1.0 Content

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1.0Content1

2.0Introduction 2-6

3.0Task 17-15

4.0Task 216-24

5.0Conclusion25

6.0References26

7.0Coursework27-33

2.0 IntroductionThe concept of the Information System Management has evolved over a period of time comprising many different facets of the organizational function. MIS is a necessity of all the organizations.The initial concept of Management Information System (MIS) was to process data from the organization and present it in the for of reports at regular intervals. The system was largely capable of handling the data from collection to processing. It was more impersonal, requiring each individual to pick and choose the processed data and use it for his requirements. This concept was further modified when a distinction was made between data and information. The information is a product of an analysis of data. This concept is similar to a raw material and the finished product. What are needed are information and not a mass of data. However, the data can be analyzed in a number of ways, producing different shades and specifications of the information as a product. It was, therefore, demanded that the system concept be an individual- oriented, as each individual may have a different orientation. Towards the information. This concept was further modified, that the system should present information in such a form and format that it creates an impact on its user, provoking a decision or an investigation. It was later realized then even though such an impact was a welcome modification, some sort of selective approach was necessary in the analysis and reporting. Hence, the concept of exception reporting was imbibed in MIS. The norm for an exception.Was necessary to evolve in the organization. The concept remained valid till and to the extent that the norm for an exception remained true and effective. Since the environment turns competitive and is ever changing, fixation of the norm for an exception becomes ka futile exercise at least for the people in the higher echelons of the organization. The concept was then evolved that the system should be capable of handling a need based exception reporting. This need maybe either of an individual or a group of people. This called for keeping all data together in such a form that it can be accessed by anybody and can be processed to suit his needs. The concept is that the data is one but it can be viewed by different individuals in different ways. This gave rise to the concept of DA eABASE, and the MIS based on the DATABASE proved much more effective.Over a period of time, when these conceptual developments were taking place, the concept of the end user computing using multiple databases emerged. This concept brought a fundamental charge in MIS. The change was decentralization of the system andthe user of the in formation becoming independent of computer professionals. When this becomes a reality, the concept of MIS changed to a decision making system. The job in a computer department is to manage the information resource and leave the task of information processing to the user. The concept of MIS in todayis world is a system which handles the databases, databases, provides com-putting facilities to the end user and gives a variety of decision making tools to the user of the system.The concept of MIS gives high regard to the individual and his ability to use information. An MIS gives information through data analysis. While analyzing the data, it relies on many academic disciplines. These include the theories, principles and concepts from the Management Science, Psychology and Human Behavior, making the MID more effective and useful. These academic disciplines are used in designing the MIS, evolving the decision support tools for modeling and decision - making.The foundation of MIS is the principles of management and if its practices. MIS uses the concept of management Information System can be evolved for a specific objective if it is evolved after systematic planning and design. It calls for an analysis of a business, management views and policies, organization culture and the culture and the management style. The information should be generated in this setting and must be useful in managing the business. This is possible only when it in conceptualized as system with an appropriate design. The MIS, therefore, relies heavily on the systems theory offers solutions to handle the complex situations of the input and output flows. It uses theories of communication which helps to evolve a system design capable of handling data inputs, process, and outputs with the lest possible noise or distortion in transmitting the information form a source to a destination. It uses the principles of system Design, Viz., an ability of continuous adjustment or correction in the system in line with the environmental change in which the MIS operates. Such a design help to keep the MIS tuned with the business managements needs of the organization.The concept, therefore, is a blend of principle, theories and practices of the Management, Information and System giving rise to single product known as Management Information System (MIS). The conceptual view of the MIS is shown as a pyramid in Fig.1.1.The Physical view of the MIS can be seen as assembly of several subsystems based on the databases in the organization. These subsystems range from data collection, transaction processing and validating, processing, analyzing and storing the information in databases. The subsystem could be at a functional level or a corporate level. The information is evolved through them for a functional or a department management and it provides the information for the management of business at the corporate level.The MIS is a product of a multi- disciplinary approach to the business management. It is a product which needs to be kept under a constant review and modification to meet the corporate needs of the information. It is prescribed product design for the organization. The MIS differs since the people in two organizations involved in the same business. The MIS is for the people in the organization. The MIS model may be the same but it differs greatly in the contents.The MIS, therefore, is a dynamic concept subject to change, time and again, with a change in the business management process. It continuously interacts with the internal and the external environment of the business and provides a corrective mechanism in thesystem so that the change needs of information are with effectively. The MIS, therefore, is a dynamic design, the primary objectively. The MIS, therefore, is a dynamic design the primary objective of which is to the information the information for decision making and it is developed considering the organizational fabric, giving due regard to the people in the organizational the management functions and the managerial and the managerial control.The MIS model of the organization changes over a time as the business passes through several phases of developmental growth cycle. It supports the management of the business in each phase by giving the information which is crucial in that phase. Every has critical success factors in each phase of growth cycle and the MIS model gives more information on the critical success factors for decision making.

3.0 Task 1(a) Information systems is an academic study of the complementary networks of hardware and software that people and organizations use to collect, filter, process, create and distribute data. Any specific information system aims to support operations, management and decision making. An information system is the information and communication technology (ICT) that an organization uses, and also the way in which people interact with this technology in support of business processes. Information systems typically include an ICT component but are not purely concerned with ICT, focusing instead on the end use of information technology. Information systems are also different from business processes. Information systems help to control the performance of business processes. An information system is a form of communication system in which data represent and are processed as a form of social memory. An information system can also be considered a semi-formal language which supports human decision making and action.There are several types of information systems, including the following common types:Operations support systems, including transaction processing systemsManagement information systemsDecision support systemsExecutive information systemsAn information system commonly refers to a basic computer system but may also describe a telephone switching or environmental controlling system. The IS involves resources for shared or processed information, as well as the people who manage the system. People are considered part of the system because without them, systems would not operate correctly. There are many types of information systems, depending on the need they are designed to fill. An operations support system, such as a transaction processing system, converts business data (financial transactions) into valuable information. Similarly, a management information system uses database information to output reports, helping users and businesses make decisions based on extracted data. In a decision support system, data is pulled from various sources and then reviewed by managers, who make determinations based on the compiled data. An executive information system is useful for examining business trends, allowing users to quickly access custom strategic information in summary form, which can be reviewed in more detail.

(b) The environment information system is designed to identify and promote the progress of environmental management. The system utilizes the Environmental Impact Information System to collect and process data on environmental impact and the Environmental Accounting System to collect and process data on environmental costs and effects. The collected data are processed and analyzed to identify Eco Balance; draw up environmental action plans; support decision-making in sustainable environmental management; promote environmentally-friendly design; improve activities by each division; process Corporate Environmental Accounting; and disclose information to the public. The Environmental Information Systems Program has a dual role of developing environmental information systems, services and tools that turn data into information and knowledge which also enable and deliver the land and water multi-disciplinary science. The systems and tools are integrate and use data, information and knowledge at local, national and international scales, provide platforms to deliver our land and water science, as both products and services, link to and leverage national platforms (such as TERN, IMOS, AuScope, NCI) and CSIRO initiatives. As well, the systems and tools provide an enhanced impact pathway for land and water science. These systems and tools are built using emerging best practice approaches using web and desktop technologies.(c) Business information systems (BIS) can be defined as systems integrating information technology, people and business. BIS is also an exciting, fast-changing field. BIS bring business functions and information modules together for establishing effective communication channels which are useful for making timely and accurate decisions and in turn contribute to organisational productivity and competitiveness. This paradigm shift leads to global outsourcing, strategic alliances and partnerships to be competitive in terms of price, quality, flexibility, dependability, responsiveness. IJBIS highlights new strategies, techniques, tools and technologies for developing suitable BIS.In today's world BIS professionals are playing an important role in enabling organisations to meet their strategic goals, driving business innovation, and assisting businesses to comply with increasingly complex legal requirements. An understanding of BIS is important to the work of all business professionals including executive managers who determine the organisation's strategic direction; information professionals who design and deliver new information services; accounting and financial managers who use information systems for financial management and business reporting; and marketing and sales managers who use.

(d) Similar to computer science, other disciplines can be seen as both related and foundation disciplines of IS. The domain of study of IS involves the study of theories and practices related to the social and technological phenomena, which determine the development, use, and effects of information systems in organization and society. But, while there may be considerable overlap of the disciplines at the boundaries, the disciplines are still differentiated by the focus, purpose, and orientation of their activities.In a broad scope, the termInformation Systemsis a scientific field of study that addresses the range of strategic, managerial, and operational activities involved in the gathering, processing, storing, distributing, and use of information and its associated technologies in society and organizations.The term information systems is also used to describe an organizational function that applies IS knowledge in industry, government agencies, and not-for-profit organizations.Information Systemsoften refers to the interaction between algorithmic processes and technology. This interaction can occur within or across organizational boundaries. An information system is the technology an organization uses and also the way in which the organizations interact with the technology and the way in which the technology works with the organizations business processes. Information systems are distinct frominformation technology(IT) in that an information system has an information technology component that interacts with the processes' components.Businessprocesses play a major role in many commercial software systems and are of considerable interest to the research communities in Software Engineering, and Information and SystemSecurity. A process-aware information system provides support for the specification, execution, monitoring and auditing of intra- as well as cross-organizational business processes.Designing and enacting secure business processes is as tricky as "Programming Satan'sComputer", as Ross Anderson and Roger Needham observed in a paper with that title. Recent fraud disasters show how subtle secure process engineering and control can be. The Swiss bankUBSsuffered from a rogue trader scandal in 2011, which led to a loss of a then-estimated US$2 billion, was possible because the risk of trades could be disguised by using "forward-settling" Exchange-traded Funds (ETF) cash positions. Specifically, processes that implemented ETF transactions in Europe do not issue confirmations until after settlement has taken place. The exploitation of this process allows apartyin a transaction to receive payment for a trade before the transaction has been confirmed. While the cash proceeds in this scheme cannot be simply retrieved, the seller may still show the cash on their books and possibly use it in further transactions. Eventually, the mechanics of this attack allowed for a carrousel of transactions, thereby creating an ever growing snowball. Similar analyses, usually based upon insider threats, can also be made for fraud cases such as the well-documented Socit Gnrale case, but also for the WorldCom and Parmalat cases.Addressing these problems requires, on the one hand, strong security and compliance guarantees. On the other hand, these guarantees must be substantiated by formal methods ensuring a verifiably secure business process enactment. It should be noted that these concerns are not confined to the financial service sector or to insider threats. For example, the planned unification of Eurpean data protection law into a sole Data Protection Regulation law is likely to change the statuatory duties of the private sector. Under this plan, companies will be legally required to report any breaches of this regulation and may be liable to penalties in the range of 2--5% of their global annual turnover. European industries seem to be ill-prepared to ensure that their information systems and processes will comply with the security requirements of that upcoming regulation, and the threat of substantial fines means that there is an urgent need to createmoreresilient systems and processes, which calls for more research within the thematic scope of this seminar.At theinterfaceof security requirements, business needs, and compliance methodologies we can ask many practically relevantresearchquestions, and their answers are bound to have significant impact in academia and industry alike. Relatively little work has been done, however, on adapting or creating new formal methods with which one can check that processes are compliant with rules, preserve demanded privacy constraints,andenforce desired security policies at the same time.One main purpose of the seminar was to present the state of theartin research within the three communities of Security, Verification, and Process-Aware Information Systems to all three communities in an accessible manner and with a view of identifying important research topics at the intersections of these communities. In addition, that exercise was also meant to explore what strategic activities could help in promoting research at the junction of these communities. This agenda was persued through a mix of keynotes, technical presentations, break-out groups under the WorldCafe method, sessions with free-style discussions, and tool demonstrations.We now highlight some of the key questions and findings can emerged during that week of work - we refer to the online archive of presentation slides, papers, and abstracts for more detailed discussions and findings. Three action items that seemed of particular importance to the participants were.Finally, it was also noted that some of the research problems that suggest themselves to the specialists may not be issues in the field. For example, we may want trusted system composition across organizations but there may not be the need to formally validate such trust since contractual or other legal mechanisms may be in place that incentivize parties to honor that trust, and that give parties a means of seeking damages in case that trust has been violated. On the other hand, such legal mechanisms may not be adequate in the upcoming Internet of Things were 2-party, end-to-end composition will be the exception and not the norm.

4.0 Task 2.1 Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is an academic field of study which studies the goal of creating intelligence. Major AI researchers and textbooks define this field as "the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. AI research is highly technical and specialized, and is 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. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI.AI also important in corporate world. Lets consider some of the recent advances in building AI scientists. In 2009, Adam became the first robot to discover new scientific knowledge, having to do with the genetics of yeast. The robot, which consists of a small room filled with experimental equipment connected to a computer, came up with its own hypothesis and tested it. Though the context and the experiment were simple, this milestone points to a new world of robotic possibilities. This is where the intersection between AI and other transhumanist areas, such as life extension research, could become profound.Many experiments in life science and biochemistry require a great deal of trial and error. Certain experiments are already automated with robotics, but what about computers that formulate and test their own hypotheses? Making this feasible would require the computer to understand a great deal of common sense knowledge, as well as specialized knowledge about the subject area. Consider a robot scientist like Adam with the object-level knowledge of the Jeopardy!-winning Watson supercomputer. This could be built today in theory, but it will probably be a few years before anything like it is built in practice. Once it is, its difficult to say what the scientific returns could be, but they could be substantial. Well just have to build it and find out. That concludes this brief overview. There are many other interesting trends in AI, but machine vision, cognitive prostheses, and robotic scientists are among the most interesting, and relevant to futurist goals. The importance of artificial intelligence is the ability to create a never-ending thought process and collective that could solve our problems. Accomplishing this by thinking of every possible solution. We are limited now by the number of people who can do this. With artificial intelligence, we could build computers, upon thousands of computers, that could all work in unison to solve our great and most dire problems. One example is global warming. Whether you believe we are the cause or not, the fact is that global temperatures are on the rise. We need a way out or around or an idea to slow the process down. We need something, Artificial intelligence could, and should solve this faster than we are or could.

Task 2.2A neural network is designed to simulate a set of neurons, usually connected by synapses. Each neuron makes a simple decision based on its other input synapses, and places the decision on its output synapses. This model mimics the behavior of a brain, and is considered vital to create a true learning system, though modern computers (barring super-computers) do not have the computational resources to execute a neural network with a sufficient number of nodes to be useful (you would need at least a few million neurons firing in unison to be useful). Artificial intelligence, of course, is software that is designed to pretend like its a living, thinking creature. Older implementations were not learning systems, but rather would take input and offer a conditioned response provided by the programmer ahead of time. These systems seemed to be highly intelligent, so long as you did not leave its realm of preplanned responses. Newer AI systems learn by interacting with the user (for example, remembering their favorite color or music artist), and can sometimes even figure out correlated data based on this information.

However, current AI systems tend to still have limited spheres of knowledge, and without external learning sources, can not make any intelligent responses or decisions outside this realm of information. The missing component, of course, is a system that is capable of learning information and incorporating what it learns into its current knowledge base. Neural networks hold the promise of bridging this gap in the "learning curve" that AI systems have by allowing the AI to actually learn topics that were not covered during its original "training" or "programming."The relationship between these two technologies could be said to be symbiotic in nature; both of these can be implemented without the other (i.e. a NN could be used inside a coffee maker for some advanced coffee-making logic, and an AI can certainly use other sources of information to make valid responses), but the combination of the two would allow for a more realistic AI that would be capable of learning data by making correlations between seemingly unrelated data (which is how humans learn, coincidentally).

Task 2.3Understanding user habits allows companies to make intelligent recommendations, such as if someone regularly goes for a movie after dinner on Sunday evenings, a mobile app can recommend movies or related services every Sunday. E-commerce sites such as Amazon recommend products similar to what the user has just viewed or purchased using collaborative filtering, by looking at social networks of the user and filtering through products to find ones similar to what the user has bought. One challenge of analysing social media data is the sheer diversity of data available. There can also be a lot of complexity in location-based data. One example is that you may be located in a hotel, but you may bein a meeting room rather than a guest room. This level of information exceeds what can be told by a GPS.Applications of these types of virtual robots who can act like a human being behind-the-scenes and interact with people intelligently, has a wide range of applications, such as a teaching assistant providing information to students to help them learn.Spammers who send out messages to the masses for commercial gain negatively affects user experience. Social media sites and commercial websites may rely on user complaints to a call centre to follow-up and discover spammers. By mining data from the social activities of users, it is possible to discover spammers because they hide within networks of users. By combining this knowledge of social connections with data from the domain side, a filtering system can be built to reduce false alarms and increase success rates of catching spammers at call centres.In 1984, the University of Minnesota'sCollege of Educationand Wilson Learning Corporation created the Alliance for Learning to support a variety ofresearchprojects focused on developing new areas of knowledge about adult learning and new technologies for deliveringtrainingand education. This paper describes an Alliance project exploring the application of expert systems in the area of sales and sales management. The first part of the project involved the development of an expert system shell that would allow maximum flexibility and provide the capability to address a wide variety of individual performance issues. The second part involved developing a knowledge base derived from Wilson sales training materials and sales management subject matter experts. The outcome of the project was the development of an experimental expert system called "The Sales Coach." It is designed as a tool to allow sales managers to assess the individual needs of their sales staff and generate individualized development plans for improving performance. While the existing knowledge base represents generic Wilson sales training content, it is highly tailorable, allowing for complete customization. This document describes the research perspective that influenced the design of the shell and the knowledge base the method used to develop both the specific features of the resulting system; and the significance of the system in the larger context of education and training in business and industry. Samples of computer screens from the expert system are included. (GL) An intelligent system contains knowledge about some domain; it has sophisticated decision-making processes and the ability to explain its actions. The most important aspect of an intelligent system is its ability to effectively interact with humans to teach or assist complex information processing. Two intelligent systems are Intelligent Tutoring Systems (ITs) and Expert Systems. The ITSs provide instruction to a student similar to a human tutor. The ITSs capture individual performance and tutor deficiencies. These systems consist of an expert module, which contains the knowledge or material to be taught; the student module, which contains a representation of the knowledge the student knows and does not know about the domain; and the instructional or teaching module, which selects specific knowledge to teach, the instructional strategy, and provides assistance to the student to tutor deficiencies. Expert systems contain an expert's knowledge about some domain and perform specialized tasks or aid a novice in the performance of certain tasks. The most important part of an expert system is the knowledge base. This knowledge base contains all the specialized and technical knowledge an expert possesses. For an expert system to interact effectively with humans, it must have the ability to explain its actions. Use of intelligent systems can have a profound effect on human resources. The ITSs can provide better training by tutoring on an individual basis, and the expert systems can make better use of human resources through job aiding and performing complex tasks. With increasing training requirements and "doing more with less," intelligent systems can have a positive effect on human resources.

5.0 ConclusionMIS differ from regular information systems because the primary objectives of these systems are to analyze other systems dealing with the operational activities in the organization. In this way, MIS is a subset of the overall planning and control activities covering the application of humans, technologies, and procedures of the organization. Within the field of scientific management, MIS is most of ten tailored to the automation or support of human decision making. Management information systems (MIS) make it possible for organizations to get the right information to the right people at the right time by enhancing the interaction between the organizations people, the data collected in its various IT systems, and the procedures it uses. It brings together the raw data collected by the various business areas of the organization, which, while useful for specific functions such as accounting, does not provide, by itself, information that can be used to make decisions. As organizations grow, MIS allows information to move between functional areas and departments instantly, reducing the need for face-to-face communications among employees, thus increasing the responsiveness of the organization.

6.0 References1. Textbook BBA10092. http://www.oum.edu.my/oum/v3/download/CBAD2103.pdf3. http://en.wikipedia.org/wiki/Information_system4. https://www.ricoh.com/environment/management/info_system.html5. http://www.csiro.au/Organisation-Structure/Divisions/Land-and-Water/Environmental-Information-Systems.aspx6. http://www.inderscience.com/jhome.php?jcode=IJBIS7. http://en.wikipedia.org/wiki/Artificial_intelligence8. http://www.answers.com/Q/What_is_the_importance_of_artificial_intelligence9. http://www.answers.com/Q/Relationship_between_artificial_intelligence_and_neural_networks_with_help_of_a_scenario10. https://sites.google.com/site/assignmentssolved/mca/semester4/mc0076/5

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