Other Real-Time Applications

 Two most common real-time applications are real-time databases and multimedia applications.

Real-Time Databases

The term real-time database systems refers to a diverse spectrum of information systems, ranging from stock price quotation systems, to track records databases, to real-time file systems. What distinguishes these databases from non-real-time databases is the perishable nature of the data maintained by them. Specifically, a real-time database contains data objects, called image objects that represent real-world objects. The attributes of an image object are those of the represented real world object.

For example, an air traffic control database contains image objects that represent aircraft in the coverage area. The attributes of such an image object include the position and heading of the aircraft. The values of these attributes are updated periodically based on the measured values of the actual position and heading provided by the radar system. Without this update, the stored position and heading will deviate more and more from the actual position and heading. In this sense, the quality of stored data degrades. This is why we say that real-time data are perishable. In contrast, an underlying assumption of non-real-time databases (e.g., a payroll database) is that in the absence of updates the data contained in them remain good (i.e., the database remains in some consistent state satisfying all the data integrity constraints of the database).

Absolute Temporal Consistency

The age of a data object measures how up-to-date the information provided by the object is. The age of an image object at any time is the length of time since the instant of the last update, that is, when its value is made equal to that of the real-world object it represents. The age of a data object whose value is computed from the values of other objects is equal to the oldest of the ages of those objects.

A set of data objects is said to be absolutely (temporally) consistent if the maximum age of the objects in the set is no greater than a certain threshold.

Relative Temporal Consistency

A set of data objects is said to be relatively consistent if the maximum difference in ages of the objects in the set is no greater than the relative consistency threshold used by the application. The column labeled “Rel. Cons.” in Table 1–1gives typical values of this threshold.

Multimedia Applications

A multimedia application may process, store, transmit, and display any number of video streams, audio streams, images, graphics, and text. A video stream is a sequence of data frames which encodes a video. An audio stream encodes a voice, sound, or music. Without compression, the storage space and transmission bandwidth required by a video are enormous. (As an example, we consider a small 100 × 100-pixel, 30-frames/second color video. If uncompressed, the video requires a transmission bandwidth of 2.7 Mbits per second when the value of each component at each pixel is encoded with 3 bits.) Therefore, a video stream, as well as the associated audio stream, is invariably compressed as soon as it is captured.

MPEG Compression/ Decompression

This compression standard makes use of 3 techniques. They are motion compensation for reducing temporal redundancy, discrete cosine transform for reducing spatial redundancy and entropy encoding for reducing the number of bits required to encode all the information.

Motion Estimation

In this step analysis and estimation is done. Video frames are not independent and hence significance amount of compression can be done. For this each image is divided into 16*16 pixel square pieces called major block.

Only frames 1+αk for k=0,1,2…. Are encoded independently of other frames where α is an application specified integer constant. These frames are I frames. I(Intra coded) frames are the points for random access of the video. More the value of α , more random accessible the video is and poor the compression ratio. A good compromise is α=9. The frame between consecutive I- frames are called P and B frames. When α is 9 frames produced are I,B,B,P,B,B,P,B,B,I,B,B,P… For every k ≥ 0 frame 1+9k+3 is P(Predictive coded)-frame. P frame is generated by prediction from previous I frame. B (bidirectionally predicted) frame is predicted from both I and P frames.