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Transaction processing systemA transaction processing system is a type of information system. TPSs collect, store, modify, and retrieve the transactions of an organization. A transaction is an event that generates or modifies data that is eventually stored in an information system. It is recommended that a transaction processing system should pass the ACID test. The essence of a transaction program is that it manages data that must be left in a consistent state, e.g. if an electronic payment is made, the amount must be both withdrawn from one account and added to the other; it cannot complete only one of those steps. Either both must occur, or neither. In case of a failure preventing transaction completion, the partially executed transaction must be 'rolled back' by the TPS. While this type of integrity must be provided also for batch transaction processing, it is particularly important for online processing: if e.g. an airline seat reservation system is accessed by multiple operators, after an empty seat inquiry, the seat reservation data must be locked until the reservation is made, otherwise another user may get the impression a seat is still free while it is actually being booked at the time. Without proper transaction monitoring, double bookings may occur. Other transaction monitor functions include deadlock detection and resolution (deadlocks may be inevitable in certain cases of cross-dependence on data), and transaction logging (in 'journals') for 'forward recovery' in case of massive failures. Transaction Processing is not limited to application programs. For example, Journaling file systems also employ the notion of transactions. TypesContrasted with batch processingBatch processing is a form of transaction processing. Batch processing involves processing several transactions at the same time, and the results of each transaction are not immediately available when the transaction is being entered;[1] there is a time delay. Transactions are accumulated for a certain period (say for day) where updates are made especially after work. Online transaction processing is the form of transaction processing that processes data as it becomes available. Real-time and batch processingThere are a number of differences between real-time and batch processing. These are outlined below: Each transaction in real-time processing is unique. It is not part of a group of transactions, even though those transactions are processed in the same manner. Transactions in real-time processing are stand-alone both in the entry to the system and also in the handling of output. Real-time processing requires the master file to be available more often for updating and reference than batch processing. The database is not accessible all of the time for batch processing. Real-time processing has fewer errors than batch processing, as transaction data is validated and entered immediately. With batch processing, the data is organised and stored before the master file is updated. Errors can occur during these steps. Infrequent errors may occur in real-time processing; however, they are often tolerated. It is not practical to shut down the system for infrequent errors. More computer operators are required in real-time processing, as the operations are not centralised. It is more difficult to maintain a real-time processing system than a batch processing system. Intelligent decision support systemsIntelligent Decision Support Systems (IDSS) is a term that describes decision support systems that make extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history, indeed terms such as Knowledge-based systems (KBS) and intelligent systems have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston[1][2] in the late 1970s. Flexible manufacturing systems (FMS) [3], intelligent marketing decision support systems [4] and medical diagnosis systems [5] can also be considered examples of intelligent decision support systems. Ideally, an intelligent decision support system should behave like a human consultant; supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating the proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, whilst emulating human capabilities as closely as possible. Many IDSS implementations are based on expert systems[6], a well established type of KBS that encode the cognitive behaviours of human experts using predicate logic rules and have been shown to perform better than the original human experts in some circumstances.[7][8] Expert systems emerged as practical applications in the 1980s [9] based on research in artificial intelligence performed during the late 1960s and early 1970s.[10] They typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed), but performance can be poor when novel or uncertain circumstances arise. Some research in AI, focused on enabling systems to respond to novelty and uncertainty in more flexible ways is starting to be used in intelligent decision support systems. For example intelligent agents[11] [12] that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications.[13] Capabilities of these intelligent agents includeknowledge sharing, machine learning, data mining, and automated inference. A range of AI techniques such as case based reasoning, rough sets[14] and fuzzy logic have also been used to enable decision support systems to perform better in uncertain conditions. 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