Data Base needs and Data Mining

8/13/2019 Data Base needs and Data Mining

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IRJMST Volume 1 Issue 1 Online ISSN 2250 - 1959

International Research Journal of Management Science & Technologyhttp:www.irjmst.com Page 5

Name of the Scholar - Fazia M. Aliyar

We are in an age often referred to as the information age. In this information age, because we

believe that information leads to power and success, and thanks to sophisticated technologies

such as computers, satellites, etc., we have been collecting tremendous amounts of information.

Initially, with the advent of computers and means for mass digital storage, we started collecting

and storing all sorts of data, counting on the power of computers to help sort through this

amalgam of information. Unfortunately, these massive collections of data stored on disparate

structures very rapidly became overwhelming. This initial chaos has led to the creation of

structured databases and database management systems (DBMS). The efficient databasemanagement systems have been very important assets for management of a large corpus of data

and especially for effective and efficient retrieval of particular information from a large

collection whenever needed. The proliferation of database management systems has also

contributed to recent massive gathering of all sorts of information. Today, we have far more

information than we can handle: from business transactions and scientific data, to satellite

pictures, text reports and military intelligence. Information retrieval is simply not enough

anymore for decision-making. Confronted with huge collections of data, we have now created

new needs to help us make better managerial choices. These needs are automatic summarization

of data, extraction of the essence of information stored, and the discovery of patterns in raw

data.

What kind of information are we collecting?

We have been collecting a myriad of data, from simple numerical measurements and text

documents, to more complex information such as spatial data, multimedia channels, and

hypertext documents. Here is a non-exclusive list of a variety of information collected in digital

form in databases and in flat files.

Business transactions: Every transaction in the business industry is (often) memorizedfor perpetuity. Such transactions are usually time related and can be inter-business deals

such as purchases, exchanges, banking, stock, etc., or intra-business operations such as

management of in-house wares and assets. Large department stores, for example, thanks

to the widespread use of bar codes, store millions of transactions daily representing often

terabytes of data. Storage space isnot the major problem, as the price of hard disks is

Data Base needs and Data Mining


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continuously dropping, but the effective use of the data in a reasonable time frame for

competitive decision making is definitely the most important problem to solve for

businesses that struggle to survive in a highly competitive world.

Scientific data: Whether in a Swiss nuclear accelerator laboratory counting particles, in

the Canadian forest studying readings from a grizzly bear radio collar, on a South Poleiceberg gathering data about oceanic activity, or in an American university investigating

human psychology, our society is amassing colossal amounts of scientific data that need

to be analyzed. Unfortunately, we can capture and store more new data faster than we can

analyze the old data already accumulated.

Medical and personal data: From government census to personnel and customer files,

very large collections of information are continuously gathered about individuals and

groups. Governments, companies and organizations such as hospitals, are stockpiling

very important quantities of personal data to help them manage human resources, better

understand a market, or simply assist clientele.

Regardless of the privacy issues this type of data often reveals, this information is collected, used

and even shared. When correlated with other data this information can shed light on customer

behaviour and the like.

Surveillance video and pictures: With the amazing collapse of video camera prices,

video cameras are becoming ubiquitous. Video tapes from surveillance cameras are

usually recycled and thus the content is lost. However, there is a tendency today to store

the tapes and even digitize them for future use and analysis.

Satellite sensing: There is a countless number of satellites around the globe: some are

geo-stationary above a region, and some are orbiting around the Earth, but all are sending

a non-stop stream of data to the surface. NASA, which controls a large number of

satellites, receives more data every second than what all NASA researchers and engineers

can cope with. Many satellite pictures and data are made public as soon as they are

received in the hopes that other researchers can analyze them.


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Data Mining in Business

Through the use of automated statistical analysis (or "data mining") techniques, businesses are

discovering new trends and patterns of behavior that previously went unnoticed. Once they've

uncovered this vital intelligence, it can be used in a predictive manner for a variety of

applications. Brian James, assistant coach of the Toronto Raptors, uses data mining techniques to

rack and stack his team against the rest of the NBA. The Bank of Montreal's businessintelligence and knowledge discovery program is used to gain insight into customer behavior.

Gathering Data

The first step toward building a productive data mining program is, of course, to gather data!

Most businesses already perform these data gathering tasks to some extent -- the key here is to

locate the data critical to your business, refine it and prepare it for the data mining process. If

you're currently tracking customer data in a modern DBMS, chances are you're almost done.

Take a look at the article Mining Customer Data from DB2 Magazine for a great feature on

preparing your data for the mining process.

Selecting an Algorithm

At this point, take a moment to pat yourself on the back. You have a data warehouse! The next

step is to choose one or more data mining algorithms to apply to your problem. If you're just

starting out, it's probably a good idea to experiment with several techniques to give yourself a

feel for how they work. Your choice of algorithm will depend upon the data you've gathered, the


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problem you're trying to solve and the computing tools you have available to you. Let's take a

brief look at two of the more popular algorithms.

Regression

Regression is the oldest and most well-known statistical technique that the data miningcommunity utilizes. Basically, regression takes a numerical dataset and develops a mathematical

formula that fits the data. When you're ready to use the results to predict future behavior, you

simply take your new data, plug it into the developed formula and you've got a prediction! The

major limitation of this technique is that it only works well with continuous quantitative data

(like weight, speed or age). If you're working with categorical data where order is not significant

(like color, name or gender) you're better off choosing another technique.

Classification

Working with categorical data or a mixture of continuous numeric and categorical data?

Classification analysis might suit your needs well. This technique is capable of processing a

wider variety of data than regression and is growing in popularity. You'll also find output that is

much easier to interpret. Instead of the complicated mathematical formula given by the

regression technique you'll receive a decision tree that requires a series of binary decisions. One

popular classification algorithm is the k-means clustering algorithm. Take a look at the

Classification Trees chapter from the Electronic Statistics Textbook for in-depth coverage of this

technique.

Other Techniques

Regression and classification are two of the more popular classification techniques, but they onlyform the tip of the iceberg. For a detailed look at other data mining algorithms, look at this

feature on Data Mining Techniques or the SPSS Data Mining page.

Data Mining Products

Data mining products are taking the industry by storm. The major database vendors have already

taken steps to ensure that their platforms incorporate data mining techniques. Oracle's Data

Mining Suite (Darwin) implements classification and regression trees, neural networks, k-nearest

neighbors, regression analysis and clustering algorithms. Microsoft's SQL Server also offers data

mining functionality through the use of classification trees and clustering algorithms. If you'realready working in a statistics environment, you're probably familiar with the data mining

algorithm implementations offered by the advanced statistical packages SPSS, SAS, and S-Plus.

Moving On


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Have we whetted your appetite for data mining knowledge? If you're ready to get started but

can't find any sample data, take a look at the various repositories listed in Data Sources for

Knowledge Discovery. Good luck with your data mining endeavors!


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Chemical Property of Water and How to Change it using Chemical

reactions

Name of the Scholar

Amna Khan

PhD from Chhatrapati Shahu ji Maharaj University Kanpur, 2005

Abstract:We all had heard the terms 'hard water' and 'soft water', but do we know what they

mean? Is one type of water somehow better than the other? What type of water do you have?

Let's take a look at the definitions of these terms and how they relate to water in everyday life.

Hard wateris any water containing an appreciable quantity of dissolved minerals. Soft water istreated water in which the only cation (positively charged ion) is sodium. The minerals in water

give it a characteristic taste. Some natural mineral waters are highly sought for their flavor and

the health benefits they may confer. Soft water, on the other hand, may taste salty and may not

be suitable for drinking.

If soft water tastes bad, then why might you use a water softener? The answer is that extremely

hard water may shorten the life of plumbing and lessen the effectiveness of certain cleaning

agents. When hard water is heated, the carbonates precipitate out of solution, forming scale in

pipes and tea kettles. In addition to narrowing and potentially clogging the pipes, scale prevents

efficient heat transfer, so a water heater with scale will have to use a lot of energy to give you hot

water. Soap is less effective in hard water because its reacts to form the calcium or magnesium

salt of the organic acid of the soap. These salts are insoluble and form grayish soap scum, but no

cleansing lather. Detergents, on the other hand, lather in both hard and soft water. Calcium and

magnesium salts of the detergent's organic acids form, but these salts are soluble in water.

Hard water can be softened (have its minerals removed) by treating it with lime or by passing it

over an ion exchange resin. The ion exchange resins are complex sodium salts. Water flows over

the resin surface, dissolving the sodium. The calcium, magnesium, and other cations precipitate

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Data Base needs and Data Mining