SlideShare a Scribd company logo

Finding Similar Files in Large Repositories Using Content-Based Chunking

•
•
F
feiwin
Technology
1 of 19
Download to read offline
Finding Similar Files in Large Document Repositories KDD’05, August 21-24, 2005, Chicago, Illinois, USA. Copyright 2005 ACM George Forman HewlettPackard Labs [email_address] Kave Eshghi HewlettPackard Labs [email_address] Stephane Chiocchetti HewlettPackard France [email_address]
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hashing background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chunking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chunking and file similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
File similarity algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object]
File similarity algorithm (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
File similarity algorithm (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
File similarity algorithm (cont.) ,[object Object],[object Object],[object Object]
Handling identical files ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Handling identical files (cont.)
Complexity analysis ,[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommended

Scalable Discovery Of Hidden Emails From Large Folders
Scalable Discovery Of Hidden Emails From Large FoldersScalable Discovery Of Hidden Emails From Large Folders
Scalable Discovery Of Hidden Emails From Large Foldersfeiwin
 
Data Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and FutureData Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and Futurefeiwin
 
Ir 02
Ir 02Ir 02
Ir 02Mohammed Romi
 
Ir 03
Ir 03Ir 03
Ir 03Mohammed Romi
 
Ir 08
Ir 08Ir 08
Ir 08Mohammed Romi
 
Ir 09
Ir 09Ir 09
Ir 09Mohammed Romi
 
Ju3517011704
Ju3517011704Ju3517011704
Ju3517011704IJERA Editor
 
Email Data Cleaning
Email Data CleaningEmail Data Cleaning
Email Data Cleaningfeiwin
 

Recommended

Scalable Discovery Of Hidden Emails From Large Folders
Scalable Discovery Of Hidden Emails From Large FoldersScalable Discovery Of Hidden Emails From Large Folders
Scalable Discovery Of Hidden Emails From Large Foldersfeiwin
 
Data Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and FutureData Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and Futurefeiwin
 
Ir 02
Ir 02Ir 02
Ir 02Mohammed Romi
 
Ir 03
Ir 03Ir 03
Ir 03Mohammed Romi
 
Ir 08
Ir 08Ir 08
Ir 08Mohammed Romi
 
Ir 09
Ir 09Ir 09
Ir 09Mohammed Romi
 
Ju3517011704
Ju3517011704Ju3517011704
Ju3517011704IJERA Editor
 
Email Data Cleaning
Email Data CleaningEmail Data Cleaning
Email Data Cleaningfeiwin
 
Text categorization
Text categorizationText categorization
Text categorizationShubham Pahune
 
Duet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning TrackDuet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning TrackBhaskar Mitra
 
Algorithm Name Detection & Extraction
Algorithm Name Detection & ExtractionAlgorithm Name Detection & Extraction
Algorithm Name Detection & ExtractionDeeksha thakur
 
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackConformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
 
TextRank: Bringing Order into Texts
TextRank: Bringing Order into TextsTextRank: Bringing Order into Texts
TextRank: Bringing Order into TextsShubhangi Tandon
 
IRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research PapersIRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research PapersSriTeja Allaparthi
 
Indexing for Large DNA Database sequences
Indexing for Large DNA Database sequencesIndexing for Large DNA Database sequences
Indexing for Large DNA Database sequencesCSCJournals
 
Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)KU Leuven
 
Probabilistic models (part 1)
Probabilistic models (part 1)Probabilistic models (part 1)
Probabilistic models (part 1)KU Leuven
 
Similarity Measurement Preliminary Results
Similarity Measurement Preliminary ResultsSimilarity Measurement Preliminary Results
Similarity Measurement Preliminary Resultsxiaojuzheng
 
Classifying Text using CNN
Classifying Text using CNNClassifying Text using CNN
Classifying Text using CNNSomnath Banerjee
 
A Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia ArticlesA Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia Articlesijma
 
How web searching engines work
How web searching engines workHow web searching engines work
How web searching engines workVNIT-ACM Student Chapter
 
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESFINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESkevig
 
Text categorization
Text categorizationText categorization
Text categorizationKU Leuven
 
The vector space model
The vector space modelThe vector space model
The vector space modelpkgosh
 
Does sizematter
Does sizematterDoes sizematter
Does sizematterAmparo Elizabeth Cano Basave
 
Topic Extraction on Domain Ontology
Topic Extraction on Domain OntologyTopic Extraction on Domain Ontology
Topic Extraction on Domain OntologyKeerti Bhogaraju
 
ppt
pptppt
pptbutest
 
Latent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyLatent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyAuro Tripathy
 
Fota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity AlgorithmsFota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity AlgorithmsShivansh Gaur
 
2.5 lab1
2.5 lab12.5 lab1
2.5 lab1Saqlain Abbas
 

More Related Content

What's hot

Duet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning TrackDuet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning TrackBhaskar Mitra
 
Algorithm Name Detection & Extraction
Algorithm Name Detection & ExtractionAlgorithm Name Detection & Extraction
Algorithm Name Detection & ExtractionDeeksha thakur
 
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackConformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
 
TextRank: Bringing Order into Texts
TextRank: Bringing Order into TextsTextRank: Bringing Order into Texts
TextRank: Bringing Order into TextsShubhangi Tandon
 
IRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research PapersIRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research PapersSriTeja Allaparthi
 
Indexing for Large DNA Database sequences
Indexing for Large DNA Database sequencesIndexing for Large DNA Database sequences
Indexing for Large DNA Database sequencesCSCJournals
 
Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)KU Leuven
 
Probabilistic models (part 1)
Probabilistic models (part 1)Probabilistic models (part 1)
Probabilistic models (part 1)KU Leuven
 
Similarity Measurement Preliminary Results
Similarity Measurement Preliminary ResultsSimilarity Measurement Preliminary Results
Similarity Measurement Preliminary Resultsxiaojuzheng
 
Classifying Text using CNN
Classifying Text using CNNClassifying Text using CNN
Classifying Text using CNNSomnath Banerjee
 
A Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia ArticlesA Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia Articlesijma
 
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESFINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESkevig
 
Text categorization
Text categorizationText categorization
Text categorizationKU Leuven
 
The vector space model
The vector space modelThe vector space model
The vector space modelpkgosh
 
Topic Extraction on Domain Ontology
Topic Extraction on Domain OntologyTopic Extraction on Domain Ontology
Topic Extraction on Domain OntologyKeerti Bhogaraju
 
Latent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyLatent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyAuro Tripathy
 

What's hot (20)

Text categorization
Text categorizationText categorization
Text categorization
 
Duet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning TrackDuet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning Track
 
Algorithm Name Detection & Extraction
Algorithm Name Detection & ExtractionAlgorithm Name Detection & Extraction
Algorithm Name Detection & Extraction
 
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackConformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
 
TextRank: Bringing Order into Texts
TextRank: Bringing Order into TextsTextRank: Bringing Order into Texts
TextRank: Bringing Order into Texts
 
IRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research PapersIRE- Algorithm Name Detection in Research Papers
IRE- Algorithm Name Detection in Research Papers
 
Indexing for Large DNA Database sequences
Indexing for Large DNA Database sequencesIndexing for Large DNA Database sequences
Indexing for Large DNA Database sequences
 
Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)Tdm probabilistic models (part 2)
Tdm probabilistic models (part 2)
 
Probabilistic models (part 1)
Probabilistic models (part 1)Probabilistic models (part 1)
Probabilistic models (part 1)
 
Similarity Measurement Preliminary Results
Similarity Measurement Preliminary ResultsSimilarity Measurement Preliminary Results
Similarity Measurement Preliminary Results
 
Classifying Text using CNN
Classifying Text using CNNClassifying Text using CNN
Classifying Text using CNN
 
A Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia ArticlesA Document Exploring System on LDA Topic Model for Wikipedia Articles
A Document Exploring System on LDA Topic Model for Wikipedia Articles
 
How web searching engines work
How web searching engines workHow web searching engines work
How web searching engines work
 
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESFINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES
 
Text categorization
Text categorizationText categorization
Text categorization
 
The vector space model
The vector space modelThe vector space model
The vector space model
 
Does sizematter
Does sizematterDoes sizematter
Does sizematter
 
Topic Extraction on Domain Ontology
Topic Extraction on Domain OntologyTopic Extraction on Domain Ontology
Topic Extraction on Domain Ontology
 
ppt
pptppt
ppt
 
Latent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyLatent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro Tripathy
 

Similar to Finding Similar Files in Large Repositories Using Content-Based Chunking

Fota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity AlgorithmsFota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity AlgorithmsShivansh Gaur
 
FINAL PROJECT REPORT
FINAL PROJECT REPORTFINAL PROJECT REPORT
FINAL PROJECT REPORTDhrumil Shah
 
Comparison Study of Lossless Data Compression Algorithms for Text Data
Comparison Study of Lossless Data Compression Algorithms for Text DataComparison Study of Lossless Data Compression Algorithms for Text Data
Comparison Study of Lossless Data Compression Algorithms for Text DataIOSR Journals
 
Google File System
Google File SystemGoogle File System
Google File SystemDreamJobs1
 
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - Indexing
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - IndexingInformation Retrieval, Encoding, Indexing, Big Table. Lecture 6 - Indexing
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - IndexingSean Golliher
 
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...IRJET Journal
 
An unsupervised framework for effective indexing of BigData
An unsupervised framework for effective indexing of BigDataAn unsupervised framework for effective indexing of BigData
An unsupervised framework for effective indexing of BigDataRamakrishna Prasad Sakhamuri
 
Advances in File Carving
Advances in File CarvingAdvances in File Carving
Advances in File CarvingRob Zirnstein
 
File System Implementation.pptx
File System Implementation.pptxFile System Implementation.pptx
File System Implementation.pptxRajapriya82
 
Amazon Product Sentiment review
Amazon Product Sentiment reviewAmazon Product Sentiment review
Amazon Product Sentiment reviewLalit Jain
 
Duplicate File Analyzer using N-layer Hash and Hash Table
Duplicate File Analyzer using N-layer Hash and Hash TableDuplicate File Analyzer using N-layer Hash and Hash Table
Duplicate File Analyzer using N-layer Hash and Hash TableAM Publications
 
Building modern data lakes
Building modern data lakes Building modern data lakes
Building modern data lakes Minio
 
An Efficient Search Engine for Searching Desired File
An Efficient Search Engine for Searching Desired FileAn Efficient Search Engine for Searching Desired File
An Efficient Search Engine for Searching Desired FileIDES Editor
 
Software tools for high-throughput materials data generation and data mining
Software tools for high-throughput materials data generation and data miningSoftware tools for high-throughput materials data generation and data mining
Software tools for high-throughput materials data generation and data miningAnubhav Jain
 
Efficient Shared Data in Perl
Efficient Shared Data in PerlEfficient Shared Data in Perl
Efficient Shared Data in PerlPerrin Harkins
 
An Efficient Approach to Manage Small Files in Distributed File Systems
An Efficient Approach to Manage Small Files in Distributed File SystemsAn Efficient Approach to Manage Small Files in Distributed File Systems
An Efficient Approach to Manage Small Files in Distributed File SystemsIRJET Journal
 
(Julien le dem) parquet
(Julien le dem) parquet(Julien le dem) parquet
(Julien le dem) parquetNAVER D2
 

Similar to Finding Similar Files in Large Repositories Using Content-Based Chunking (20)

Fota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity AlgorithmsFota Delta Size Reduction Using FIle Similarity Algorithms
Fota Delta Size Reduction Using FIle Similarity Algorithms
 
2.5 lab1
2.5 lab12.5 lab1
2.5 lab1
 
FINAL PROJECT REPORT
FINAL PROJECT REPORTFINAL PROJECT REPORT
FINAL PROJECT REPORT
 
Comparison Study of Lossless Data Compression Algorithms for Text Data
Comparison Study of Lossless Data Compression Algorithms for Text DataComparison Study of Lossless Data Compression Algorithms for Text Data
Comparison Study of Lossless Data Compression Algorithms for Text Data
 
Google File System
Google File SystemGoogle File System
Google File System
 
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - Indexing
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - IndexingInformation Retrieval, Encoding, Indexing, Big Table. Lecture 6 - Indexing
Information Retrieval, Encoding, Indexing, Big Table. Lecture 6 - Indexing
 
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...
 
An unsupervised framework for effective indexing of BigData
An unsupervised framework for effective indexing of BigDataAn unsupervised framework for effective indexing of BigData
An unsupervised framework for effective indexing of BigData
 
Advances in File Carving
Advances in File CarvingAdvances in File Carving
Advances in File Carving
 
File System Implementation.pptx
File System Implementation.pptxFile System Implementation.pptx
File System Implementation.pptx
 
Amazon Product Sentiment review
Amazon Product Sentiment reviewAmazon Product Sentiment review
Amazon Product Sentiment review
 
Duplicate File Analyzer using N-layer Hash and Hash Table
Duplicate File Analyzer using N-layer Hash and Hash TableDuplicate File Analyzer using N-layer Hash and Hash Table
Duplicate File Analyzer using N-layer Hash and Hash Table
 
Building modern data lakes
Building modern data lakes Building modern data lakes
Building modern data lakes
 
An Efficient Search Engine for Searching Desired File
An Efficient Search Engine for Searching Desired FileAn Efficient Search Engine for Searching Desired File
An Efficient Search Engine for Searching Desired File
 
Software tools for high-throughput materials data generation and data mining
Software tools for high-throughput materials data generation and data miningSoftware tools for high-throughput materials data generation and data mining
Software tools for high-throughput materials data generation and data mining
 
Efficient Shared Data in Perl
Efficient Shared Data in PerlEfficient Shared Data in Perl
Efficient Shared Data in Perl
 
An Efficient Approach to Manage Small Files in Distributed File Systems
An Efficient Approach to Manage Small Files in Distributed File SystemsAn Efficient Approach to Manage Small Files in Distributed File Systems
An Efficient Approach to Manage Small Files in Distributed File Systems
 
(Julien le dem) parquet
(Julien le dem) parquet(Julien le dem) parquet
(Julien le dem) parquet
 
ALA Interoperability
ALA InteroperabilityALA Interoperability
ALA Interoperability
 
Hadoop data management
Hadoop data managementHadoop data management
Hadoop data management
 

More from feiwin

2007/7/25 Proposal update
2007/7/25 Proposal update2007/7/25 Proposal update
2007/7/25 Proposal updatefeiwin
 
2006/11/20 Proposal
2006/11/20 Proposal2006/11/20 Proposal
2006/11/20 Proposalfeiwin
 
2006/10/16 Proposal
2006/10/16 Proposal2006/10/16 Proposal
2006/10/16 Proposalfeiwin
 
Mining Product Reputations On the Web
Mining Product Reputations On the WebMining Product Reputations On the Web
Mining Product Reputations On the Webfeiwin
 
Mining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire DataMining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire Datafeiwin
 
An Integrated Framework on Mining Logs Files for Computing System Management
An Integrated Framework on Mining Logs Files for Computing System ManagementAn Integrated Framework on Mining Logs Files for Computing System Management
An Integrated Framework on Mining Logs Files for Computing System Managementfeiwin
 
Real Time Competitive Marketing Intelligence
Real Time Competitive Marketing IntelligenceReal Time Competitive Marketing Intelligence
Real Time Competitive Marketing Intelligencefeiwin
 

More from feiwin (7)

2007/7/25 Proposal update
2007/7/25 Proposal update2007/7/25 Proposal update
2007/7/25 Proposal update
 
2006/11/20 Proposal
2006/11/20 Proposal2006/11/20 Proposal
2006/11/20 Proposal
 
2006/10/16 Proposal
2006/10/16 Proposal2006/10/16 Proposal
2006/10/16 Proposal
 
Mining Product Reputations On the Web
Mining Product Reputations On the WebMining Product Reputations On the Web
Mining Product Reputations On the Web
 
Mining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire DataMining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire Data
 
An Integrated Framework on Mining Logs Files for Computing System Management
An Integrated Framework on Mining Logs Files for Computing System ManagementAn Integrated Framework on Mining Logs Files for Computing System Management
An Integrated Framework on Mining Logs Files for Computing System Management
 
Real Time Competitive Marketing Intelligence
Real Time Competitive Marketing IntelligenceReal Time Competitive Marketing Intelligence
Real Time Competitive Marketing Intelligence
 

Recently uploaded

Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Nikki Chapple
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
Infrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsInfrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsYoss Cohen
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sectoritnewsafrica
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...itnewsafrica
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfAarwolf Industries LLC
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 
Accelerating Enterprise Software Engineering with Platformless
Accelerating Enterprise Software Engineering with PlatformlessAccelerating Enterprise Software Engineering with Platformless
Accelerating Enterprise Software Engineering with PlatformlessWSO2
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFMichael Gough
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...itnewsafrica
 

Recently uploaded (20)

Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
Infrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsInfrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platforms
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdf
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 
Accelerating Enterprise Software Engineering with Platformless
Accelerating Enterprise Software Engineering with PlatformlessAccelerating Enterprise Software Engineering with Platformless
Accelerating Enterprise Software Engineering with Platformless
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDF
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
 

Finding Similar Files in Large Repositories Using Content-Based Chunking

  • 1. Finding Similar Files in Large Document Repositories KDD’05, August 21-24, 2005, Chicago, Illinois, USA. Copyright 2005 ACM George Forman HewlettPackard Labs [email_address] Kave Eshghi HewlettPackard Labs [email_address] Stephane Chiocchetti HewlettPackard France [email_address]
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.  
  • 9.  
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 16.
  • 17.
  • 18.
  • 19.