Flashcards and Answers – MGTS2202 Lecture MCQs

A) there is less demand on IT departments for reports. B) mobile platforms such as the iPhone are supported by these products. C) they explore massive amounts of data in hours, not days. D) it is easier to spot useful patterns and trends in the data.

A) operational data that identify what actions to take to resolve a problem B) summarized dimensional data to analyze the root cause of problems C) * graphical, abstracted data to monitor key performance metrics D) summarized dimensional data to monitor key performance metrics

A) ensuring that the organization has access to the latest web browsers B) ensuring that the organization has the appropriate hardware onsite to support it C) * ensuring that the required information is shown clearly on a single screen D) ensuring that users across the organization have access to it

A) heat map B) bubble chart C) *pie chart D) bullet

A) immersive environments for consuming data. B) * new forms of computation of business logic. C) worldwide digital distribution of visualization. D) new graphics displays through PC displays

A) strategic level managers making long-term, wide-ranging decisions B) mid-level managers making tactical decision C) frontline workers who must make decisions rapidly * D) operational managers who make shop floor decisions

A) intuition, demand, and supply. B) intuition, competition, and supply. C) business rules, demand, and supply. * D) business rules, supply, and intuition.

A) a simulation model that tried out many options. B) a set of 500 rules on the subject.* C) an optimization model. D) an expert system whose performance was inferior to human experts.

A) knowledge consolidation* B) knowledge representation C) knowledge acquisition D) knowledge validation

A) experts may not be able to put into words how they conduct their work. B) testing and refining of knowledge is complex and difficult. C) many business areas have no identifiable experts.* D) experts often change their behavior when observed

Expert Systems Game Playing Computer Vision Automatic Programming Speech Understanding Autonomous Robots Intelligent Tutoring Intelligent Agents Natural Language Processing Voice Recognition

Identifying proper experts Acquiring knowledge Selecting the building tools Coding the system Evaluating the system

Answer: In simple terms, An ADS is a rule-based system that provides a solution, usually in one functional area, to a specific repetitive managerial problem, usually in one industry.

Cluster algorithms are used when the data records do not have predefined class identifiers (i.e., it is not known to what class a particular record belongs).

Association rule mining is appropriate to use when the objective is to discover two or more items (or events or concepts) that go together. Students' answers will differ.

Classification is for prediction that can be based on historical data and relationships, such as predicting the weather, product demand, or a student's success in a university. If what is being predicted is a class label (e.g., "sunny," "rainy," or "cloudy") the prediction problem is called a classification, whereas if it is a numeric value (e.g., temperature such as 68°F), the prediction problem is called a regression.

Classification methods learn from previous examples containing inputs and the resulting class labels, and once properly trained they are able to classify future cases. Clustering partitions pattern records into natural segments or clusters.

Generally speaking, data mining tasks can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. In contrast, with unsupervised learning the training data includes only the descriptive attributes.

ith supervised learning algorithms, the training data includes both the descriptive attributes (i.e., independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable).

In contrast, with unsupervised learning the training data includes only the descriptive attributes.

1. A deployed SOA-based architecture will provide a loosely-integrated suite of services that can be used within multiple business domains. **2. SOA, is an architecture approach that packages functionality as interoperable, loosely-coupled units, or services, made accessible over a network and communicating by passing data independent of operating system or programming language. 3. SOA is an approach to organizing and utilizing distributed data resources operated by independent organizations. The SOA establishes standardized procedures for interactions (services) between these resources. ...

1. A technology that provides a vehicle for pushing data from source systems into a data warehouse 2. An evolving tool space that promises real-time data integration from a variety of sources 3.A new way of integrating information systems

1. A technology that provides a vehicle for pushing data from source systems into a data warehouse 2. An evolving tool space that promises real-time data integration from a variety of sources 3. A new way of integrating information systems

Price Learning curve Ability to read from and write to an unlimited number of data sources/architectures Automatic capturing and delivery of metadata A history of conforming to open standards An easy-to-use interface for the developer and the functional user

1.Executive Dashboard 2.Analytics Play Pen 3.A database

1.Number of bookings per year 2.Star rankings 3. Existence of written feedback on site 4. Repeat visit to site 5. Response to survey

1.Database Integration (various) 2.Online Analytical Processing 3.Real time Data warehouse 4.Visual Analytics 5.Regular Data warehouse 6.Data mining 7.Other