Introduction

Data compression is the process of converting an input data stream or the source stream or the original raw data into another data stream that has a smaller size. data compression is popular because of two reasons

1) People like to accumulate data and hate to throw anything away. No matter however large a storage device may be, sooner or later it is going to overflow. Data compression seems useful because it delays this inevitability
2) People hate to wait a long time for data transfer. There are many known methods of data ....etc

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I want the report on Video Image Compression Techniques ....etc

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ABSTRACT

In this seminars, we give a brief introduction to data compression followed by a review of various compression strategies designed specifically for XML data. We then present two novel XML compression techniques, AXECHOP and TREECHOP.
AXECHOP uses a grammar-based approach that exploits the significant structural redundancies within XML documents, while TREECHOP supports querying of compressed XML data without requiring prior decompression. TREECHOP, which supports querying of compressed XML data without requi ....etc

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hello sir,
i am kukab..my seminar topic is xml data compression....i am in need of document and ppt for my seminar please send me the doc and ppt for xml data compression.
thank you

hello sir,
i am kukab..my seminar topic is xml data compression....i am in need of document and ppt for my seminar please send me the doc and ppt for xml data compression.my email id is [email protected]
thank you

....etc

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XML is a popular meta-language that facilitates the interchange and access of data. However, XML's verbose nature tends to increase the size of a data file by as much as ten-fold.

In this seminars, we give a brief introduction to data compression followed by a review of various compression strategies designed specifically for XML data. We then present two novel XML compression techniques, AXECHOP and TREECHOP.

AXECHOP uses a grammar-based approach that exploits the significant structural redundancies within XML documents, whi ....etc

[:=Read Full Message Here=:]

XML is a popular meta-language that facilitates the interchange and access of data. However, XML's verbose nature tends to increase the size of a data file by as much as ten-fold. In this seminars, we give a brief introduction to data compression followed by a review of various compression strategies designed specifically for XML data. We then present two novel XML compression techniques, AXECHOP and TREECHOP. AXECHOP uses a grammar-based approach that exploits the significant structural redundancies within XML documents, while TREECHOP support ....etc

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need some ideas for implementing of vcr engine in two wheeler si engines ....etc

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ABSTRACT
Data compression is the technique of converting an input data stream or the original data into another data stream which is shorter in length. There are many known methods for data compression. They are based on different ideas and are suitable for different types of data. But they all have the same principle that they compress data by removing the redundancy in the original data. Otherwise data compression can be looked up on as a change in representation from inefficient to efficient form.

INTROD ....etc

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An image defined in the real world is considered to be a function of two real variables say x,y.

Before going to processing an image, it is converted into a digital form. Digitization includes sampling of image and quantization of sampled values.

After converting the image into bit information, processing is performed.

This processing technique may be,
Image enhancement
Image reconstruction
Image compression

Image enhancement refers to accentuation, or sharpening, of image features such as boundaries.

Image restoration is co ....etc

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Presented by:
Sudeepta Mishra

Under the guidance of
Mr. Rowdra Ghatak




Introduction


Data compression is the process of encoding data so that it takes less storage space or less transmission time than it would if it were not compressed.
Compression is possible because most real-world data is very redundant
....etc

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