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Today, the costs of data redundancy have decreased to some extent. Being able to more efficiently store data in tables "eliminated much data storage and provided much more flexible data access" (Data redundancy, 2010, Logic). Controlled redundancy with careful limits on unauthorized access can eliminate the problem of data inconsistency and having one set of data altered but not the other. According to Junhu Wan's 2006 article "Binary equality implication constraints, normal forms and data redundancy," inconsistent "redundancies can be prevented if the instances of the two relation schemas do not contain overlapping information," and thus the benefits that can be accrued from redundancies may outweigh their detriments (Wan 2006, p.2).
References
Data redundancy. (2010). Computing students. Retrieved October 17, 2010 at http://www.computingstudents.com/dictionary/?word=Data%20Redundancy
Data redundancy. (2010). Logic data UK. Retrieved October 17, 2010 at http://www.logicdata.co.uk/data-security/Data-Redundancy/data-redundancy-dbms/
Wan, Junhu. (2006). Binary equality implication constraints, normal forms and data redundancy.
Retrieved October 17, 2010 at citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.3912.
Intelligent agents
According to Michael Luck's article on "Agent-based computing," intelligent agents are "defined to be autonomous, problem-solving computational entities capable of effective operation in dynamic and open environments" (Luck 2006, p.35). Intelligent agents are, much like human intelligent agents, capable of interacting with computerized and human agents in a dynamic fashion and are even able to take into consideration that the people and other systems with which they are interacting might have conflicting aims. Multi-agent systems are capable of understanding change, and are thus more appropriate for representing real world environments. Agent-based computing has been used in both biological simulations of the human body and also in military computer simulations. However, Luck characterizes...
Significantly, the financial industry has been the most eager user of agent-based systems because of its ability to create decision-making models which change in their outcomes, based upon a variety of different potential market circumstances (Luck 2006, p.36). However, all businesses that have adapted intelligent agency in their computer systems have benefited because of the assistance it is able to give in guiding resource allocation, depending on split-second, sudden changes in the environment.
To create such intelligent agents that are capable of reacting to change at a moment's notice, intelligent systems must be able to learn from other agents and act upon user preferences, find ways to negotiate and cooperate with other agents, and develop "appropriate means of forming and managing coalitions" between different entities (Luck 2006, p.35). Intelligence is not defined as mere reactivity, but a form of complex reactivity. For example, one cargo company that has deployed intelligent agency found that "the system can dynamically adapt plans in response to unexpected changes, such as transportation cost fluctuations or changes to vessels, ports or cargo. Agent-based optimization techniques not only provided improved responsiveness, but also reduced the human effort necessary to deal with the vast amounts of information required, thus reducing costly mistakes, and preserving the knowledge developed in the process of scheduling" (Luck 2006, p.35). Changes were not confined to one sphere -- intelligent agents must be truly forward-thinking in their ability to examine diverse influences, such as changes in input costs and time-related scheduling. They possess, because of their ability to systemize different areas of content, accurate, a flexibility in some instances even superior to that of a human.
Reference
Luck, Michael. (2006, May). Agent-Based Computing.GeoConnection International Magazine.
Retrieved October 17, 2010 at http://www.geoconnexion.com/uploads/agentbased_intv5i5.pdf
references, find ways to negotiate and cooperate with other agents, and develop "appropriate means of forming and managing coalitions" between different entities (Luck 2006, p.35). Intelligence is not defined as mere reactivity, but a form of complex reactivity. For example, one cargo company that has deployed intelligent agency found that "the system can dynamically adapt plans in response to unexpected changes, such as transportation cost fluctuations or changes to vessels, ports or cargo. Agent-based optimization techniques not only provided improved responsiveness, but also reduced the human effort necessary to deal with the vast amounts of information required, thus reducing costly mistakes, and preserving the knowledge developed in the process of scheduling" (Luck 2006, p.35). Changes were not confined to one sphere -- intelligent agents must be truly forward-thinking in their ability to examine diverse influences, such as changes in input costs and time-related scheduling. They possess, because of their ability to systemize different areas of content, accurate, a flexibility in some instances even superior to that of a human.
Reference
Luck, Michael. (2006, May). Agent-Based Computing.GeoConnection International Magazine.
Retrieved October 17, 2010 at http://www.geoconnexion.com/uploads/agentbased_intv5i5.pdf
Data Warehousing Data Warehouse technology has changed the way that global organizations conduct business. Many have found it impossible to create a business strategy without a data warehouse. The purpose of this discussion is to research and explain the importance of data warehouse management. We will begin by defining data warehouse and describing the business uses for the technology. Our discussion will then focus of data warehouse management. We will examine the
Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline
Similarly, the Air Force needed no only some intelligent reporting capabilities, but a way that Air Force personnel, government employees, and civilian IT contractors would work together in the evaluation of applications and reports in a more robust and real-time manner. "The intent was to provide the Keystone user community the ability to do more complex financial analysis and reporting on a "self-service" basis to reduce overall system maintenance and
because the system is designed to be able to handle complex queries for information much faster than are traditional databases, designing and implementing such an attack becomes more difficult and complex (Warigon, 1997). At the same time, the ease with which information in a data warehouse can be manipulated creates more significant problems than a traditional database should unauthorized access be obtained (TechFaq, 2010). While no database or information
In addition, the support of multiple taxonomies is also critical for a data warehouse, and to the extent the architects have created a database architecture that will provide for metadata definition and re-defining of taxonomies is the extent to which the data warehouse will have greater use in the organization. Without a strong focus on these aspects of data agility, a data warehouse can quickly become outmoded and only
Since poor data quality within a system often results in poor business decisions being made from this data, it is very important that each administrator or system architect look at each customer or end-user differently, in their own unique light. Since each end-user is different, and the needs of the customer often stem from the warehouse's ability to accurately store quality information, a system that dates back to the 1970's