SlideShare uma empresa Scribd logo
1 de 15
Purpose
The application allows large files to be compressed
for either sending via e-mail or transferring to another
source (e.g. from desktop computer to laptop).
software which is used to compress data and
therefore save time and space and make e-mail
attachments faster.
Compress files make it easy to keep related files
together and make transporting, e-mailing,
downloading and storing data and software faster and
more efficient
The basic objective of the project
Compress files compress data and therefore save time and
space and make downloading software and transferring e-
mail attachments faster. Typical uses for compress files
include:
Distributing files on the Internet: the file transfer is
quicker because the file is compressed.
Sending a group of related files to an associate: When you
distribute a collection of files as a single compress file, you
benefit from the file grouping as well as compression.
Saving disk space: If you have large files that are
important but seldom used, such as large data files, simply
compress the files into a compress file and then
decompress (or "extract") them only when needed.
GZipStream / DeflateStream
This gzip stream class represents the gzip data format, which
uses an industry-standard algorithm for lossless file
compression and decompression. The format includes a
cyclic redundancy check value for detecting data corruption.
The gzip data format uses the same algorithm as the
DeflateStream class, but can be extended to use other
compression formats. The format can be readily
implemented in a manner not covered by patents.
Starting with the .NET Framework 4.5, the DeflateStream
class uses the zlib library for compression. As a result, it
provides a better compression algorithm and, in most cases,
a smaller compressed file than it provides in earlier versions
of the .NET Framework.
GZipStream / DeflateStream
The compression functionality in DeflateStream
and GZipStream is exposed as a stream. Data is
read on a byte-by-byte basis, so it is not possible
to perform multiple passes to determine the
best method for compressing entire files or large
blocks of data.
The DeflateStream and GZipStream classes are
best used on uncompressed sources of data. If
the source data is already compressed, using
these classes may actually increase the size of
the stream.
GZipStream / DeflateStream
The DeflateStream class is a direct descendant of the
Stream class; it provides the methods that the Stream
class defines. The DeflateStream class implements the
Deflate algorithm as it reads and writes data. This is
an industry-standard algorithm that performs lossless
file compression and decompression. The
DeflateStream class cannot process a stream that is
larger than 4 gigabytes (GB).
GZipStream / DeflateStream
Like the DeflateStream class, the GZipStream class
inherits from the Stream class and implements the Deflate
algorithm. The difference is that the format of the data is
compatible with the GZIP specification; it includes
additional header information that enables tools such as
GZip, WinZip, and WinRAR to examine and decompress a
file that is written by using a GZipStream object. Similarly,
you can use the GZipStream class to read compressed files
that are created by using these tools. The GZIP format
adds a small overhead, so data that is compressed by using
a GZipStream object is a little larger than that compressed
by using a DeflateStream object.  
WORKING PROCESS OF THE
PROJECT
The proposed system contains the following main 
processes: -
 Compression  
To create a new Zip file, open G-zip setup.
Search the file from computer for compressing by
clicking the “Browse” button
Simply click a button “Compress” to Create a new Zip
file in your computer
After this a compress file is created with “.gkg
“extension
WORKING PROCESS OF THE
PROJECT
Decompression
To decompress a zip file, open G-zip setup
Search the file from computer for compressing by
clicking the “Browse” button
Simply click a button “Decompress” to Create a new
Zip file in your computer
After this the file is decompress.
Working Process
Application design look
File name
File
Type
Size
before
compress
ion
Size after
compress
ion
Compressi
on
percentage
01 - Maroon 5
-OneMore
PPT Sandeep Tayal
Sum 41 -Pieces
-YouTube
VHDL.Programming.
DouglasPerry
Data-Security
.mp3
.ppt
.mp4
.pdf
.txt
.docx
8,674 kb
4,823 kb
13,819
kb
2,357 kb
3.65 mb
5.59 mb
8,317 kb
4,789 kb
13,657
kb
1,819 kb
0.657 mb
5.55 mb
4.1%
0.7%
1.1%
22.32%
82%
0.7%
Result
Compress Files and Save Space with GZip and DeflateStreams

Mais conteúdo relacionado

Mais procurados

Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChangerZero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChangerMongoDB
 
MongoDB Memory Management Demystified
MongoDB Memory Management DemystifiedMongoDB Memory Management Demystified
MongoDB Memory Management DemystifiedMongoDB
 
Fusion-io and MySQL at Craigslist
Fusion-io and MySQL at CraigslistFusion-io and MySQL at Craigslist
Fusion-io and MySQL at CraigslistJeremy Zawodny
 
MongoUK - Approaching 1 billion documents with MongoDB1 Billion Documents
MongoUK - Approaching 1 billion documents with MongoDB1 Billion DocumentsMongoUK - Approaching 1 billion documents with MongoDB1 Billion Documents
MongoUK - Approaching 1 billion documents with MongoDB1 Billion DocumentsBoxed Ice
 
Features of couchDB
Features of couchDBFeatures of couchDB
Features of couchDBJavatpoint
 
Webinar - Approaching 1 billion documents with MongoDB
Webinar - Approaching 1 billion documents with MongoDBWebinar - Approaching 1 billion documents with MongoDB
Webinar - Approaching 1 billion documents with MongoDBBoxed Ice
 
IPWB and IPFS at WAC2017
IPWB and IPFS at WAC2017IPWB and IPFS at WAC2017
IPWB and IPFS at WAC2017David Dias
 
Development to Production with Sharded MongoDB Clusters
Development to Production with Sharded MongoDB ClustersDevelopment to Production with Sharded MongoDB Clusters
Development to Production with Sharded MongoDB ClustersSeveralnines
 
Redis for horizontally scaled data processing at jFrog bintray
Redis for horizontally scaled data processing at jFrog bintrayRedis for horizontally scaled data processing at jFrog bintray
Redis for horizontally scaled data processing at jFrog bintrayRedis Labs
 
Realtime Search Infrastructure at Craigslist (OpenWest 2014)
Realtime Search Infrastructure at Craigslist (OpenWest 2014)Realtime Search Infrastructure at Craigslist (OpenWest 2014)
Realtime Search Infrastructure at Craigslist (OpenWest 2014)Jeremy Zawodny
 
Advanced Administration, Monitoring and Backup
Advanced Administration, Monitoring and BackupAdvanced Administration, Monitoring and Backup
Advanced Administration, Monitoring and BackupMongoDB
 
Date-tiered Compaction Policy for Time-series Data
Date-tiered Compaction Policy for Time-series DataDate-tiered Compaction Policy for Time-series Data
Date-tiered Compaction Policy for Time-series DataHBaseCon
 
Accessing mongo DB In Mule ESB
Accessing mongo DB In Mule ESBAccessing mongo DB In Mule ESB
Accessing mongo DB In Mule ESBSrinu Prasad
 
Day 2 General Session Presentations RedisConf
Day 2 General Session Presentations RedisConfDay 2 General Session Presentations RedisConf
Day 2 General Session Presentations RedisConfRedis Labs
 
Introducing MongoDB in a multi-site HA environment
Introducing MongoDB in a multi-site HA environmentIntroducing MongoDB in a multi-site HA environment
Introducing MongoDB in a multi-site HA environmentSebastian Geib
 

Mais procurados (19)

Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChangerZero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
 
MongoDB Memory Management Demystified
MongoDB Memory Management DemystifiedMongoDB Memory Management Demystified
MongoDB Memory Management Demystified
 
Hdfs internals
Hdfs internalsHdfs internals
Hdfs internals
 
Fusion-io and MySQL at Craigslist
Fusion-io and MySQL at CraigslistFusion-io and MySQL at Craigslist
Fusion-io and MySQL at Craigslist
 
MongoUK - Approaching 1 billion documents with MongoDB1 Billion Documents
MongoUK - Approaching 1 billion documents with MongoDB1 Billion DocumentsMongoUK - Approaching 1 billion documents with MongoDB1 Billion Documents
MongoUK - Approaching 1 billion documents with MongoDB1 Billion Documents
 
Features of couchDB
Features of couchDBFeatures of couchDB
Features of couchDB
 
Webinar - Approaching 1 billion documents with MongoDB
Webinar - Approaching 1 billion documents with MongoDBWebinar - Approaching 1 billion documents with MongoDB
Webinar - Approaching 1 billion documents with MongoDB
 
IPWB and IPFS at WAC2017
IPWB and IPFS at WAC2017IPWB and IPFS at WAC2017
IPWB and IPFS at WAC2017
 
Development to Production with Sharded MongoDB Clusters
Development to Production with Sharded MongoDB ClustersDevelopment to Production with Sharded MongoDB Clusters
Development to Production with Sharded MongoDB Clusters
 
Ipfs
IpfsIpfs
Ipfs
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
 
Redis for horizontally scaled data processing at jFrog bintray
Redis for horizontally scaled data processing at jFrog bintrayRedis for horizontally scaled data processing at jFrog bintray
Redis for horizontally scaled data processing at jFrog bintray
 
Introduction to Web Designing
Introduction to Web DesigningIntroduction to Web Designing
Introduction to Web Designing
 
Realtime Search Infrastructure at Craigslist (OpenWest 2014)
Realtime Search Infrastructure at Craigslist (OpenWest 2014)Realtime Search Infrastructure at Craigslist (OpenWest 2014)
Realtime Search Infrastructure at Craigslist (OpenWest 2014)
 
Advanced Administration, Monitoring and Backup
Advanced Administration, Monitoring and BackupAdvanced Administration, Monitoring and Backup
Advanced Administration, Monitoring and Backup
 
Date-tiered Compaction Policy for Time-series Data
Date-tiered Compaction Policy for Time-series DataDate-tiered Compaction Policy for Time-series Data
Date-tiered Compaction Policy for Time-series Data
 
Accessing mongo DB In Mule ESB
Accessing mongo DB In Mule ESBAccessing mongo DB In Mule ESB
Accessing mongo DB In Mule ESB
 
Day 2 General Session Presentations RedisConf
Day 2 General Session Presentations RedisConfDay 2 General Session Presentations RedisConf
Day 2 General Session Presentations RedisConf
 
Introducing MongoDB in a multi-site HA environment
Introducing MongoDB in a multi-site HA environmentIntroducing MongoDB in a multi-site HA environment
Introducing MongoDB in a multi-site HA environment
 

Destaque

data compression technique
data compression techniquedata compression technique
data compression techniqueCHINMOY PAUL
 
Fundamentals of Data compression
Fundamentals of Data compressionFundamentals of Data compression
Fundamentals of Data compressionM.k. Praveen
 
Data Compression for Multi-dimentional Data Warehouses
Data Compression for Multi-dimentional Data WarehousesData Compression for Multi-dimentional Data Warehouses
Data Compression for Multi-dimentional Data WarehousesMushfiqur Rahman
 
Keystone summer school_2015_miguel_antonio_ldcompression_4-joined
Keystone summer school_2015_miguel_antonio_ldcompression_4-joinedKeystone summer school_2015_miguel_antonio_ldcompression_4-joined
Keystone summer school_2015_miguel_antonio_ldcompression_4-joinedJoel Azzopardi
 
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...saumyatapu
 
Data Compression Project Presentation
Data Compression Project PresentationData Compression Project Presentation
Data Compression Project PresentationMyuran Kanga, MS, MBA
 
Data Compression In SQL
Data Compression In SQLData Compression In SQL
Data Compression In SQLBoosh Booshan
 
Chapter 5 - Data Compression
Chapter 5 - Data CompressionChapter 5 - Data Compression
Chapter 5 - Data CompressionPratik Pradhan
 
Data compression techniques
Data compression techniquesData compression techniques
Data compression techniquesDeep Bhatt
 
Spandana image processing and compression techniques (7840228)
Spandana   image processing and compression techniques (7840228)Spandana   image processing and compression techniques (7840228)
Spandana image processing and compression techniques (7840228)indianspandana
 
Text compression in LZW and Flate
Text compression in LZW and FlateText compression in LZW and Flate
Text compression in LZW and FlateSubeer Rangra
 
Data Compression Technique
Data Compression TechniqueData Compression Technique
Data Compression Techniquenayakslideshare
 
Data compression introduction
Data compression introductionData compression introduction
Data compression introductionRahul Khanwani
 

Destaque (20)

Data compression
Data compressionData compression
Data compression
 
Compression techniques
Compression techniquesCompression techniques
Compression techniques
 
data compression technique
data compression techniquedata compression technique
data compression technique
 
Data compression
Data compressionData compression
Data compression
 
Compression
CompressionCompression
Compression
 
Fundamentals of Data compression
Fundamentals of Data compressionFundamentals of Data compression
Fundamentals of Data compression
 
Data Compression for Multi-dimentional Data Warehouses
Data Compression for Multi-dimentional Data WarehousesData Compression for Multi-dimentional Data Warehouses
Data Compression for Multi-dimentional Data Warehouses
 
Compression
CompressionCompression
Compression
 
Data compression
Data compressionData compression
Data compression
 
Keystone summer school_2015_miguel_antonio_ldcompression_4-joined
Keystone summer school_2015_miguel_antonio_ldcompression_4-joinedKeystone summer school_2015_miguel_antonio_ldcompression_4-joined
Keystone summer school_2015_miguel_antonio_ldcompression_4-joined
 
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...
 
Data Compression Project Presentation
Data Compression Project PresentationData Compression Project Presentation
Data Compression Project Presentation
 
Data Compression In SQL
Data Compression In SQLData Compression In SQL
Data Compression In SQL
 
Chapter 5 - Data Compression
Chapter 5 - Data CompressionChapter 5 - Data Compression
Chapter 5 - Data Compression
 
Data compression techniques
Data compression techniquesData compression techniques
Data compression techniques
 
Spandana image processing and compression techniques (7840228)
Spandana   image processing and compression techniques (7840228)Spandana   image processing and compression techniques (7840228)
Spandana image processing and compression techniques (7840228)
 
Text compression in LZW and Flate
Text compression in LZW and FlateText compression in LZW and Flate
Text compression in LZW and Flate
 
Data Compression Technique
Data Compression TechniqueData Compression Technique
Data Compression Technique
 
Data compression
Data compression Data compression
Data compression
 
Data compression introduction
Data compression introductionData compression introduction
Data compression introduction
 

Semelhante a Compress Files and Save Space with GZip and DeflateStreams

Hadoop compression strata conference
Hadoop compression strata conferenceHadoop compression strata conference
Hadoop compression strata conferencenkabra
 
List the most common arguments and describe the effect of that argumen.docx
List the most common arguments and describe the effect of that argumen.docxList the most common arguments and describe the effect of that argumen.docx
List the most common arguments and describe the effect of that argumen.docxdarlened3
 
UserGuideHDFS_FinalDocument
UserGuideHDFS_FinalDocumentUserGuideHDFS_FinalDocument
UserGuideHDFS_FinalDocumentAnna Ellis
 
Managing your data - Introduction to Linux for bioinformatics
Managing your data - Introduction to Linux for bioinformaticsManaging your data - Introduction to Linux for bioinformatics
Managing your data - Introduction to Linux for bioinformaticsBITS
 
Lower bandwidth consumption and less waiting with Dropbox Business
Lower bandwidth consumption and less waiting with Dropbox BusinessLower bandwidth consumption and less waiting with Dropbox Business
Lower bandwidth consumption and less waiting with Dropbox BusinessPrincipled Technologies
 
7-zip compression settings guide
7-zip compression settings guide7-zip compression settings guide
7-zip compression settings guideLevan Chelidze
 
Advantages Of SAMBA
Advantages Of SAMBAAdvantages Of SAMBA
Advantages Of SAMBAAngela Hays
 
data stage-material
data stage-materialdata stage-material
data stage-materialRajesh Kv
 
Rhel cluster gfs_improveperformance
Rhel cluster gfs_improveperformanceRhel cluster gfs_improveperformance
Rhel cluster gfs_improveperformancesprdd
 
Google File System
Google File SystemGoogle File System
Google File Systemvivatechijri
 
FILE SPLITTER AND JOINER
FILE SPLITTER AND JOINERFILE SPLITTER AND JOINER
FILE SPLITTER AND JOINERRajesh Roky
 
Sequential file programming patterns and performance with .net
Sequential  file programming patterns and performance with .netSequential  file programming patterns and performance with .net
Sequential file programming patterns and performance with .netMichael Pavlovsky
 
File types pro forma
File types pro formaFile types pro forma
File types pro formaCam Stannard
 
Management file and directory in linux
Management file and directory in linuxManagement file and directory in linux
Management file and directory in linuxZkre Saleh
 
Unit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptxUnit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptxAnkitChauhan817826
 

Semelhante a Compress Files and Save Space with GZip and DeflateStreams (20)

Hadoop compression strata conference
Hadoop compression strata conferenceHadoop compression strata conference
Hadoop compression strata conference
 
File management.pptx
File management.pptxFile management.pptx
File management.pptx
 
List the most common arguments and describe the effect of that argumen.docx
List the most common arguments and describe the effect of that argumen.docxList the most common arguments and describe the effect of that argumen.docx
List the most common arguments and describe the effect of that argumen.docx
 
UserGuideHDFS_FinalDocument
UserGuideHDFS_FinalDocumentUserGuideHDFS_FinalDocument
UserGuideHDFS_FinalDocument
 
Managing your data - Introduction to Linux for bioinformatics
Managing your data - Introduction to Linux for bioinformaticsManaging your data - Introduction to Linux for bioinformatics
Managing your data - Introduction to Linux for bioinformatics
 
Lower bandwidth consumption and less waiting with Dropbox Business
Lower bandwidth consumption and less waiting with Dropbox BusinessLower bandwidth consumption and less waiting with Dropbox Business
Lower bandwidth consumption and less waiting with Dropbox Business
 
7-zip compression settings guide
7-zip compression settings guide7-zip compression settings guide
7-zip compression settings guide
 
Advantages Of SAMBA
Advantages Of SAMBAAdvantages Of SAMBA
Advantages Of SAMBA
 
Demo 0.9.4
Demo 0.9.4Demo 0.9.4
Demo 0.9.4
 
data stage-material
data stage-materialdata stage-material
data stage-material
 
C) ICT Application
C) ICT ApplicationC) ICT Application
C) ICT Application
 
File_mngtChap6.pdf
File_mngtChap6.pdfFile_mngtChap6.pdf
File_mngtChap6.pdf
 
Rhel cluster gfs_improveperformance
Rhel cluster gfs_improveperformanceRhel cluster gfs_improveperformance
Rhel cluster gfs_improveperformance
 
Google File System
Google File SystemGoogle File System
Google File System
 
FILE SPLITTER AND JOINER
FILE SPLITTER AND JOINERFILE SPLITTER AND JOINER
FILE SPLITTER AND JOINER
 
Sequential file programming patterns and performance with .net
Sequential  file programming patterns and performance with .netSequential  file programming patterns and performance with .net
Sequential file programming patterns and performance with .net
 
File types pro forma
File types pro formaFile types pro forma
File types pro forma
 
Lesson 2
Lesson 2Lesson 2
Lesson 2
 
Management file and directory in linux
Management file and directory in linuxManagement file and directory in linux
Management file and directory in linux
 
Unit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptxUnit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptx
 

Último

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilV3cube
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 

Último (20)

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 

Compress Files and Save Space with GZip and DeflateStreams

  • 1.
  • 2. Purpose The application allows large files to be compressed for either sending via e-mail or transferring to another source (e.g. from desktop computer to laptop). software which is used to compress data and therefore save time and space and make e-mail attachments faster. Compress files make it easy to keep related files together and make transporting, e-mailing, downloading and storing data and software faster and more efficient
  • 3. The basic objective of the project Compress files compress data and therefore save time and space and make downloading software and transferring e- mail attachments faster. Typical uses for compress files include: Distributing files on the Internet: the file transfer is quicker because the file is compressed. Sending a group of related files to an associate: When you distribute a collection of files as a single compress file, you benefit from the file grouping as well as compression. Saving disk space: If you have large files that are important but seldom used, such as large data files, simply compress the files into a compress file and then decompress (or "extract") them only when needed.
  • 4. GZipStream / DeflateStream This gzip stream class represents the gzip data format, which uses an industry-standard algorithm for lossless file compression and decompression. The format includes a cyclic redundancy check value for detecting data corruption. The gzip data format uses the same algorithm as the DeflateStream class, but can be extended to use other compression formats. The format can be readily implemented in a manner not covered by patents. Starting with the .NET Framework 4.5, the DeflateStream class uses the zlib library for compression. As a result, it provides a better compression algorithm and, in most cases, a smaller compressed file than it provides in earlier versions of the .NET Framework.
  • 5. GZipStream / DeflateStream The compression functionality in DeflateStream and GZipStream is exposed as a stream. Data is read on a byte-by-byte basis, so it is not possible to perform multiple passes to determine the best method for compressing entire files or large blocks of data. The DeflateStream and GZipStream classes are best used on uncompressed sources of data. If the source data is already compressed, using these classes may actually increase the size of the stream.
  • 6. GZipStream / DeflateStream The DeflateStream class is a direct descendant of the Stream class; it provides the methods that the Stream class defines. The DeflateStream class implements the Deflate algorithm as it reads and writes data. This is an industry-standard algorithm that performs lossless file compression and decompression. The DeflateStream class cannot process a stream that is larger than 4 gigabytes (GB).
  • 7. GZipStream / DeflateStream Like the DeflateStream class, the GZipStream class inherits from the Stream class and implements the Deflate algorithm. The difference is that the format of the data is compatible with the GZIP specification; it includes additional header information that enables tools such as GZip, WinZip, and WinRAR to examine and decompress a file that is written by using a GZipStream object. Similarly, you can use the GZipStream class to read compressed files that are created by using these tools. The GZIP format adds a small overhead, so data that is compressed by using a GZipStream object is a little larger than that compressed by using a DeflateStream object.  
  • 8. WORKING PROCESS OF THE PROJECT The proposed system contains the following main  processes: -  Compression   To create a new Zip file, open G-zip setup. Search the file from computer for compressing by clicking the “Browse” button Simply click a button “Compress” to Create a new Zip file in your computer After this a compress file is created with “.gkg “extension
  • 9. WORKING PROCESS OF THE PROJECT Decompression To decompress a zip file, open G-zip setup Search the file from computer for compressing by clicking the “Browse” button Simply click a button “Decompress” to Create a new Zip file in your computer After this the file is decompress.
  • 12.
  • 13.
  • 14. File name File Type Size before compress ion Size after compress ion Compressi on percentage 01 - Maroon 5 -OneMore PPT Sandeep Tayal Sum 41 -Pieces -YouTube VHDL.Programming. DouglasPerry Data-Security .mp3 .ppt .mp4 .pdf .txt .docx 8,674 kb 4,823 kb 13,819 kb 2,357 kb 3.65 mb 5.59 mb 8,317 kb 4,789 kb 13,657 kb 1,819 kb 0.657 mb 5.55 mb 4.1% 0.7% 1.1% 22.32% 82% 0.7% Result