Presented by:
N.SravanSuryaKumar
T.PavanKumar

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ABSTRACT
One may claim that the exponential growth in the amount of data provides great opportunities for data mining. In many real world applications, the number of sources over which this information is fragmented grows at an even faster rate, resulting in barriers to widespread application of data mining. A data warehouse is designed especially for decision support queries.
Data warehousing is the process of extracting and transforming operational data into informational data and loading it into a central data store or warehouse.
The idea behind data mining , then is the “ non trivial process of identifying valid, novel , potentially useful, and ultimately understandable patterns in India”
Data mining is concerned with the analysis of data and the use of software technique for finding patterns and regularities in sets of data. Data mining potential can be enhanced if the appropriate data has been collected and stored in data warehouse
Data warehousing provides the means to change raw data into information for making effective business decision – the emphasis on information , not data. The data warehouse is the hub for decision support data.
This paper also explains partition algorithm to discover all requirements sets from the data warehousing using the data mining. Also explained relation between operational data , data warehouse and data marts.
DATA WAREHOUSE & DATAMINING
Every day organizations, both large and small, genetic billions of bytes of data related all aspects of their business. But locked up variety of systems, most of this data is extremely difficult to access. Only a very small part of data – captured, processed and stored is available to decision markers.
INTRODUCTION
What is data warehouse?
A data warehouse in its simplest perception , is in more than a collection of
the key pieces of information used to manage the and direct business for the most
popular outcome.
A large amount the right information is the key to survival in today’s competitive environment. And this kind of information can be available only if there’s a totally integrated enterprise data warehouse.
A data warehouse is repository of integrated information, available for queries and analysis. For such a repository, data and information extracted from heterogeneous resources and consolidated in a single source. This makes it much easier and efficient to query the data.
There are two fundamentally different types of information systems in enterprises: operational systems and informational systems
Operational systems run daily enterprises information like ERP(enterprises resource planning). Information systems analyze the data make decision on how enterprise will be operate, not only information systems have different focus from operational ones, they often have a different scope altogether.
There are some specific rules that govern the basic warehouse , namely that such a structure should be:
Time dependent: that is containing information collected over time, which implies there must always be connection between the information in the warehouse and time when it was entered. This is one of the most important aspect of warehouse as its relates to data mining, because information can then be stored according to period.
Non-volatile: that is data in a data warehouse never updated but used only for queries. Thus such data only located from other database such as the operational database. End- users we want to update data must use operational databases, as only latter can be updated, changed and deleted. This means that a data warehouse will always be filled with historical data.
Subject oriented: that is, built all existing applications of the operational data. Not all the information in operational database is useful for data warehouse, since the data warehouse is designed specially for decision support while the operational database information containing day-to-day.
Integrated: that is, it reflects the business information of organization. In an operational data environment you will find many types of information being used in variety of applications and some applications will be using different name for same entities. However in a data warehouse is essential to integrate this information and make it consistent; only one name must exist to describe each entity.
A data warehouse is designed especially for decision support queries, therefore only data that is needed for decision support extracted from operational data and stored and stored in warehouse.
Need for DATA WAREHOUSE
1. To summarize the large volumes of data.
2. To integrate data’s from different sources.
3. Make decision makers to access past data.
4. Enable people to make informed decision.
Users
From the definition we can infer that the data warehouse users are as follows
1. This person’s job involves drawing conclusions from, and making decision Based on large masses of data.
2. This person doesn’t want to get involved with finding and organizing the Data for this purpose.
3. This person also doesn’t want to access a database highly technical fashion.
STRUCTURE OF DATA WAREHOUSE
Data warehousing is one of the hottest industry trends for good reason. The structure of a data warehouse consist as follows.
• Physical data warehouse
• Logical data warehouse
• Data marts
Physical data marts in which all the data for the data warehouse are stored, along with meta data and processing for scrubbing , organizing , packing and processing detail the data.
Logical marts also contain as physical database but does not contain actual data. Instead it contains the information necessary to access the data wherever they reside.
Data mart is subset of an enterprise wide data warehouse, which potentially supports an enterprise element.
DATA MARTS
Data marts are partitions of the overall data warehouse. It contains overlapping data. The task of implementing a data warehouse can be a very big effort, taking a significant amount of time. One feasible option is to start with a set of data marts for each component of departments. One can have a stand alone data mart or dependent data mart. A set of smaller, manageable, database is called data marts.
Stand alone data mart - a data mart with minimal or no impact on the enterprise operational databases.
Dependent data mart – similar to stand alone data mart, Except that management of data source by enterprise data base is required. These data sources include operational databases and external source of data.
DATA WAREHOUSE-ARCITECTURE
The architecture of an information system refers to the way its pieces are laid out , what types of tasks allocated to each piece of hoe pieces interaction with each other and how they interact with outside world. The architecture of data warehouse