This chapter gives detailed information on the data processing functions and their how this processing interacts with other Geoplot facilities, especially. Data processing systems or methods that are specially adapted for managing, promoting or practicing commercial or financial activities. Groups G06Q 10/ TOPICS FOR SS 1 THEME 1: Information Age ICT and the Society CHAPTER One: HISTORY OF THEME 3: Information Processing COMPUTING I CHAPTER.

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ICT's into modern business practices. The Data processing cycle. 2. COMPUTERS AND ASSOCIATED PERIPHERALS. Introduction to key ICT technologies. Data can be done manually using a pen and paper, mechanically using simple devices eg typewritter or electronically using modern dat processing toolseg. Introduction to Data Processing by MICHAEL J. CERULLO. Assistant Professor of Accounting. State University of New York at Albany. Albany, New York

View on ScienceDirect. Susan Wooldridge. Made Simple.

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Institutional Subscription. Free Shipping Free global shipping No minimum order. Why Use Software? Revision Questions Part Three: Batch Processing To save computational time, before the widespread use of distributed systems architecture, or even after it, stand-alone computer systems apply batch processing techniques.

This is particularly useful in financial applications or where data was secure such as medical records. Batch processing completes a range of data processes as a batch, by simplifying single commands to provide actions to multiple data sets.

This is a little like the comparison of a computer spreadsheet to a calculator in some ways. A calculation can be applied with one function, that is one step, to a whole column or series of columns, giving multiple results from one action.

The same concept is achieved in batch processing for data.

Data Processing

A series of actions or results can be achieved by applying a function to a whole series of data. In this way, the computer processing time is far less.

Batch processing can complete a queue of tasks without human intervention, and data systems may program priorities to certain functions or set times when batch processing can be completed.

Banks typically use this process to execute transactions after the close of business, where computers are no longer involved in data capture and can be dedicated to processing functions. Real Time Data Processing For commercial uses, many large data processing applications require real-time processing.

That is they need to get results from data exactly as it happens. One application of this that most of us can identify with is tracking stock market and currency trends. The data needs to be updated immediately since investors download in real time and prices update by the minute. Data on airline schedules and ticketing, and GPS tracking applications in transport services have similar needs for real-time updates.

The most common technology used in real time processing is stream processing.

The data analytics are drawn directly from the stream, that is, at the source. Where data is used to draw conclusions without uploading and transforming, the process is much quicker. Data virtualization techniques are another important development in real-time data processing, where the data remains in its source form, the only information is pulled for the data processing.

The beauty of data virtualization is that where transformation is not necessary, so the error is reduced. Data virtualization and stream processing mean that data analytics can be drawn in real time much quicker, benefiting many technical and financial applications, reducing processing times and errors.

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Other than these popular Data processing Techniques there are three more processing techniques which are mentioned below- 6. This technique is now known as immediate or irregular access handling. This can be viewed easily with continuous preparing of data sets. This processing method highlights the fast contribution of exchange of data and connects directly with the databases. However, it is used all over the globe where we have the computer-based setups for Data capture and processing.

Data Processing

The computer is also known as electronic data processing machine. This method of processing data is very fast and accurate. For example, in a computerized education environment results of students are prepared through computer; in banks, accounts of customers are maintained or processed through computers etc.

Batch Processing is a method where the information to be organized is sorted into groups to allow for efficient and sequential processing.

Online Processing is a method that utilizes Internet connections and equipment directly attached to a computer. It is used mainly for information recording and research. Real-Time Processing is a technique that has the ability to respond almost immediately to various signals in order to acquire and process information. Distributed Processing is commonly utilized by remote workstations connected to one big central workstation or server.

ATMs are good examples of this data processing method. This is a method that utilizes Internet connections and equipment directly attached to a computer. This allows for the data stored in one place and being used at altogether different place. Cloud computing can be considered as a example which uses this type of processing.

This technique has the ability to respond almost immediately to various signals in order to acquire and process information. These involve high maintainance andupfront cost attributed to very advanced technology and computing power. Time saved is maximum in this case as the output is seen in real time. For example in banking transactions Example of real time processing.

This method is commonly utilized by remote workstations connected to one big central workstation or server.

Computers and Data Processing

All the end machines run on a fixed software located at a particular place and makes use of exactly same information and sets of instruction. Most companies are now shifting from the use of geographically distributed personal computers. Examples of industries and business organizations that extensively use distributed processing systems. Benefits and three risks that might be associated with the distributed data Processing system.

A logical file is viewed in terms of what data items it contains and what processing operations may be performed on the data.Data processing thus carried out by software is done as per the predefined set of operations.

A physical file is viewed in terms of how the data items found in a file are arranged on the storage media and how they can be processed. These are exported as notepad or WordPad files. Personal information is secured with SSL technology. This can be viewed easily with continuous preparing of data sets. Most of the data caught in these applications are standardized, and somewhat error proofed. Electronic data management became widespread with the introduction of the personal computer in the s.