Operational Data: Data quality is one of the key features of enterprise data management. The data obtained from the operational system is low quality data because of the following reasons: Obtaining the data from outside the organization. Redundant storage of data and inconsistent metadata. Problems while entering data due to poor interfaces, manual mistypes of data. The poor quality of the data is not recognized by the organization itself is an issue. Data warehouse: Data warehouse contains the collection of data that are used for decision making and business intelligence. It is a unique kind of database so it focuses on business intelligence, time variant data, and external data. It is a subject-oriented, integrated, time-variant, and non-updateable data.

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Could you fix this? My marker has commented, What's missing? The input list is the link below. https://gmierzwinski.github.io/bishops/cs321/resources/CS321_Assignment_1_Input.txt result.put(true, dishwasherSum); result.put(false, sinkSum); return result; }}

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Chapter 13, Problem 7RQ

Program Plan Intro

Operational Data:

Data quality is one of the key features of enterprise data management.

The data obtained from the operational system is low quality data because of the following reasons:

Obtaining the data from outside the organization.

Redundant storage of data and inconsistent metadata.

Problems while entering data due to poor interfaces, manual mistypes of data.

The poor quality of the data is not recognized by the organization itself is an issue.

Data warehouse:

Data warehouse contains the collection of data that are used for decision making and business intelligence.

It is a unique kind of database so it focuses on business intelligence, time variant data, and external data.

It is a subject-oriented, integrated, time-variant, and non-updateable data.

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Could you fix this? My marker has commented, What's missing? The input list is the link below. https://gmierzwinski.github.io/bishops/cs321/resources/CS321_Assignment_1_Input.txt result.put(true, dishwasherSum); result.put(false, sinkSum); return result; }}

PLEG136: Week 5 Portofolio Project Motion to Compel

B A E H Figure 1 K Questions 1. List the shortest paths between all node pairs. Indicate the number of shortest paths that pass through each edge. Explain how this information helps determine edge betweenness. 2. Compute the edge betweenness for each configuration of DFS. 3. Remove the edge(s) with the highest betweenness and redraw the graph. Recompute the edge betweenness centrality for the new graph. Explain how the network structure changes after removing the edge. 4. Iteratively remove edges until at least two communities form. Provide step-by-step calculations for each removal. Explain how edge betweenness changes dynamically during the process. 5. How many communities do you detect in the final step? Compare the detected communities with the original graph structure. Discuss whether the Girvan- Newman algorithm successfully captures meaningful subgroups. 6. If you were to use degree centrality instead of edge betweenness for community detection, how would the results change?