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DBMS ASSIGNMENT #1 

On TPS, MIS, ESS, EIS, Neural Systems, ArtificialIntelligence, Expert Systems 

Presented to Prof.

 Ja ha nz eb Is ma t 

Malhi

Presented By 

Muhammad Awais

Roll#MBAE2012093

IBA Punjab

University 

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1 - Executive Support System (ESS)

An Executive Support System (ESS) is the combination of IT-supported DSS for top

management and EIS. ESS is a comprehensive support system that goes beyond EIS to includecommunications, office automation, and analysis support. ESS is a strategic information

system designed for unstructured decision making through advanced graphics and

communications. ESS has an ability to move from summary to lower levels of detail (Drill

down). Executive has 24 hour per day ability to examine, control progress throughout

organization.

2- Executive Information system (EIS)

An executive information system (EIS) is a type of  management information system intended to

facilitate and support the information and decision-making  needs of senior executives by

providing easy access to both internal and external information relevant to meeting the

strategic goals of the organization. It is commonly considered as a specialized form of 

decision support system (DSS).

The emphasis of EIS is on graphical displays and easy-to-use user interfaces. They offer strong

reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-

level executives analyze, compare, and highlight trends in important variables  so that they

can monitor performance and identify opportunities and problems. EIS and data warehousing 

technologies are converging in the marketplace.

In recent years, the term EIS has lost popularity in favor of   business intelligence (with the sub

areas of reporting, analytics, and digital dashboards).

3- Transaction processing system (TPS) 

A Transaction Information Systems 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an electronic payment is made, the amount must be both withdrawn from one account and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

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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.

4 - Management Information System (MIS)

A management information system (MIS) provides information that is needed to manage

organizations efficiently and effectively. Management information systems  are not only

computer systems - these systems encompass three primary components: technology, people

(individuals, groups, or organizations), and data/information for decision making.

Management information systems are distinct from other information systems in that they are

designed to be used to analyze and facilitate strategic and operational activities in the

organization. Academically, the term is commonly used to refer to the study of how

individuals, groups, and organizations evaluate, design, implement, manage, and utilize

systems to generate information to improve efficiency and effectiveness of decision making,

including systems termed decision support systems, expert systems, and executive information

systems. Most business schools (or colleges of business administration within universities) have

an MIS department, alongside departments of accounting, finance, management, marketing,

and sometimes others, and grant degrees (at undergrad, masters, and PhD levels) in MIS.

5 - Decision support system (DSS)

A decision support system (DSS) is a computer-based information system that supports

business or organizational decision-making activities. DSSs serve the management, operations,

and planning levels of an organization and help to make decisions, which may be rapidly

changing and not easily specified in advance. Decision support systems can be either fullycomputerized, human or a combination of both.

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-

based system intended to help decision makers compile useful information from a

combination of raw data, documents, and personal knowledge, or business models to identify

and solve problems and make decisions.

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Typical information that a decision support application might gather and present includes:

  inventories of information assets (including legacy and relational data sources, cubes,data warehouses, and data marts),

  comparative sales figures between one period and the next,  Projected revenue figures based on product sales assumptions.

6 - Neural Network (NN)

The term neural network was traditionally used to refer to a network or circuit of   biological

neurons. The modern usage of the term often refers to artificial neural networks, which are

composed of  artificial neurons or nodes. Thus the term has two distinct usages:

1.  Biological neural networks are made up of real biological neurons that are connected or

functionally related in a nervous system. In the field of  neuroscience, they are often

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identified as groups of neurons that perform a specific physiological function inlaboratory analysis.

2.  Artificial neural networks  are composed of interconnecting artificial neurons(programming constructs that mimic the properties of biological neurons). Artificialneural networks may either be used to gain an understanding of biological neural

networks, or for solving artificial intelligence problems without necessarily creating amodel of a real biological system. The real, biological nervous system is highlycomplex: artificial neural network algorithms attempt to abstract this complexity andfocus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, lowgeneralization error), or performance mimicking animal or human error patterns, canthen be used as one source of evidence towards supporting the hypothesis that theabstraction really captured something important from the point of view of informationprocessing in the brain. Another incentive for these abstractions is to reduce theamount of computation required to simulate artificial neural networks, so as to allowone to experiment with larger networks and train them on larger data sets.

7 - Artificial intelligence

Artificial intelligence (AI) is the intelligence  of machines and the branch of  computer science 

that aims to create it. AI textbooks define the 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. John McCarthy, who coined the term in 1955, 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:

Computer simulation of the branching architecture

of the dendrites of  pyramidal neurons 

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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 centralproblems 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.

8 - Expert system

In 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 onevariable: 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".