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Transaction processing systems
One of the first transaction processing systems was American Airline SABRE system, which became operational in 1960. Designed to process up to 83,000 transactions a day, the system ran on two IBM 7090 computers. SABRE was migrated to IBM System/360 computers in 1972, and became an IBM product first as Airline control Program (ACP) and later as Transaction Processing Facility (TPF). In addition to airlines TPF is used by large banks, credit card companies, and hotel chains. The Hewlett-Packard NonStop system (formerly Tandem NonStop was a hardware and software system designed for Online Transaction Processing (OLTP) introduced in 1976. The systems were designed for transaction processing and provided an extreme level of availability and data integrity. Transaction processing is a style of computing that divides work into individual, indivisible operations, called transactions.A transaction processing system (TPS) or transaction server is a software system, or software/hardware combination, that supports transaction processing. ·17Knowledge management systems

Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices. An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy.

Many large companies and non-profit organizations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organizations. Knowledge management efforts typically focus on organizational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organization. KM efforts overlap with organizational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. It is seen as an enabler of organisational learning and a more concrete mechanism than the previous abstract research.

·18Expert system and artificial intelligence
an expert system is a computer system that emulates the decision-making ability of a human expert.Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in conventional programming.The first expert systems were created in the 1970s and then proliferated in the 1980s.Expert systems were among the first truly successful forms of AI software.An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human.In the 80s a third part appeared: a dialog interface to communicate with users.This ability to conduct a conversation with users was later called “conversational”. Artificial intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it.

AI textbooks define the field as “the study and design of intelligent agents”[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.John McCarthy, who coined the term in 1956,[3] defines it as “the science and engineering of making intelligent machines.” AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other.Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. There are subfields which are focused on the solution of specific problems, on one of several possible approaches, on the use of widely differing tools and towards the accomplishment of particular applications.

The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.General intelligence (or “strong AI”) is still among the field’s long term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth, fiction and philosophy since antiquity.Artificial intelligence has been the subject of optimism,but has also suffered setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

Enterprise resource planning systems
Enterprise resource planning (ERP) systems integrate internal and external management information across an entire organization, embracing finance/accounting, manufacturing, sales and service, customer relationship management, etc. ERP systems automate this activity with an integrated software application. The purpose of ERP is to facilitate the flow of information between all business functions inside the boundaries of the organization and manage the connections to outside stakeholders.ERP systems can run on a variety of computer hardware and network configurations, typically employing a database as a repository for information.

E-commerce systems
Electronic commerce, commonly known as e-commerce, is the buying and selling of product or service over electronic systems such as the Internet and other computer networks. Electronic commerce draws on such technologies as electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. Modern electronic commerce typically uses the World Wide Web at least at one point in the transaction’s life-cycle, although it may encompass a wider range of technologies such as e-mail, mobile devices and telephones as well. Electronic commerce is generally considered to be the sales aspect of e-business. It also consists of the exchange of data to facilitate the financing and payment aspects of business transactions. E-commerce can be divided into:

E-tailing or “virtual storefronts” on Web sites with online catalogs, sometimes gathered into a “virtual mall” The gathering and use of demographic data through Web contacts and social media Electronic Data Interchange (EDI), the business-to-business exchange of data E-mail and fax and their use as media for reaching prospects and established customers (for example, with newsletters) Business-to-business buying and selling

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