Question

Assignment

What are the similarities,differences,advantages and disadvantages of the following over the other;

Data Mining and Warehousing,

Machine learning and Deep learning,

Artificial Intelligence and Expert system.

Answer 1

Data Mining and Warehousing: -

Both are business intelligence tools, which work upon huge sets. it gathers useful information which can be devised for many real time application line futuristic prediction , market analysis and customer behavior etc. Since it’s working strategy based on huge amount of data stored long time within different storage which brings under one hood so that it could be utilized in many actionable form.

Data WareHouse:-

It is technology to store data in very effective manner from multiple sources,so that manipulate and stored logically.
Data warehouse is database system which is designed for analytical analysis instead of transactional work.
Data is stored periodically.
Data warehousing is the process of extracting and storing data to allow easier reporting.
Data warehousing is solely carried out by engineers.
Data mining is the process of analyzing data patterns.
Data is analyzed regularly.

DataMining:-

Storing data by Warehouse is extracted by this technology so that it could be utilzed by business user or end user.
Use of pattern recognition logic to identify patterns by end user.
It carried by business users with the help of engineers.
Data mining is considered as a process of extracting data from large data sets which complied by datawarehouse.

DB(1)..DB(2)..DB(N)

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Extraction

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DATAWAREHOUSE(TECHNICAL LOGIC)

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CONTROLLER(SERVER)

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DATAMINING(BUSINESS LOGIC)

Disadvantages of Data mining: -

Privacy Issues

Security issues

Misuse of information/inaccurate information

Advantages of Data mining: -

Marketing / Retail

Help to build models based on historical data to predict who will respond to the new marketing campaigns such as direct mail, online marketing campaign etc.

Finance / Banking

Giving Information of customers ,client to determine their status . Based on historical data it is helpful to sanction or other business relationship with them.

Manufacturing

By applying data mining in operational engineering data, manufacturers can detect faulty equipment

Governments

Detecting money laundering or criminal activities.

DWH Advantages:

Saves Time and Money

Tracks Historically Intelligent Data

Limitations of using DWH

Extra Report Work.

Inflexibility and homogenization of data

Hidden issues consume time

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Deep learning is a subset of machine learning, and machine learning is a subset of AI.

• Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned
• Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own
• Deep learning is a sub-field of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence

Advantages : -

Massive Data Consumption from Unlimited Sources

Rapid Analysis Prediction and Processing

Interpret Past Customer Behaviors

Disadvantages :-

Implementation of set-up cost is very expensive few can afford it .

Creativity and behavioral aspects can’t be devised

It could be estimated that human can be replaced with machine

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An Expert System is defined as an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems.

   Examples of Expert Systems

MYCIN ,DENDRAL ,PXDES,CaDet

Advantages expert systems

• It improves the decision quality
• Cuts the expense of consulting experts for problem-solving
• It provides fast and efficient solutions to problems in a narrow area of specialization.
• It can gather scarce expertise and used it efficiently.
• Ability to solve complex and challenging issues
• Expert Systems can work steadily work without getting emotional, tensed or fatigued.

   Disadvantages of the expert system

• Unable to make a creative response in an extraordinary situation
• Errors in the knowledge base can lead to wrong decision
• The maintenance cost of an expert system is too expensive
• Each problem is different therefore the solution from a human expert can also be different and more creativity.