Information systems (IS) is the study of complementary networks of hardware and software that people and organizations use to collect, filter, process, create, and distribute data.

Components of Information System

Technology:

Technology can be thought of as the application of scientific knowledge for practical purposes.

The first three components of information systems – hardware, software, and data – all fall under the category of technology.

People:

A focus on the people involved in information systems is the next step.

From the front-line help-desk workers, to systems analysts, to programmers, all the way up to the chief information officer

Process:

The last component of information systems is process. A process is a series of steps undertaken to achieve a desired outcome or goal.

Information systems are becoming more and more integrated with organizational processes, bringing more productivity and better control to those processes.

Data Compression techniques

Data compression squeezes data so it requires less disk space for storage and less bandwidth on a data transmission channel.

Most compression schemes take advantage of the fact that data contains a lot of repetition. For example, alphanumeric characters are normally represented by a 7-bit ASCII code, but a compression scheme can use a 3-bit code to represent the eight most common letters.

Compression concepts Lossy compression :

With lossy compression, it is assumed that some loss of information is acceptable.

The best example is a videoconference where there is an acceptable amount of frame loss in order to deliver the image in real time.

People may appear jerky in their movements, but you still have a grasp for what is happening on the other end of the conference.

In the case of graphics files, some resolution may be lost in order to create a smaller file.

The loss may be in the form of color depth or graphic detail.

For example, high-resolution details can be lost if a picture is going to be displayed on a low-resolution device. Loss is also acceptable in voice and audio compression, depending on the desired quality.

Lossless compression:

With lossless compression, data is compressed without any loss of data.

It assumes you want to get everything back that you put in.

Critical financial data files are examples where lossless compression is required.

Compression technique

Null compression:

Replaces a series of blank spaces with a compression code, followed by a value that represents the number of spaces.

Run-length compression:

Expands on the null compression tenique by compressing any series of four or more repeating characters.

The characters are replaced with a compression code, one of the characters, and a value that represents the number of characters to repeat.

Some synchronous data transmissions can be compressed by as much as 98 percent using this scheme.

Keyword encoding:

Creates a table with values that represent common sets of characters.

Frequently occurring words like for and the or character pairs like sh or th are represented with tokens used to store or transmit the characters.

Adaptive Huffman coding and Lempel Ziv algorithms:

These compression techniques use a symbol dictionary to represent recurring patterns.

The dictionary is dynamically updated during a compression as new patterns occur.

For data transmissions, the dictionary is passed to a receiving system so it knows how to decode the characters.

For file storage, the dictionary is stored with the compressed file.