1. Goldschmidt (1998) suggests that the main advantages of using standard protocols and reference models include their being well-defined, widely accepted, and free in sense that no single company controls them. At the same time, the major limitations of using such protocols and models include SYN attacks, sequence manipulation, and amorphous identification. SYN attacks result in monopolization of computer resources. Sequence manipulation can lead to theft of important data. Amorphous identification complicates an application’s security.
2. Data planning and modeling involves a number of advantages. It facilitates interaction among the designer, the application programmer and the end user. It also results in improved understanding of organization for which the database is developed. Finally, data models serve as communication tools. Disadvantages of modeling and planning depend on the type of data. Hierarchal data model for example involves complex implementation, challenging navigational system, and a close relationship between the structure and the application, where changes in the former lead to changes in the latter. These disadvantages are alleviated through developing unified standards.
3. Dot-com companies do their business on the Internet, usually through websites that have popular .com domain. Until the year 2000, most dot-com companies were involved in what is referred to as the dot-com bubble, and it was the time when they outperformed traditional companies developing their online sales capabilities. The success of dot-com companies can be attributed to many factors, including surplus of venture capital funding, “get big fast” philosophy (Grant, 2003), and novelty of online business.
4. In order to explain the relationship between data warehousing and data mining, it is suggested to define both concepts. Data warehousing can be defined as “The electronic storage of a large amount of information by a business” (Investopedia, 2015). In turn, data mining is “the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cut costs, or both” (UCLA Anderson, n. d.). From the definitions, it is clear that data mining is impossible without data warehousing. At the same time, data warehousing does not make sense without data mining. Data mining involves the existence of different techniques, including clustering and nearest neighborhoods. The former involves gathering into a cluster, a number of items of the same kind (Han & Kamber, 2006).
A simple example of clustering is people doing the laundry. They sort things based on specific characteristics, it means perform clustering. The nearest neighborhood data mining technique involves an optimization problem for finding closest points. An example of the nearest neighborhood algorithm involves examining well-being of people in a specific area to conclude that individuals with approximately the same level of income tend to settle in the neighborhood.
5. Modern enterprise system involves the existence of different application areas. The former response focuses on engineering services and networks and data centers as the “hottest” …