Please describe a few differences between fact tables and


Part 1: 150-200 words with references

Please describe a few differences between fact tables and dimension tables. In what case(s) might they be used interchangeably?

Part 2: 150-200 words with references

Why is it important to analyze both structured and unstructured data ?

Part 3: 150-200 words with references discussion forum reply

Discussion for reply:

According to Siganos, Neural networks take a different approach to problem-solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem-solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do.

Neural networks process information in a similar way the human brain does. The network is composed of many highly interconnected processing elements(neurons) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task.

The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The problem is that there is no way of knowing if the system is faulty or not unless an error occurs.

Part 4: 150-200 words with references discussion forum reply

Discussion for reply:

Neural networks are universal approximators, and they work best if the system you are using them to model has a high tolerance to error.

One would therefore not be advised to use a neural network to balance one's cheque book! However they work very well for:

• capturing associations or discovering regularities within a set of patterns;

• where the volume, number of variables or diversity of the data is very great;

• the relationships between variables are vaguely understood; or,

• the relationships are difficult to describe adequately with conventional approaches.

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