Database Technology

Why Database Technology Is Necessary for Real-time Analysis and Control

There is a growing demand for the use of database technologies and management systems spanning several organizations and industries. The need to manage, store, preserve and discard the flood of information generated have given rise to the necessity for database technology systems. Every organization requires a database system for data management, production processes, cost control, analysis, decision making etc. Giving the complexity of information generated daily, it is almost impossible to manage the amount of commercial and processed data. There is a need for organizations to manage, measure and control the flow of data in database systems as well as the need for this various system to work together in other to facilitate effective decision making, learn differences between database systems to help in the selection of the best database for different and specific situations. Prior to the information revolution of database systems, security trading systems basically stored more recent and necessary transactional activities while old and irrelevant information were discarded. The very essence of this procedure was to make storage space available. It was unnecessary to preserve every phone call, website, address and events but with the evolution of traditional systems to automated systems, organizations are more likely to save everything. The limitation of computer systems to store complex amount of information became a problem in IT system because the information age has brought about countless data than it did erstwhile and large database systems are not solution friendly as they are regularly generating data. More data, and information can be permanently acquired, stored and saved with the growth of computer storage capacity but however, the cost of saving and managing database systems with massive amounts of data is alarming.

Database technology for real-time analysis and control have made the management and preservation of data more efficient and effective with respect to relational database commonly used in commercial applications like customer relationship management systems which have enabled client’s details such as name, company, address, phone number and e-mail address to be stored. While industrial applications require the storage of tag name, measurement value and time stamp. A great advantage of the historian database is simply a massive production of data and historical data generation. Although, the production data is relatively simple but the point count is usually very large because the data processing ability of a relational database is smaller in comparison to a real-time and historian database. A database comparison study on an environmental protection management information system by Wellintech Inc., revealed a cut down in storage space by 25%through compression when the oracle based relational database systems’ data was converted and substituted into a process historian database. This occurred due to 90% space occupied by the oracle based relational database system hard disk. The study also revealed that the system had been operational for three years and held a huge amount of data which stored a lot of GPS information, maps, locations, time stamps, special maps information, locations and information on the management of the environmental monitoring system based on a GIS system.

The process historian database compresses data by multiple compression algorithm. It allows for accuracy of data loss within a certain range by users because only small portion tags or variable changes in value frequently and slowly. This changes occur in industrial production process field data often as waveform laws. Real-time/historical database technology is of utmost importance to organization as data compression process can help save massive amounts of space and aid query speed. Compression algorithm of change (0) is provided for any type of variable compression. It recognizes the time-out of compression and check for accuracy of the same value detection. It does not preserve a variable if nothing has changed. Thus, time and quality stamp should be thoroughly examined for any kind of compression algorithm. Dead banding compression algorithm is quite simple as it preserves data when the change in value reaches a certain gateway. For other variables, the actual production process experiences a slow change which can dramatically reduce the amount of stored data. However, the swinging door compression algorithm first proposed by OSI soft PI. Is relatively simple of which the whole algorithm was opened to the public after its adoption and technological optimization. Historian database process strongly hold compression algorithm for its uniqueness and principle, whereby it adjudicates whether a data point needs to be saved by drawing a straight line between data from a previous saved point P to the next data point N. It checks the absolute bias of the data points (including A) between two points and data points on that line corresponding to the time stamp. If there is prejudice of the point exceeding the compression bias, that point A is saved. Compression technologies are of utmost importance as it aids the reservation of storage space and enhance data querying speed.

A large amount of data is collected in industrial database from measurement and control hardware. A large sum of industrial communication protocols is used in many industries such as BACnet and LonWorks commonly used in HVAC systems, 102 protocol in power plants and Modbus in process control. This complex amount of data collection requires connectivity which is very important in intelligent systems. Data collection from hardware, storage of datato database and conversion of data into usable information for decision making are required in all industries for efficiency of intelligent analysis. Historian database analysis tools help compute things like how much water will be required to thoroughly clean a sewage in one month. This data is only accurate by its conversion into information using the historian database tools. It is also useful in future prediction of events or rather a clear estimation of uncertain past event. While these database process are effective in themselves, the relational database system are also great for commercial or smaller systems unlike the others which require vast amount of data which are used for industrial applications and some others for customer relationship management.