture 7: Information Retrieval II Aidan Hogan aidhog@gmail.com How does Google know about the Web? Inverted Index: Example 1 Fruitvale Station is a 2013
Clustering (COSC 488) Nazli Goharian. Document Clustering.
Clustering (COSC 488) Nazli Goharian nazli@ir.cs.georgetown.edu 1 Document Clustering. Cluster Hypothesis : By clustering, documents relevant to the same topics tend to be grouped together. C. J. van Rijsbergen,
Big Data Analytics: What is Big Data? Stony Brook University CSE545, Fall 2016 the inaugural edition
Big Data Analytics: What is Big Data? Stony Brook University CSE545, Fall 2016 the inaugural edition What s the BIG deal?! 2011 2011 2008 2010 2012 What s the BIG deal?! (Gartner Hype Cycle) What s the
Text Analytics (Text Mining)
CSE 6242 / CX 4242 Apr 1, 2014 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer,
Information Retrieval
Multimedia Computing: Algorithms, Systems, and Applications: Information Retrieval and Search Engine By Dr. Yu Cao Department of Computer Science The University of Massachusetts Lowell Lowell, MA 01854,
Databases 2 (VU) ( / )
Databases 2 (VU) (706.711 / 707.030) MapReduce (Part 3) Mark Kröll ISDS, TU Graz Nov. 27, 2017 Mark Kröll (ISDS, TU Graz) MapReduce Nov. 27, 2017 1 / 42 Outline 1 Problems Suited for Map-Reduce 2 MapReduce:
Overlay (and P2P) Networks
Overlay (and P2P) Networks Part II Recap (Small World, Erdös Rényi model, Duncan Watts Model) Graph Properties Scale Free Networks Preferential Attachment Evolving Copying Navigation in Small World Samu
UNIVERSITA DEGLI STUDI DI CATANIA FACOLTA DI INGEGNERIA
UNIVERSITA DEGLI STUDI DI CATANIA FACOLTA DI INGEGNERIA PhD course in Electronics, Automation and Complex Systems Control-XXIV Cycle DIPARTIMENTO DI INGEGNERIA ELETTRICA ELETTRONICA E DEI SISTEMI ing.
SOURCERER: MINING AND SEARCHING INTERNET- SCALE SOFTWARE REPOSITORIES
SOURCERER: MINING AND SEARCHING INTERNET- SCALE SOFTWARE REPOSITORIES Introduction to Information Retrieval CS 150 Donald J. Patterson This content based on the paper located here: http://dx.doi.org/10.1007/s10618-008-0118-x
CS473: Course Review CS-473. Luo Si Department of Computer Science Purdue University
CS473: CS-473 Course Review Luo Si Department of Computer Science Purdue University Basic Concepts of IR: Outline Basic Concepts of Information Retrieval: Task definition of Ad-hoc IR Terminologies and
Text Analytics (Text Mining)
CSE 6242 / CX 4242 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko,
Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.
Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How
Chapter 2. Architecture of a Search Engine
Chapter 2 Architecture of a Search Engine Search Engine Architecture A software architecture consists of software components, the interfaces provided by those components and the relationships between them
Information Retrieval CSCI
Information Retrieval CSCI 4141-6403 My name is Anwar Alhenshiri My email is: anwar@cs.dal.ca I prefer: aalhenshiri@gmail.com The course website is: http://web.cs.dal.ca/~anwar/ir/main.html 5/6/2012 1
Contextual Information Retrieval Using Ontology-Based User Profiles
Contextual Information Retrieval Using Ontology-Based User Profiles Vishnu Kanth Reddy Challam Master s Thesis Defense Date: Jan 22 nd, 2004. Committee Dr. Susan Gauch(Chair) Dr.David Andrews Dr. Jerzy
CSE 494 Project C. Garrett Wolf
CSE 494 Project C Garrett Wolf Introduction The main purpose of this project task was for us to implement the simple k-means and buckshot clustering algorithms. Once implemented, we were asked to vary
Automatic Identification of User Goals in Web Search [WWW 05]
![Automatic Identification of User Goals in Web Search [WWW 05]](/thumbs/87/96071045.jpg "Automatic Identification of User Goals in Web Search [WWW 05]")
Automatic Identification of User Goals in Web Search [WWW 05] UichinLee @ UCLA ZhenyuLiu @ UCLA JunghooCho @ UCLA Presenter: Emiran Curtmola@ UC San Diego CSE 291 4/29/2008 Need to improve the quality
Lecture 1: January 22
CMPSCI 677 Distributed and Operating Systems Spring 2018 Lecture 1: January 22 Lecturer: Prashant Shenoy Scribe: Bin Wang 1.1 Introduction to the course The lecture started by outlining the administrative
How Do Real Networks Look? Networked Life NETS 112 Fall 2014 Prof. Michael Kearns
How Do Real Networks Look? Networked Life NETS 112 Fall 2014 Prof. Michael Kearns Roadmap Next several lectures: universal structural properties of networks Each large-scale network is unique microscopically,
Information Retrieval
Natural Language Processing SoSe 2014 Information Retrieval Dr. Mariana Neves June 18th, 2014 (based on the slides of Dr. Saeedeh Momtazi) Outline Introduction Indexing Block 2 Document Crawling Text Processing
Clustering (COSC 416) Nazli Goharian. Document Clustering.
Clustering (COSC 416) Nazli Goharian nazli@cs.georgetown.edu 1 Document Clustering. Cluster Hypothesis : By clustering, documents relevant to the same topics tend to be grouped together. C. J. van Rijsbergen,
An Architectural Approach to Inter-domain Measurement Data Sharing
An Architectural Approach to Inter-domain Measurement Data Sharing Brian Trammell Communication Systems Group, ETH Zürich DUST 2012, San Diego, 15 May 2012 Questions and Wishes common platform for darkspace
Part I: Data Mining Foundations
Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web and the Internet 2 1.3. Web Data Mining 4 1.3.1. What is Data Mining? 6 1.3.2. What is Web Mining?
Search Computing: Business Areas, Research and Socio-Economic Challenges
Search Computing: Business Areas, Research and Socio-Economic Challenges Yiannis Kompatsiaris, Spiros Nikolopoulos, CERTH--ITI NEM SUMMIT Torino-Italy, 28th September 2011 Media Search Cluster Search Computing
Information Retrieval
Natural Language Processing SoSe 2015 Information Retrieval Dr. Mariana Neves June 22nd, 2015 (based on the slides of Dr. Saeedeh Momtazi) Outline Introduction Indexing Block 2 Document Crawling Text Processing
Object Model. Object Orientated Analysis and Design. Benjamin Kenwright
Object Model Object Orientated Analysis and Design Benjamin Kenwright Outline Submissions/Quizzes Review Object Orientated Programming Concepts (e.g., encapsulation, data abstraction,..) What do we mean
Inverted Index for Fast Nearest Neighbour
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IMPACT FACTOR: 5.258 IJCSMC,
VK Multimedia Information Systems
VK Multimedia Information Systems Mathias Lux, mlux@itec.uni-klu.ac.at This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Results Exercise 01 Exercise 02 Retrieval
Midterm Exam Search Engines ( / ) October 20, 2015
Student Name: Andrew ID: Seat Number: Midterm Exam Search Engines (11-442 / 11-642) October 20, 2015 Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points
Web Security Vulnerabilities: Challenges and Solutions
Web Security Vulnerabilities: Challenges and Solutions A Tutorial Proposal for ACM SAC 2018 by Dr. Hossain Shahriar Department of Information Technology Kennesaw State University Kennesaw, GA 30144, USA
Lecture 1: January 23
CMPSCI 677 Distributed and Operating Systems Spring 2019 Lecture 1: January 23 Lecturer: Prashant Shenoy Scribe: Jonathan Westin (2019), Bin Wang (2018) 1.1 Introduction to the course The lecture started
Space Details. Available Pages
Key: Space Details extremescale Name: WebSphere extreme Scale and DataPower XC10 Appliance Wiki Description: Creator (Creation Date): dwblogadmin (Apr 09, 2009) Last Modifier (Mod. Date): carriemiller
In = number of words appearing exactly n times N = number of words in the collection of words A = a constant. For example, if N=100 and the most
In = number of words appearing exactly n times N = number of words in the collection of words A = a constant. For example, if N=100 and the most common word appears 10 times then A = rn*n/n = 1*10/100
Information Retrieval (IR) Introduction to Information Retrieval. Lecture Overview. Why do we need IR? Basics of an IR system.
Introduction to Information Retrieval Ethan Phelps-Goodman Some slides taken from http://www.cs.utexas.edu/users/mooney/ir-course/ Information Retrieval (IR) The indexing and retrieval of textual documents.
Modelling data networks research summary and modelling tools
Modelling data networks research summary and modelling tools a 1, 3 1, 2 2, 2 b 0, 3 2, 3 u 1, 3 α 1, 6 c 0, 3 v 2, 2 β 1, 1 Richard G. Clegg (richard@richardclegg.org) December 2011 Available online at
How to explore big networks? Question: Perform a random walk on G. What is the average node degree among visited nodes, if avg degree in G is 200?
How to explore big networks? Question: Perform a random walk on G. What is the average node degree among visited nodes, if avg degree in G is 200? Questions from last time Avg. FB degree is 200 (suppose).
OnCrawl Metrics. What SEO indicators do we analyze for you? Dig into our board of metrics to find the one you are looking for.
1 OnCrawl Metrics What SEO indicators do we analyze for you? Dig into our board of metrics to find the one you are looking for. UNLEASH YOUR SEO POTENTIAL Table of content 01 Crawl Analysis 02 Logs Monitoring
INTRODUCTION TO DATA MINING
INTRODUCTION TO DATA MINING 1 Chiara Renso KDDLab - ISTI CNR, Italy http://www-kdd.isti.cnr.it email: chiara.renso@isti.cnr.it Knowledge Discovery and Data Mining Laboratory, ISTI National Research Council,
A project report submitted to Indiana University
Page Rank Algorithm Using MPI Indiana University, Bloomington Fall-2012 A project report submitted to Indiana University By Shubhada Karavinkoppa and Jayesh Kawli Under supervision of Prof. Judy Qiu 1
Internet Search. (COSC 488) Nazli Goharian Nazli Goharian, 2005, Outline
Internet Search (COSC 488) Nazli Goharian nazli@cs.georgetown.edu Nazli Goharian, 2005, 2012 1 Outline Web: Indexing & Efficiency Partitioned Indexing Index Tiering & other early termination techniques
Image Credit: Photo by Lukas from Pexels
Are you underestimating the importance of Keywords Research In SEO? If yes, then really you are making huge mistakes and missing valuable search engine traffic. Today s SEO world talks about unique content
Feature selection. LING 572 Fei Xia
Feature selection LING 572 Fei Xia 1 Creating attribute-value table x 1 x 2 f 1 f 2 f K y Choose features: Define feature templates Instantiate the feature templates Dimensionality reduction: feature selection
Chuck Cartledge, PhD. 21 January 2018
Big Data: Data Analysis Boot Camp Serial vs. Parallel Processing Chuck Cartledge, PhD 21 January 2018 1/24 Table of contents (1 of 1) 1 Intro. 2 Amdahl A little math 3 BD Processing Programming paradigms
Information Retrieval. hussein suleman uct cs
Information Management Information Retrieval hussein suleman uct cs 303 2004 Introduction Information retrieval is the process of locating the most relevant information to satisfy a specific information
Beyond Ten Blue Links Seven Challenges
Beyond Ten Blue Links Seven Challenges Ricardo Baeza-Yates VP of Yahoo! Research for EMEA & LatAm Barcelona, Spain Thanks to Andrei Broder, Yoelle Maarek & Prabhakar Raghavan Agenda Past and Present Wisdom
ONTOPARK: ONTOLOGY BASED PAGE RANKING FRAMEWORK USING RESOURCE DESCRIPTION FRAMEWORK
Journal of Computer Science 10 (9): 1776-1781, 2014 ISSN: 1549-3636 2014 doi:10.3844/jcssp.2014.1776.1781 Published Online 10 (9) 2014 (http://www.thescipub.com/jcs.toc) ONTOPARK: ONTOLOGY BASED PAGE RANKING
An overview of Graph Categories and Graph Primitives
An overview of Graph Categories and Graph Primitives Dino Ienco (dino.ienco@irstea.fr) https://sites.google.com/site/dinoienco/ Topics I m interested in: Graph Database and Graph Data Mining Social Network
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org Pete Skomoroch Research Scientist at LinkedIn Consultant at Data Wrangling @peteskomoroch 09/29/09 1 Talk Outline TrendingTopics Overview Wikipedia Page
10/10/13. Traditional database system. Information Retrieval. Information Retrieval. Information retrieval system? Information Retrieval Issues
COS 597A: Principles of Database and Information Systems Information Retrieval Traditional database system Large integrated collection of data Uniform access/modifcation mechanisms Model of data organization
Enhancing applications with Cognitive APIs IBM Corporation
Enhancing applications with Cognitive APIs After you complete this section, you should understand: The Watson Developer Cloud offerings and APIs The benefits of commonly used Cognitive services 2 Watson
Models of Network Formation. Networked Life NETS 112 Fall 2017 Prof. Michael Kearns
Models of Network Formation Networked Life NETS 112 Fall 2017 Prof. Michael Kearns Roadmap Recently: typical large-scale social and other networks exhibit: giant component with small diameter sparsity
Syllabus INFO-GB Design and Development of Web and Mobile Applications (Especially for Start Ups)
Syllabus INFO-GB-3322 Design and Development of Web and Mobile Applications (Especially for Start Ups) Fall 2015 Stern School of Business Norman White, KMEC 8-88 Email: nwhite@stern.nyu.edu Phone: 212-998
Disambiguating Search by Leveraging a Social Context Based on the Stream of User s Activity
Disambiguating Search by Leveraging a Social Context Based on the Stream of User s Activity Tomáš Kramár, Michal Barla and Mária Bieliková Faculty of Informatics and Information Technology Slovak University
Semantic Scholar. ICSTI Towards a More Efficient Review of Research Literature 11 September
Semantic Scholar ICSTI Towards a More Efficient Review of Research Literature 11 September 2018 Allen Institute for Artificial Intelligence (https://allenai.org/) Non-profit Research Institute in Seattle,
Map Reduce. Yerevan.
Map Reduce Erasmus+ @ Yerevan dacosta@irit.fr Divide and conquer at PaaS 100 % // Typical problem Iterate over a large number of records Extract something of interest from each Shuffle and sort intermediate
An Exploratory Journey Into Network Analysis A Gentle Introduction to Network Science and Graph Visualization
An Exploratory Journey Into Network Analysis A Gentle Introduction to Network Science and Graph Visualization Pedro Ribeiro (DCC/FCUP & CRACS/INESC-TEC) Part 1 Motivation and emergence of Network Science
Fundamentals of STEP Implementation
Fundamentals of STEP Implementation David Loffredo loffredo@steptools.com STEP Tools, Inc., Rensselaer Technology Park, Troy, New York 12180 A) Introduction The STEP standard documents contain such a large
JBoss World 2009 Aaron Darcy
1 Java Application Platforms for Lightweight Workloads Insight into Red Hat's Application Platform Strategy Aaron Darcy JBoss Product Line Director Red Hat September 2009 2 About Me JBoss Product Line
CS47300 Web Information Search and Management
CS47300 Web Information Search and Management Search Engine Optimization Prof. Chris Clifton 31 October 2018 What is Search Engine Optimization? 90% of search engine clickthroughs are on the first page
Help! My Birthday Reminder Wants to Brick My Phone!
Institut für Technische Informatik und Kommunikationsnetze Master Thesis Help! My Birthday Reminder Wants to Brick My Phone! Student Name Advisor: Dr. Stephan Neuhaus, neuhaust@tik.ee.ethz.ch Professor:
Efficient Diversification of Web Search Results
Efficient Diversification of Web Search Results G. Capannini, F. M. Nardini, R. Perego, and F. Silvestri ISTI-CNR, Pisa, Italy Laboratory Web Search Results Diversification Query: Vinci, what is the user
A Generating Function Approach to Analyze Random Graphs
A Generating Function Approach to Analyze Random Graphs Presented by - Vilas Veeraraghavan Advisor - Dr. Steven Weber Department of Electrical and Computer Engineering Drexel University April 8, 2005 Presentation
Exploiting peer group concept for adaptive and highly available services
Computing in High Energy and Nuclear Physics, 24-28 March 2003 La Jolla California 1 Exploiting peer group concept for adaptive and highly available services Muhammad Asif Jan Centre for European Nuclear
: Semantic Web (2013 Fall)
03-60-569: Web (2013 Fall) University of Windsor September 4, 2013 Table of contents 1 2 3 4 5 Definition of the Web The World Wide Web is a system of interlinked hypertext documents accessed via the Internet
BUBBLE RAP: Social-Based Forwarding in Delay-Tolerant Networks
1 BUBBLE RAP: Social-Based Forwarding in Delay-Tolerant Networks Pan Hui, Jon Crowcroft, Eiko Yoneki Presented By: Shaymaa Khater 2 Outline Introduction. Goals. Data Sets. Community Detection Algorithms
Outline. Possible solutions. The basic problem. How? How? Relevance Feedback, Query Expansion, and Inputs to Ranking Beyond Similarity
Outline Relevance Feedback, Query Expansion, and Inputs to Ranking Beyond Similarity Lecture 10 CS 410/510 Information Retrieval on the Internet Query reformulation Sources of relevance for feedback Using
Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. Springer
Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures Springer Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web
Index 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.
Mobile Device Architecture CS 4720 Mobile Application Development
Mobile Device Architecture Mobile Application Development The Way Back Time When a phone was a phone Plus a story! 2 Oh yes this was a phone The Motorola DynaTAC 8000X 1983 13 x 1.75 x 3.5 2.5 pounds $3,995
Computing Yi Fang, PhD
Computing Yi Fang, PhD Department of Computer Engineering Santa Clara University yfang@scu.edu http://www.cse.scu.edu/~yfang/ 1 This Talk Part I Computing Part II Computing at SCU Part III Data Science
Information Retrieval
Information Retrieval WS 2016 / 2017 Lecture 2, Tuesday October 25 th, 2016 (Ranking, Evaluation) Prof. Dr. Hannah Bast Chair of Algorithms and Data Structures Department of Computer Science University
How to pimp high volume PHP websites. 27. September 2008, PHP conference Barcelona. By Jens Bierkandt
How to pimp high volume PHP websites 27. September 2008, PHP conference Barcelona By Jens Bierkandt 1 About me Jens Bierkandt Working with PHP since 2000 From Germany, living in Spain, speaking English
A project report submitted to Indiana University
Sequential Page Rank Algorithm Indiana University, Bloomington Fall-2012 A project report submitted to Indiana University By Shubhada Karavinkoppa and Jayesh Kawli Under supervision of Prof. Judy Qiu 1
Hyperbolic Geometry of Complex Network Data
Hyperbolic Geometry of Complex Network Data Konstantin Zuev http://www.its.caltech.edu/~zuev/ Joint work with D. Krioukov, M. Boguñá, and G. Bianconi CMX seminar, Caltech May 24, 2017 How do complex networks
Social Information Retrieval
Social Information Retrieval Sebastian Marius Kirsch kirschs@informatik.uni-bonn.de th November 00 Format of this talk about my diploma thesis advised by Prof. Dr. Armin B. Cremers inspired by research
A Primer on Design Patterns
A Primer on Design Patterns First Edition Rahul Batra This book is for sale at http://leanpub.com/aprimerondesignpatterns This version was published on 2016-03-23 This is a Leanpub book. Leanpub empowers
Relevance of a Document to a Query
Relevance of a Document to a Query Computing the relevance of a document to a query has four parts: 1. Computing the significance of a word within document D. 2. Computing the significance of word to document
Computer Science 572 Midterm Prof. Horowitz Thursday, March 8, 2012, 2:00pm 3:00pm
Computer Science 572 Midterm Prof. Horowitz Thursday, March 8, 2012, 2:00pm 3:00pm Name: Student Id Number: 1. This is a closed book exam. 2. Please answer all questions. 3. There are a total of 40 questions.
Introduction to Information Retrieval
Introduction to Information Retrieval (Supplementary Material) Zhou Shuigeng March 23, 2007 Advanced Distributed Computing 1 Text Databases and IR Text databases (document databases) Large collections
CSE373: Data Structures & Algorithms Lecture 23: Course Victory Lap. Kevin Quinn Fall 2015
CSE373: Data Structures & Algorithms Lecture 23: Course Victory Lap Kevin Quinn Fall 2015 Today Rest-of-course logistics: exam, etc. Review of main course themes Course evaluations Thoughtful and constructive
Exam IST 441 Spring 2014
Exam IST 441 Spring 2014 Last name: Student ID: First name: I acknowledge and accept the University Policies and the Course Policies on Academic Integrity This 100 point exam determines 30% of your grade.
Introduction to Networks and Business Intelligence
Introduction to Networks and Business Intelligence Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 16th, 2014 Outline n Network Science A Random History n Network Analysis Network
Historical Text Mining:
Historical Text Mining Historical Text Mining, and Historical Text Mining: Challenges and Opportunities Dr. Robert Sanderson Dept. of Computer Science University of Liverpool azaroth@liv.ac.uk http://www.csc.liv.ac.uk/~azaroth/
The Constellation Project. Andrew W. Nash 14 November 2016
The Constellation Project Andrew W. Nash 14 November 2016 The Constellation Project: Representing a High Performance File System as a Graph for Analysis The Titan supercomputer utilizes high performance
Improving Difficult Queries by Leveraging Clusters in Term Graph
Improving Difficult Queries by Leveraging Clusters in Term Graph Rajul Anand and Alexander Kotov Department of Computer Science, Wayne State University, Detroit MI 48226, USA {rajulanand,kotov}@wayne.edu
XOTcl an Object-Oriented Scripting Language. First European Tcl/Tk User Meeting, 15/16th June 2000
XOTcl an Object-Oriented Scripting Language Gustaf Neumann Department of Information Systems Vienna University of Economics Vienna, Austria gustaf.neumann@wu-wien.ac.at Uwe Zdun Specification of Software
Programming II. Modularity 2017/18
Programming II Modularity 2017/18 Module? Lecture Outline Evolution and history of programming languages Modularity Example History of Programming Programming Paradigms How and why languages develop? How
Social Behavior Prediction Through Reality Mining
Social Behavior Prediction Through Reality Mining Charlie Dagli, William Campbell, Clifford Weinstein Human Language Technology Group MIT Lincoln Laboratory This work was sponsored by the DDR&E / RRTO
Ranked Retrieval. Evaluation in IR. One option is to average the precision scores at discrete. points on the ROC curve But which points?
Ranked Retrieval One option is to average the precision scores at discrete Precision 100% 0% More junk 100% Everything points on the ROC curve But which points? Recall We want to evaluate the system, not
Case Studies in Complex Networks
Case Studies in Complex Networks Introduction to Scientific Modeling CS 365 George Bezerra 08/27/2012 The origin of graph theory Königsberg bridge problem Leonard Euler (1707-1783) The Königsberg Bridge
SEO STRATEGY DOCUMENT
SEO STRATEGY DOCUMENT 01903 368559 Table Of Contents Introduction SEO Strategy SEO Process Pricing How To Get Started Introduction Thanks for your interest in our SEO Service! In this document you will
vector space retrieval many slides courtesy James Amherst
vector space retrieval many slides courtesy James Allan@umass Amherst 1 what is a retrieval model? Model is an idealization or abstraction of an actual process Mathematical models are used to study the
Jordan Boyd-Graber University of Maryland. Thursday, March 3, 2011
Data-Intensive Information Processing Applications! Session #5 Graph Algorithms Jordan Boyd-Graber University of Maryland Thursday, March 3, 2011 This work is licensed under a Creative Commons Attribution-Noncommercial-Share
USC Viterbi School of Engineering
Introduction to Computational Thinking and Data Science USC Viterbi School of Engineering http://www.datascience4all.org Term: Fall 2016 Time: Tues- Thur 10am- 11:50am Location: Allan Hancock Foundation
Authoritative K-Means for Clustering of Web Search Results
Authoritative K-Means for Clustering of Web Search Results Gaojie He Master in Information Systems Submission date: June 2010 Supervisor: Kjetil Nørvåg, IDI Co-supervisor: Robert Neumayer, IDI Norwegian
Social and Technological Network Data Analytics. Lecture 5: Structure of the Web, Search and Power Laws. Prof Cecilia Mascolo
Social and Technological Network Data Analytics Lecture 5: Structure of the Web, Search and Power Laws Prof Cecilia Mascolo In This Lecture We describe power law networks and their properties and show
Wednesday, March 8, Complex Networks. Presenter: Jirakhom Ruttanavakul. CS 790R, University of Nevada, Reno
Wednesday, March 8, 2006 Complex Networks Presenter: Jirakhom Ruttanavakul CS 790R, University of Nevada, Reno Presented Papers Emergence of scaling in random networks, Barabási & Bonabeau (2003) Scale-free
Query Processing in Highly-Loaded Search Engines
Query Processing in Highly-Loaded Search Engines Daniele Broccolo 1,2, Craig Macdonald 3, Salvatore Orlando 1,2, Iadh Ounis 3, Raffaele Perego 2, Fabrizio Silvestri 2, and Nicola Tonellotto 2 1 Università