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Model-Driven Architecture (MDA) is a framework for software development processes that is at the core of the Object Management Group's (OMG) recommendations:

There are three models in MDA:
1) Computation-Independent Model (CIM)
The CIM combines the requirements and domain models. It models the system in terms of how it will interact with its environment.

2) Platform-Independent Model (PIM)
The PIM is the design model. It describes the internal structure of the model without regard to the hosting platform.

3) Platform-Specific Model (PSM)
The PSM is the implementation model. It adds concepts from the hosting platform to the hosting platform. Examples of platforms include .Net, J2SE, J2ME, J2EE, CORBA.

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EIS (Enterprise Information systems) (Lee Kuan Lun)

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EIS (Enterprise Information systems) (Lee Kuan Lun)

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Enterprise Information systems, systems which look to improve the functions of an enterprise business processes by integration of many systems.  Various higher education enterprise systems include admissions, alumni, decision Support, executive, ERP, human resources, integrated systems, procurement, registration, student enrollment, and student information systems.

An EIS can be used to increase business productivity and reduce service cycles, product development cycles and marketing life cycles. It may be used to amalgamate existing applications. Other outcomes include higher operational efficiency and cost savings.

Common types of EIS
- Automated billing systems
- Payment processing
- Email marketing systems
- Customer Relationship Management (CRM)
- Enterprise Resource Planning (ERP)
- Business Intelligence (BI)
- Business Continuity Planning (BCP)
- Enterprise Application Integration (EAI)
- Enterprise Content Management
- Enterprise search
- Enterprise Messaging Systems (EMS)
- Call center and customer support
- HR Management

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-  is a system for applying and using knowledge management principles. These include data-driven objectives around business productivity, a competitive business model, business intelligence analysis and more.
- Knowledge management systems can help with staff training and orientation, support better sales, or help business leaders to make critical decisions.
Tools:
- Content Repository
- Knowledge Search
- Communication Tool
- Social Software
- knowledge Visualization
- Decision Support
- Big Data

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Groupware & meeting tools (Nurul Amalina)

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Groupware & meeting tools (Nurul Amalina)

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Groupware is a technology to help a group of people work together on a shared task. Its allow users to use it at the same or different time, and at the same or different places. Business team often use groupware to conduct a meeting whenever they are in the different places. They also use groupware to share information  so that team members can benefit from each others work, avoid duplication of tasks, and avoid making mistakes that others have already made.
Example of groupware tools: Microsoft Sharepoint, Monday.com
(https://dspace.mit.edu/bitstream/handle/1721.1/48021/groupwarekeytoma00bull.pdf;sequence=1)

Meeting tool is a tool for
creating online meetings, webinars, and video conferences. It saves business teams from travelling, saves a lot of time, and allows quick collaboration. They also can share documents using meeting tools. Example of meeting tools: OpenMeetings, Yugma

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(Nur Syafiqah)
E-commerce is the process of buying and selling of goods and services across the Internet.
It is one of the ways to exchanging the information between individuals and companies by
combining a range of processes, such as electronic mail, electronic data interchange (EDI),
Electronic Fund Transfer (EFT), World Wide Web and other Internet based applications.
The basic objective of DSS based E-Commerce Model is to help the customer purchasing
goods from the Web stores on the Internet.The basic idea behind the model is to simplify
the presentation of products and at one time show information about only one product.
This model is based on the concept that “keep it simple as possible you can” while
representing the information to the customer.
Who will wins and who will loses that depends on how businesses represent information on their web stores.
Web store home page is the first impression that online businesses make in the mind of their web customers.
Whether the category of business is B2B, B2C or C2C, businesses should make every effort to present the
information to the visitors with attractive, easy to use page and that gives customers quickly access
to the information needed by them.

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(Navin)
An example of a successful knowledge management strategy is often focused upon the introduction of a knowledge management Portal or company intranet. One widely cited example is the case of Buckman Laboratories, where an electronic database and discussion forum was implemented in 1992. The portal allowed staff to access the stored information of the organisation and employee knowledge across the company, and this system was purported to have led to a 21% increase in sales of new products (O’Dell & Jackson Grayson, 1998). It is recommended that practitioners, who are charged with introducing a knowledge management portal, must firstly determine the underlying concepts and terminology of the organisation before proceeding to implement collaborative tools.
In an ideal scenario, a knowledge management portal should offer a rich and complex shared information work space for the creation, exchange, retention, and reuse of knowledge.

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(Ooi Hui Yin)
Pattern recognition - automated recognition of patterns in data. In pattern recognition, we find similarities among the decomposed problems to solve more complex problems more effectively.
The regularities in data are discovered automatically and then the data is classified into several categories.
Approach of pattern recognition include machine learning and heuristic.
Some examples of pattern recognition application:
-identification of spam/ non-spam email messages
- OCR (optical character recognition) which converts typed/ handwritten text into machine-encoded text.

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Computer aided software engineering tools are software tools used as part of the software development processes to develop high-quality, defect-free and maintainable software.

Examples of CASE tools include diagram tools, documentation tools, process modeling tools, analysis and design tools, system software tools, project management tools, design tools, prototyping tools, configuration manage tools, programming tools, web development tools, testing tools, maintenance tools, quality assurance tools, database management tools and re-engineering tools.

Who to use?
In projects involving a large amount of coding or high complexity, the standardisation provided by using CASE tools allows easier debugging. Therefore, the larger the project, the more important it becomes to use CASE tools in the software's development.

Why use?

The use of CASE tools is recommended because they:
(a) Maximize the consistency between the design and the implementation
(b) Minimize human error incurred by translating the design into source code
(c) Shorten the time spent on the development of source code
(d) Facilitate the implementation of change control procedures

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2 – Iteration Is Key—a GA Learns and Improves Over Time

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Cloud Business Intelligence (Sim Jia Wei)

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Cloud Business Intelligence (Sim Jia Wei)

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Grid computing is a processor architecture that combines computer resources from various domains to reach a main objective. In grid computing, the computers on the network can work on a task together, thus functioning as a supercomputer.

Typically, a grid works on various tasks within a network, but it is also capable of working on specialized applications. It is designed to solve problems that are too big for a supercomputer while maintaining the flexibility to process numerous smaller problems. Computing grids deliver a multiuser infrastructure that accommodates the discontinuous demands of large information processing.

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IoT is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

IoT can be linked with business intelligence (BI), providing the possibility to analyze data and get valuable information.

Example of IoT solutions:

1) Predictive analytics which can add real value to business. IoT will be responsible for collecting large data volumes and tracking things, while BI part will go to processing data and making smart predictions.

2) Prescriptive analytics. The core thing here is to make right and on-time recommendations (so-called prescriptions), thus allowing to improve decision-making, save costs, and avoid failures.

Advantages of connecting IoT and BI

1) Improve anomaly detection. The tool will notify about any kinds of changes and suspicious activities that take place during every 24 hours. If there are some anomalies and other things that are hard to detect, the tool can also use an integrated Machine Learning module, that will easily catch them up.

2) Provide businesses with comprehensive monitoring statistics, real-time data calculates user-defined KPIs (Key Performance Indicators), always keeps you updated on your strengths and weaknesses.

3) Generate accurate individually visualized reports, that can schedule according to the company needs.

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Definition : a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction

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KMS (Knowledge Management System)- Lukman

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KMS (Knowledge Management System)- Lukman

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Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, considered to be "fuzzy". By contrast, in Boolean logic, the truth values of variables may only be 0 or 1, often called "crisp" values. Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false, for example, the result of a comparison between two things could be not "tall" or "short" but ".38 of tallness." Furthermore, when linguistic variables are used, these degrees may be managed by specific (membership) functions.
Many of the early successful applications of fuzzy logic were implemented in Japan. The first notable application was on the subway train in Sendai in which fuzzy logic was able to improve the economy, comfort, and precision of the ride. It has also been used in recognition of hand written symbols in Sony pocket computers, flight aid for helicopters, controlling of subway systems in order to improve driving comfort, precision of halting, and power economy, improved fuel consumption for automobiles, single-button control for washing machines, automatic motor control for vacuum cleaners with recognition of surface condition and degree of soiling, and prediction systems for early recognition of earthquakes through the Institute of Seismology Bureau of Meteorology, Japan.

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The enterprise information portal (EIP), also known as a business portal, is a concept for a Web site that serves as a single gateway to a company's information and knowledge base for employees and possibly for customers, business partners, and the general public as well.

An EIP is made up of these elements: access/search, categorization, collaboration, personalization, expertise and profiling, application integration, and security.

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Group decision support system(GDSS) technology supports project collaboration through the enhancement of digital communication with various tools and resource. Many decisions in an organization require the collaboration and participation of multiple individuals so use of GDSS can facilitate the decision making process

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Definition :
Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.

Example : Image Recognition
Learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge about cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process.

CNN is another specific type of ANN that uses perceptrons, a machine learning unit algorithm that is for supervised learning that analyze data. It is also applies to image processing, natural languages processing & other cognitive tasks. CNN also was trained to perform multi class multi label classification by using power data from MISO. The result of CNN and big data analysis in power system can be seen with the accuracy 90% for the utility of TensorFlow

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DSS (DECISION SUPPORT SYSTEM) by bibi asha

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DSS (DECISION SUPPORT SYSTEM) by bibi asha

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DSS is a computer-based information systems ( collection of integrated software apps and hardware) that supports business/organization decision making activity.
> Support managers/ business professionals to make decisions
> may be rapidly charging and not easily specified in advance

3 Quantitative Models typically used by DSSs;
1.Sensitivity analysis  - the study if the impact that changes in one or more parts of the model have on other parts of the model
2. What-if analysis - checks the impact of a change in an assumption on the proposed solution
3. Goal-seeking analysis - finds the inputs necessary to achieve goal such as a desired level output
Advantage of DSS in decision-making
- save time
- enhance  effectiveness
- competitive advantage
- reduce cost
- improve decision making satisfaction

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Higher-level procedure or heuristic designed to find, generate, or select a partial search algorithm that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
Metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems.

Properties that characterize most metaheuristics:

-Metaheuristics are strategies that guide the search process.
-The goal is to efficiently explore the search space in order to find near–optimal solutions.
-Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.
-Metaheuristic algorithms are approximate and usually non-deterministic.
-Metaheuristics are not problem-specific.

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- The ability of a computer program to understand human language as it is spoken.
- A component of artificial intelligence

Uses of NLP
- Most of the research being done on natural language processing revolves around search especially enterprise search.
- Involves allowing users to query data sets in the form of a question that they might pose to another person. The machine interprets the important elemens of the human language sentence, such as those that might correspond to specific features in a data set and returns an answer.
-Interpret free text and make it analyzable

How it works?
- Based on deep learning : require massive amounts of labelled data to train on and identify relevant correlation and assembling this kind of big data set is one of the main hurdles to NLP

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Data mining is the process of discovering patterns in large data sets with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.
Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign. In contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data.

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Supply Chain Management

-The scm is all about to fullfil demand of customers.
- To improve the supply chain the company with suppliers.
- by using the system, the company can identify what needed that they need to supply to their company in a real time.

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by Tan Chong Gee (128399)
Definition:
The field of study interested in the development of computer algorithms to transform data into intelligent action is known as machine learning. This field originated in an environment where available data, statistical methods, and computing power rapidly and simultaneously evolved.
Categories of ML:
1. Supervised Learning

Supervised learning learns from a set of labelled data and generate a model which is capable of label unknown dataset based on the generalization of the labelled dataset. Examples of supervised learning include regression and classification.

2. Unsupervised Learning
Unsupervised Learning extracts knowledge/patterns from data without labelled data.

Algorithms such as clustering can be applied on unlabelled dataset to cluster different clusters of data based on the attributes of the data.

Applications of ML in BI:
1. Market Basket Analysis - Association Rule Learning is used to know what combination of products are frequently purchased together. Knowing the trend will help with optimal product placement and thus increase sales by reducing purchasing friction.
2. Fraud Analysis - Banks often use ML algorithms to detect potential fraud transactions for customers thus preventing further losses by customer.

3. 24-h realtime customer support - Virtual assistant or chatbot is widely used by companies to handle basic inquiries, orders, and so on from customers from all around the world in this globalised economy. Examples: Facebook Messenger Bot.
(Further Reading: https://www.thoughtspot.com/data-chief/5-ways-machine-learning-can-make-your-bi-better)

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OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling.

OLAP transforms raw data so that it reflects the real dimensionality of the enterprise as understood by the user. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.

It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting.

Multidimensional views are inherently representative of an actual business model. Rarely is a business model limited to fewer than three dimensions. Managers typically look at financial data by scenario (for example, actual versus budget), organization, line items, and time; and at sales data by product, geography, channel, and time.

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The enterprise information portal (EIP), also known as a business portal, is a concept for a website that serves as a single gateway to a company's information and knowledge base for employees and possibly for customers, business partners, and the general public as well.

In one model, an EIP is made up of these elements: access/search, categorization, collaboration, personalisation, expertise and profiling, application integration, and security.

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What is genetic algorithm?
It is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.

Usage of genetic algorithm in the application
The most popular application of Genetic Algorithms is in the field of Search applications.

Mostly, in such search techniques, the end objective may be an optimization function where the genetic algorithm may be applied for Fast Search through a huge pool of possible solutions so as to find the most optimal solution fast, without compromising the fitness of purpose too much.

Similarly, problems which are often solved using genetic algorithms include time-table scheduling and job-scheduling problems. Many scheduling software packages use Genetic Algorithms as a predictor model. GAs have also been applied to classic engineering disciplines besides the information technology, communications, telecommunications, electronics and semiconductor industry. Genetic algorithms are also popular for usage as an approach to solve global optimization problems.

Key ideas of Genetic Algorithm:
1 – Genetic Algorithms (GA) Can Optimize Solutions in Minutes
2 – Iteration Is Key—a GA Learns and Improves Over Time

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ERP -enterprise resource planning
-Integrate all core processes needed to run a company: finance, HR, manufacturing, supply chain, services, procurement, and others into a single system.
-use the latest technologies – such as machine learning and AI – to provide intelligence, visibility, and efficiency across every aspect of a business.
Benefits:
-better insight
-accelerated reporting
-lower risk
-simpler IT
-improved agility

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Hybrid Support Systems (HSS) represent the systems that are the result of integrating DSS with other tools and technologies for decision supporting in order to maximize the efficiency and efficacy of organizational decision-making process.The implementation of HSS in the management of organizations oriented towards Business Intelligence is very expensive and complex. The complexity and the high price of this kind of implementation come from the heterogeneous technologies and frameworks involved, and the hardware resources needed.

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GSS

GSS is Group Support System, which is also known as GDSS(Group Decision Support System). It supports project collaboration through the enhancement of digital communication with various tools and resources. These types of programs are used to support customized projects requiring group work, input to a group and various types of meeting protocols. Group decision support system software tools helps the decision makers in organizing their ideas, gathering required information and setting and ranking priorities. Examplesare ThinkTank and MeetingWorks.

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ERM (enterprise resource management) describes software that lets an enterprise manage user access to its network resources efficiently. ERM software generally lets a user sign on to different enterprise systems and applications using the same password. ERM software makes it easy for the enterprise to control and keep track of which systems and resources each user has access to, and provides consistent standards for creating and changing passwords.

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Management science (MS) is the broad interdisciplinary study of problem solving and decision making in human organizations, with strong links to management, economics, business, engineering, management consulting, and other sciences. It uses various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms to improve an organization's ability to enact rational and accurate management decisions by arriving at optimal or near optimal solutions to complex decision problems. Management sciences help businesses to achieve goals using various scientific methods.
Some of the fields that management science involves include: data mining, decision analysis, forecasting, simulation as well as many other more.

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Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.

Analytic Tools & Techniques

1.) Excel
- An MS office tool for data manipulation and visualization.
- Pivots for quick analysis, charting, filtering and visualizing data, presenting the recommendations, output

2) R Programming
- Running linear, logistic regression, neural nets, random forest, basic statistical analysis and any other machine learning algorithms

3) Statistics
Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation, and presentation of data.
-It forms fundamentals of many machine learning algorithms. Hence to understand the output and make sense out of it, basic statistical learning is mandatory

4) Linear Regression
Linear regression is used to model a linear relationship between an outcome variable, y, and a set of predictor variables x1, x2, etc. Thus, it is a supervised learning technique.
-Uses of Regressions: Regressions are commonly used in the machine learning field to predict continuous value. However, Regression task can predict the value of a dependent variable based on a set of independent variables.
-Real Application: Predicting stock prices based on company performance, historical prices, etc.

5) Logistic Regression
-Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. It is used when you want to solve a binary classification problem
-Real Applications in Industries: Predicting if an email is a spam or no

6) Time Series Modelling
-Time series analysis is a statistical technique that deals with time series data, or trend analysis.  Time series data means that data is in a series of particular time periods or intervals. Thus, it is used when you have time series data, that is data with respective to specific time durations.
-Real Applications in Industries: Predicting stock prices

7) Market Basket Analysis
-Association rule mining is a procedure which aims to observe frequently occurring patterns, correlations, or associations from datasets found in various kinds of databases such as relational databases, transactional databases, and other forms of repositories. Hence, it is useful whenever you need to find frequent occurring patterns within a data.
-Real Applications in Industries: Recommendations by YouTube on videos that you may like

8) Decision Trees
-Decision tree is a type of supervised learning algorithm (having a pre-defined target variable). It is mostly use in classification problems. It works for both categorical and continuous input and output variables.
-Real Applications in Industries: Identify which demographic segment a customer belongs to

9) Clustering (K means)
-Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). In fact, it is an unsupervised algorithm. It is useful whenever you required to club similar entities together based on some characteristics
Real Applications in Industries: Identifying groups of similar outlets/stores

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Ooi Lim Seong Liang - SVM (Support Vector Machine)

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Ooi Lim Seong Liang - SVM (Support Vector Machine)

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Support Vector Machine (SVM) is a type of supervised machine learning algorithm which can be used for classification (given a data point, determine what class it belongs to) and regression (model the data points and forecast/predict the next data points) problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. The coordinates of the data points / observations are called support vectors while the boundary which best segregates the classes are called hyperplane / hyperline.

For business intelligence, SVM can be used as an algorithm to classify the products / customers based on the required information. For example, SVM can be used by financial companies and banks to predict whether a person is qualified for a loan or not based on previous credit history and income status. SVM can also be used in data mining and text mining to aid the tasks, for example in classifying whether customer feedbacks are generally positive or negative, and what categories/aspects are the customers most concerned about, so that the company can make improvement accordingly.

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Based on artificial Intelligence algorithms, Expert System may allow enterprises to stay competitive in a world that requires ever faster processing of increasingly diverse, high volume information. Expert system uses intelligent technology and applications that provide an accurate, automatic and immediate understanding of text.

Characteristics of Expert Systems

High performance

Understandable

Reliable

Highly responsive

Applications of Expert System
- Design Domain
- Medical Domain
- Knowledge Domain
- Monitoring Systems
- Process Control Systems
- Finance/Commerce

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Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers and other corporate end user to make informed business.

Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals and directions at the broadcast level.

Business Intelligent applications use data gathered from data warehouse or from a data mart. The data included historical, current, and predictive views of business operations.

Examples of BI Software

- Sisense
- Looker
- Datapine
- Zoho Analytics
- Yellowfin

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Customer-relationshp management(CRM) is an approach to manage a company's interaction with current and potential customers. It uses data analysis about customers' history with a company to improve business relationships with customers, specifically focusing on customer retention and ultimately driving sales growth.
Types of CRM:
Operational CRM streamlines the business process that includes Sales automation, Marketing automation and Service automation. Main purpose of this type of CRM is to generate leads, convert them into contacts, capture all required details and provide service throughout customer lifecycle.
Analytical CRM helps top management, marketing, sales and support personnel to determine the better way to serve customers. Data analysis is the main function of this type of CRM application. It analyzes customer data, coming from various touch points, to get better insights about current status of an organization. It helps top management to take better decision, marketing executives to understand the campaign effectiveness, sales executives to increase sales and support personnel to improve quality of support and build strong customer relationship.
Collaborative CRM, sometimes called as Strategic CRM, enables an organization to share customers’ information among various business units like sales team, marketing team, technical and support team. Collaborative CRM helps to unite all groups to aim only one goal – use all information to improve the quality of customer service to gain loyalty and acquire new customers to increase sales.
Examples:

Salesforce.

SugarCRM.

Really Simple Systems.

NetSuite CRM.

Zoho.

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A data warehouse is a system that pulls together data from many different sources within an organization for reporting and analysis. The reports are created from complex queries within data.The primary focus of a data warehouse is to provide a correlation between data from existing systems.
A database is constrained to a particular applications or set of applications.

a data warehouse is a concept and guidelines on how to organize data. That data is stored on a database.

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The term heuristic is used for algorithms which find solutions among all possible ones ,but they do not guarantee that the best will be found,therefore they may be considered as approximately and not accurate algorithms.
These algorithms,usually find a solution close to the best one and they find it fast and easily.Sometimes these algorithms can be accurate,that is they actually find the best solution, but the algorithm is still called heuristic until this best solution is proven to be the best.
Heuristics give up a little accuracy for a lot of gains in speed, efficiency, and generalizability. Heuristics can even find answers we don't know exist. This is also the basis for natural intelligence.

For example, the simple process of addition is an algorithm. We have math laws that tell us how to perform addition and they always work exactly the same. Two plus two is always four.

Now consider facial recognition. If you see a friend at a distance partially turned away from you, 99% of the time you can correctly identify your friend, but sometimes you get it wrong and maybe it’s just someone that looks like your friend. Your brain is using heuristics.

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Knowledge Management Portal
Portal is a website. Knowledge Management Portal is to integrates information, collaboration, processes and expertise to improves coordination, collaboration and to improve ability to analyze program effectiveness.   For example, a knowledge management portal that designed to collect information or data on academic staff areas of expertise and their contributions to knowledge through research. KMP able to evaluate the performance of the academic staff.

Type of decision
- Semistructured, unstructured

Type of control
- Strategic planning

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ERP(ENTERPRISE RESOURSE PLANNING)
- business process system of integrated applications to manage the business and automate many back office functions related to technology, services and human resources.
- typically integrates all facets of an operation which including product palnning, development, manufacturing, sales and marketing that uses in a single database, application and user interface.

Type of decision:
- structured

Type of control:

- operational control

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- computer-based information system that uses its knowledge about a specific complex application area to act as an expert consultant to users.
- represents mainly as if-then rules rather then conventional procedural code
- unstructured.

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- OLAP is a business analytic tools enabling user (typically managers, executives, and analysts) to better access/view on the insights gained from analyzing large volume of  multidimensional data. For example, a company can calculate year-to-date sales or compare revenue figure for a particular product using OLAP. This information provide insights to the measure of success in sales for the product in the market.
- Semi & unstructured decisions
- Used for managerial control

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Enterprise Resource Management (ERM)
ERM is a business management software that allow the management of data generated from core business processes (e.g procurement, production, sales, distribution, customer service and so on)

Type of decision: Semi-structure
Type of control: Operational control

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Enterprise Information System (EIS)

Any information system that integrates and supports business processes within an enterprise (companies or organizations), big and small.

EIS typically has operational (transaction processing) and informational (reporting) capabilities, capable of handling large amounts of data in a system-wide environment.

Type of decision: structured, semi-structured and unstructured

Type of control: operational

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Artificial Neural Network (ANN)
ANN is a computational model that based on function of biological neural network. It is a machine learning that uses a pattern-recognition approach to solve a problem.

Eg: When we want to differentiate the type of flowers in the garden, we are using technology of image processing and machine learning of ANN to identify the type of flower.

Type of Decision: Unstructured
Type of Control:  Strategic Planning

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CRM - customer relation mnagement
To take care the customer relationship by providing a platform that enable managemnet, contact,see the sales, and productivity within the company.
Communication within a platform. The company can communicate with their customer well.
Type of control : semistructed
Type of decision : operational control

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C

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Knowledge Management Portal
Portal is a website. Knowledge Management Portal is to integrates information, collaboration, processes and expertise to improves coordination, collaboration and to improve ability to analyze program effectiveness.   For example, a knowledge management portal that designed to collect information or data on academic staff areas of expertise and their contributions to knowledge through research. KMP able to evaluate the performance of the academic staff.

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Data Warehouse
Data warehouse is a system used to report and analyse data and is considered one of the core component of business intelligence. Data Warehouse is a central repository of integrated data from one or more disparate sources. It stores data in one single place that is used for creating analytical report for workers throughout the enterprise.
Type of decision: structured. The procedures for obtaining the best solution is known in data warehouse.

Type of control:
Operational control
Data is uploaded from operational system which passes through an operational data store and requires data cleansing for additional operation to ensure data quality.

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Group Support System (GSS)
GSS is the combination of the software and hardware to bring group of people together such as worker in different places so that it can help them for decision making after coming together and discuss about the problem. This would help to solve the physical constraint of the company especially they have few branches at different location.
Example include Skype, webex and so on.

Type of decision can either be semi structured or unstructured.
Type of control is managerial control.

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Knowledge Management System (KMS)
- Any kind on IT systems that stores and retrieves knowledge to help people utilize knowledge to achieve better tasks

Type of decision: semistructured
Type of control: strategic planning

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anis syafiqah

short description :
data mining is a process used to turn large raw data to useful information.
involving method at the intersection of machine learning, statistics and database

example :
Walmart processes over 20 million point of sale transaction everyday to be able determine sale trend, develop marketing campaign, predict customer loyalty

type of decision :
structured decision
decision tree is break down a dataset into smaller and smaller subset

type of control :
managerial control

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EIP
- Enterprise Information Portal
- business portal that serves as a single gateway to a company’s information and knowledge base for each employees, customers, business partners and public.
- structured (it undergoes procedures for obtaining the best solution. It takes count of various types of information when making decisions about policy and practice)
-strategic planning (continuously gather evidence and it’s an ongoing process, directly related to being a lifelong learner)

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Supply Chain Management (SCM) is the oversight of materials, informations and finances as they move in a process from supplier to manufacturer to wholesaler to retailer and to consumer. SCM involve coordinating and integrating these flow both within and among companies.

Type of decision: semistructured
Type of control: Managerial control

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Briefly write a short notes describing the DST, their type of decision and type of controls.

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Briefly write a short notes describing the DST, their type of decision and type of controls.