Data Warehouse Is Collection Of Resources

Published Date: 02 Nov 2017

A data warehouse is a collection of resources of one organization. It can be accessed to retrieve information. It store the data and designed to facilitate reporting and analysis.

Data warehouse mainly focuses on data storage. The data is available to managers and business professional for data mining, online analytical processing, in this data is retrieve and analyze. It includes the business intelligence tool, this tool to extract, transform and load data into resources

In this data made up by a collection of heterogeneous system. For example one organization having two system one system used for that handle the customer relationships, employees and that handle sales and production of data, another system handles the finance and budgeting data. But this system didn’t give the complete information like how much time spent to customer A and how much sales per day, this information is available somewhere is called data ware house. Mai purpose of the Data warehousing is to bridge such problem.

CHARACTERISTICS OF THE DATA WREHOUSE:

Subject oriented:

Data warehouse is designed for analyze the data. For example if you want to know the company sales data, first build a data warehouse then you can give any answer like how is best customer of the year, who buy the costly items many times. Data warehouse defined by subject matter is called the subject oriented

Integrated:

Integration is related to subject oriented. It put the data from disparate source into consistent format. They resolve such problems like as naming conflicts and inconsistency among units of measure is called the integrated.

Nonvolatile:

Data shouldn’t change when you entered into warehouse. Warehouses analyze what has occurred.

Time variant:

In order to make analysis a large amount of data required. For that OLTP system require historical data.

ARCHITECTURE OF THE DATA WAREHOUSE:

There are multiple architectures that supports to different environments and conditions. The architecture conceptualization aids in the building, maintenance and usage of the data warehouse.

Data warehouse consists of the following interconnection layers:

Operational database layer:

This is the source data for data warehouse. Organization ERP fall into this layer.

Data access layer:

This layer provide interface between operational and informational access layer, this layer useful to extract data, transform data and loading the data into Warehouse .

Meta data layer:

This is the data directory, there are dictionaries for entire warehouse and it useful to data can be accessed by the particular reporting and analysis tool.

Informational access layer:

In this layer data is accessed by tools for reporting and analyzing data. Business intelligence tools fall into this layer.

http://bisolutions.ir/Portals/0/DW%20Architecture.gif

Data warehouse

http://www.google.co.in/imgres?imgurl=http://bisolutions.ir/Portals/0/DW%2520Architecture.gif

OVERVIEW OF EXTRACTION, TRANSFORM AND LOAD:

ETL is a process in data warehouse that involves

Extracting data from outside

Transform into quality levels

Extract:

This is the first part of the ETL process, it extracting the data from source. It converts the data into a single format; it is useful to transformation process. Many data warehouses integrate the data from different sources and one single source is using the different organizations. Common data sources are relational databases, but may include non relational databases like as information management system (IMS), virtual storage access method (VSAM)

Transform:

It has series of rule to extract the data from source and loading data into end stage.

There are following one or more transaction types:

Select only null columns to load, these columns are called attributes. Extraction is takes only two columns

Translating coded values, this calls for automated data cleaning.

Encoding free form values.

Deriving

Sorting

Merging

Aggregation

Generating

Transposing

Splitting

Disaggregation

Validate the relevant data from tables

Applying form of simple or complex data validation. If validation fails, its results in a full, partial of the data.

Load:

This is last phase loads the data into end target; it is depend on the requirements of the organization. Some data warehouses may overwrite existing data, frequently updating the data in daily, weekly and monthly. Other databases add a new database. Data warehouse require maintain a record of the last year however the entry data for any one year will be made in a past manner. The time and scope depend on the time available and business needs. Load phase interacts with database, constraints defined in a database structure.

For example, financial institution contains the information of customer in several departments and each department contain the that customer information divided in a different ways. The membership department may list the customer by name and account department may list the customer by number. ETL can integrate this data and integrate into uniform presentation, like as for storing in a data warehouse or data base.

Another way organization’s use ETL is to move data to another application permanently. The new application may use another data base and likely a different data base structure. ETL can used to transform the data into a suitable format for the new application.

Data cleansing:

Data cleansing is act as a detecting and correcting corrupted data from data base. It is used in data bases, this is identifying the incomplete, incorrect, irrelevant data etc. it is replacing, modifying and deleting the dirty data from data bases.

After cleaning, a data will be consistence with other similar data in the system. Then it detects the inconsistence data and removed, it may have been caused by similar entities of different data dictionaries in different stores, it may have been caused by user entry errors and corrupted in storage.

Data cleansing comes from data validation, invariability means data is rejected from system at entry and is performed at entry time.

The actual process of data cleansing is removing errors or correcting values against a known list of entities. The validation may strict like as reject the invalidate data. Data cleansing is also known as data scrubbing.

Data integration:

Data integration involves integrates data. This residing the combined data in different sources and provides users with a unified view of these data. This becomes significant in different situations. It increases the frequency as the volume and share existing data.

http://www.google.co.in/imgres?imgurl=http://www.butein.com/Butein/DataIntegration/Butein-%2520Data%2520Integration.png

http://www.google.co.in/imgres?imgurl=http://upload.wikimedia.org/wikipedia/en/0/0c/Dataintegration.png

Data integration theory is the subset of database theory, apply this theories indicates the feasibility and difficulty of the data integration.

Data integration systems are defined as a triple GSM where G is the global structure, S is the heterogeneous set of source structure and M is the mapping that maps between global structure and source. The mapping M contain assertions between queries over G and queries over S.

In this database defined as asset of sets, one for each relation is called relational database. The database corresponding to source structure S would compromise the set of tuples for each of the heterogeneous data source is called source data base. this is corresponding to global structure is called global database. This satisfy the mapping M with respect to source database S. this mapping depends on nature of the correspondences between global structure and source structure. Two ways model this correspondence exits, they are:

Global as view(GAV)

Local as view(LAV)

http://www.google.co.in/imgres?imgurl=http://upload.wikimedia.org/wikipedia/en/9/9e/GAVLAV.png

Global database as set of views over source(s) by model the GAV systems. In this mapping M associated with global as a query over source. Query processing becomes a straight forward operation. This complexity falls on implementation of code instructing the data integration system. Any new source join the system, considerable effort may be required to update the mediator, when the source unlikely to change the source approach is preferable.

GAV examples is data integration system, the system designer first develop the mediators for each source and global. The designer would like to add corresponding elements to global structure. Then the bulk effort concentrates on writing the mediator code. The designer need to write code properly integrates the results from two sources.

In LAV, the source database model as asset of views over global G. in this mapping associated to elements of source S query over global G. the burden of determining how to retrieve elements from source S is placed on the query processor. The benefit of LAV design is new source can be added with far less work than in a GAV system, in this mediator structure is more stable and unlikely to change.

Data mart:

Data mart is a subset of organization data store, data marts are analytical data store for specific business functions for particular community within organization. That may be distributed to support business needs. This is derived from subset of data in data warehouse; the data warehouse is created from union of organizational data marts.

There are two types of data mart schemas, they are:

Star schema:

The star schema is also known as star join schema. It is the simple style of the data ware house; it contains one or more fact tables referencing any number of dimension tables. It is more effective for handling simple quarries.

Fact table holds the main data. It includes a bulky amount of aggregated data like as price, units, sold.

Dimensional table is smaller than fact table, it includes attributes that describes the facts. Dimension table can connect to the fact table as needed.

http://www.rapid-business-intelligence-success.com/images/star.jpg

Star schema

http://www.google.co.in/imgres?imgurl=http://www.rapid-business-intelligence- success.com/images/star.jpg

Snowflake schema:

Snowflake schema is logical arrangement of table in multidimensional data bases like as entity relationship diagrams this schema is represented by centralized fact table which contains multiple dimensions.

In this dimensions are normalized into multiple tables, where as star schema’s dimensions are de-normalized with each dimension represented by a single table. Complex snowflake shape having multiple levels of relationships, snowflake affects only the dimension tables not the fact table

http://datawarehouse4u.co.uk/images/snowflake_schema.jpg

Snowflake

http://www.google.co.in/imgres?imgurl=http://datawarehouse4u.co.uk/images/snowflake_schema.jpg

Data migration:

Data migration is the process of transferring data between computer systems. Data migration achieves an automated migration by programmatically performed. It is required to organizations to change system or upgrade to new systems.

To achieve data migration procedure, data on the old system mapped to new system, it provides a design for data extraction and loading. This design relates old system to new system and requirements. Programmatic data migration involves many phases but it includes minimally data extraction.

If take a decision to provide a set of input file specification for loading into target, It allows a preload data validation step to be put in place, interruptions’ occurred in ETL process. Like a data validation process can be designed to interrogate the data to be transferred, it ensures that it meets to pre-defined criteria of the target, and specification of the input files. This can be designed to report on load rejection errors as the load progresses. In this extracted and transformed data is the highly integrated with another and the presence of all extracted data in the target system is essential to system functionality.

After loading into the new system it subjected to data verification for data translated accurately or not in complete system. In verification process run both system parallel to identify the area of disparity and errors or data loss.

In migration process, automated and manual data cleaning is performed to data quality, and eliminate the redundant information.

Data migration process for application of reasonable to high complexity is commonly repeated several times before the new system is deployed.

Data conversion:

Data conversion is the process of converting computer data from one format to another. In overall computer environment, data is encoded in a variety of ways. Same as operating system predicted on certain standards for data and file handling. Each computer program handles data in different types. Data must be converted in some ways before it can be used by different computers. Even different versions of these elements involved in different data structures. Data conversion is converting the text file to character encoding or more complex and conversion of image and audio file formats.

In computer environment many ways to data is converted. This may be upgrading to a newer version of a computer program. It requires a special conversion program. A program may recognize different or several data file formats at the input stages and capable to storing output data in different formats. Such a program used to convert a file format. If source format is not recognizing, then at times third program may be available which permits the conversion to intermediate format.

Data transformation:

Data transformation converts the data from source data format to destination format. It can be divided into two steps they are:

Data mapping

Code generation

Data mapping:

It maps data element to data element is complicated by transformations that requires one to many and many to one transformation rules

Code generation:

It takes specification of the data element mapping and creates executable program. It can be run computer system. And it can also create transformation in easy to maintain.

OLAP

On-line Analytical processing is a category of software technology .It enables analysts, managers and executives to gain insight into data through fast, consistent, and interactive manner , and it transforms raw data into user understandability language.

The OLAP applications are used in business, marketing, financial reporting and similar applications such as agriculture.

The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).

Types:

OLAP systems have been divided in the following

Multidimensional (MOLAP):

It is the classic form of OLAP and it sometimes referred to as OLAP.It stores the data in a relational database. It stores the information in cube.

Relational (ROLAP):

ROLAP works directly with relational database. The base data and the dimension tables are stored as relational table .The new tables are created to hold the aggregated information.

HYBRID (HOLAP):

It has no clear agreement with hybrid HOLAP .It divides the data between relational and specialized storage.

OTHER TYPES:

The following types are also sometimes used.

WOLAP-Web-based OLAP

DOLAP-Desktop OLAP

RTOLAP-Real-time OLAP

Uses in business:

OLAP can be used by managers and analysts to manipulate the larger amount of data from different fields, who use OLAP in businesses it, may analyze complex relationships among thousands to millions of data items, and it is stored in data marts and other multidimensional databases.

Examples of OLAP in complex problems such as

Marketing and sales

Budgeting

Financial reporting and consolidation.

OLAP AND THE DATA WAREHOUSE

OLAP permits sophisticated multi-dimensional analysis

Of data and it can be used for decision making.

ROLLUP: The rollup operation collapses the dimension hierarchy along particular dimensions at a coarser level of granularity.

Drill-Down: In contrast, the drill-down function allows users to obtain more detailed view of a given dimension.

Slice: Here the objective is to extract a slice of the original cube corresponding to a single value of a given dimension. No aggregation is required in this model.

Dice: A related operation is the dice .In this case we are defining the sub cube of the original space.

Pivot: The pivot is simple but effective operation and it allows OLAP users to visualize cube values in natural manner.

OLAP ARCHITECTURE

OLAP architecture can be divided into 3 modules.

Graphical User Interface

Analytical Processing Logic

Data-processing Logic

In client-server environment these three modules define the detailed view of OLAP system. Those are

Multidimensional data analysis

Advanced data support

Easy-to-use interface

The OLAP engine performs data extraction, filtering, integration, and classification and aggregation functions. The OLAP performs all data preparation functions result is there is no duplication of data and OLAP handles data components more efficiently than data warehouse.

To provide better performance OLAP systems merge data warehouse and data mart approaches and to store small extracts of the data warehouse at end-user workstations. The OLAP objective is to increase speed of data access and data visualization.

http://projects.cs.dal.ca/panda/images/olap_colour.gif

http://www.google.co.in/imgres?imgurl=http://projects.cs.dal.ca/panda/images/olap_colour.gif

In client-server architecture the components are

Easy-to-use GUI

Dimensional presentation

Dimensional modeling

Dimensional analysis

Multidimensional data

Analysis

Manipulation

Structure

Database support

Data warehouse

Operational DB

Relational

Multidimensional

OLAP CUBE (ON-LINE ANYALATICAL PROCESSING CUBE)

OLAP cube is a data structure and it allows the fast analysis of data. It defines the capability of manipulating data from different users.

http://datanovasoftware.com/images/olap_cube.gif

http://www.google.co.in/imgres?imgurl=http://blogs.technet.com/blogfiles/andrew/WindowsLiveWriter/OLAPCubesandMultidimensionalAnalysis_10C03/cube.jpg

The technologies involved in OLAP system are Microsoft project server analyzer, cube generation process, extensibility model for the OLAP cube. By adding pay period dimension to the cube we can extend the Microsoft project server analyzer. If we edit the structure of the virtual cube we can run another function. Then we can regenerate the cube into new staging table data. Finally there are sections at the end i.e. how we can add security to the cube, how we can find information with Decision support objects (DSO), how we can troubleshoot the cube building process.

OLAP achieves the multidimensional functionality by using structure is called cube. OLAP cube provides the several ways to look the data. It compares the table in a relational database.

The design process of OLAP cube is report optimization. Many databases are designing for online transaction processing and efficiency but OLAP cube is designing for data. The storage process of OLAP cube data is very easy and efficient. The OLAP cubes have different categories of data called dimensions and measures. A measure represents the some fact and cost and units of service. A dimension represents the categories of data such as time or location.

The graphical representation of OLAP cube is

http://training.inet.com/OLAP/Images/kube0006.gif

http://www.google.co.in/imgres?imgurl=http://training.inet.com/OLAP/Images/kube0006.gif

There are three important concepts to analyze the data using OLAP cube

There are

SLICE

DICING

ROTATING

SLICING:

It is a subset of the multidimensional array .It corresponds to the single value for one or more dimensions i.e. not in the subset. Suppose if the members are selected from this dimension then the sub cube of all the members in that slice. Suppose if the data is removed from this slice then the data can be associated with the Non-selected members of this dimension.

For example we will take Budget, variance, Forecast etc.

http://training.inet.com/OLAP/Images/kube0003.gif

http://www.google.co.in/imgres?imgurl=http://training.inet.com/OLAP/Images/kube0003.gif

Slicing wireless mouse:

The result of the data for wireless mouse is years and locations. In another way we filter data and display into measures with the wireless product.

http://training.inet.com/OLAP/Images/kube0004.gif

http://www.google.co.in/imgres?imgurl=http://training.inet.com/OLAP/Images/kube0003.gif

In the above example data for Asia and product and years are result.

DICING:

The related operation of slicing is dicing. In this case we define the sub cube of the original space. In dicing the data is one cell from the cube. Dicing provides smallest available

http://training.inet.com/OLAP/Images/kube0008.gif

http://www.google.co.in/imgres?imgurl=http://training.inet.com/OLAP/Images/kube0008.gif

Rotating:

It changes the dimensional rotation of the report from the cube data.

In this example rotating consist of interchanging of rows and coulmns

Or moving the data from rows dimensions to coulmns dimension or

Off-spreadsheat of interchanging rows and columns.in this we can also hear the term pivoting.Rotating and pivoting terms are same things.

http://training.inet.com/OLAP/Images/CubeRotate2.gif

http://www.google.co.in/imgres?imgurl=http://training.inet.com/OLAP/Images/kube0006.gif

Generating OLAP cube:

We can build the OLAP cube in two ways. In Microsoft project web access there is a administrative setting from that administrator can configure the cube on regular basis. We can generate the cube in weekly or nightly. Second process is we can generate the cube in manually in Microsoft project web access.

Manual updating of OLAP cube:

Click Admin

Click Manage enterprise features on Administration overview page.

Click update resource tables and OLAP cube under Enterprise options.

Click Update under update frequency and then click update now.

There are two steps to require the Generating OLAP cube:

http://i.msdn.microsoft.com/dynimg/IC66525.gif

http://www.google.co.in/imgres?imgurl=http://i.msdn.microsoft.com/Aa188799.olapCube02(en-us,office.10).gif

Overview of OLAP cubes architecture:

In this process Microsoft project server creates the data warehouse to create the MSP_CUBE staging tables. The building process of cube is first data is gathered from the Microsoft project server database and put that data into designated tables and this process is known as data warehouse. These all tables have prefix MSP_CUBE in the Microsoft

Project server database.

http://i.msdn.microsoft.com/dynimg/IC91286.gif

http://www.google.co.in/imgres?imgurl=http://i.msdn.microsoft.com/Aa208429.pjsdkCubesPABuildCube1_ZA01096384(en-us,office.11).gif

Cube

Cube building 1: creating data warehouse

After building process of staging tables, the cube building service starts the second step, and then we can generate the cube from the staging tables of the data warehouse. When the completion of cube generation process.

http://i.msdn.microsoft.com/dynimg/IC5178.gif

http://www.google.co.in/imgres?imgurl=http://i.msdn.microsoft.com/Aa208429.pjsdkCubesPABuildCube2_ZA01096385(en-us,office.11).gif

Extensibility model for the OLAP cube:

Microsoft project server provides the simple extensibility model and it supports the two breakout points during the process of cube generation.

http://i.msdn.microsoft.com/dynimg/IC124992.gif

http://www.google.co.in/imgres?imgurl=http://i.msdn.microsoft.com/Aa188799.olapCube05(en-us,office.10).gif

Extending the cube with breakout functions:

The first breakout point occurs after creation of the staging tables.

By using user staging tables update to add the staging tables to the function.

After generating Microsoft project server the second breakout point occurs.

At this point we can add security roles to the cube and then process the cube again.

Extending the cube by adding a new pay period dimension:

This process describes the cube extension in three steps:

By adding new pay period dimension to extending the staging tables.

OLAP cube is updating with new data.

Add virtual cube to new dimension.

Design:

Online Analytical processing (OLAP) is a category of software technology. The main function of OLAP is to collect the data from different data sources like data bases and data segments. The designing process of project is based on requirements of the application. Here the project is to integrate the new ideas from data sources with single interface.

The communication process of different database servers we need single interface i.e. suitable with database servers. For that purpose OLAP tool is used for data integration, when ever data analysis is required to analyze that Specific domain. Here all data resources are considered as single entity and these are created as new data schema which is based on the requirement.

http://technet.microsoft.com/en-us/library/Cc966398.olap01_big(en-us,TechNet.10).gif

http://technet.microsoft.com/en-us/library/Cc966398.olap01_big%28en-us,TechNet.10%29.gif

OLAP ARCHITECTURE

Here we provide the interface to access data from different data resources and to perform the operations on individual data segments when it is need for amylase the data. The data marts are data repositories and are used for to amylase the data. The data segments and data marts have their own methodologies to amylase the data.

Cc966398.olap03(en-us,TechNet.10).gif

http://www.google.co.in/imgres?imgurl=http://technet.microsoft.com/en-us/library/Cc966398.olap03_big(en-us,TechNet.10).gif

OLAP is to capture the multidimensional data. In order to make the multidimensional analysis we consider the data from different sectors through loading, transformation, extraction process. We consider the OLAP functionality as interacting with multiple databases by using single interface technology through single query system.

By using database interface developers and end users can interact with different backend data resources like Oracle, MS-Access, MY-SQL and other data segments. These database are used to test and built and debug PL/SQL packages, triggers, procedures and functions.

The end users can create and alter database objects such as tables, views, indexes and constraints. In this project we are providing the interface window

With the greater options like interface’s SQL editor and this SQL Editor provides the easy and efficient way of write and to test the existed scripts and queries and it handles the DBMS by using power full grid environment.

We can observe the query accessing environment by using Block diagram. We mainly focus to access the different data bases to access the single interface to reduce the burden on the OLAP system.

The design process is the heart of any project. The main aim is to translate the project requirements into visualization and graphical representation. In design

Process we deal with various design steps like data design, architecture design, user interface design. The main aim of designing process is to provide the complete project view of implementation, testing and maintenance.

UML DIAGRAMS:

UML stands for Unified modeling Language. It is used for describing, visualizing object oriented system. It is a collection of variety of diagrams for different purposes. Each type of diagram shows easy to understand in visual manner. It specifies how the diagrams are to drawn and specifies meaning of each diagram. It is not dependent on any particular programming language.

In general UML diagram consist of following features.

Entities: Entities are nothing but classes, objects and users.

Relationship lines: It represents the relationships between entities.

Generalization: Generalization relationship is a relationship in which child element acquires the properties from parent element.

ASSOCIATION: Association is a relationship between two classifiers.

DEPENDENCY: Dependency relationship is a relationship in which changes in one element effect on the other element.

UML diagrams are divided into 2 types.

1 .Structural

2. Behavioral

Structural Diagram:

Structural diagram consist of class diagram, component diagram, deployment diagram.

Class diagram: The class diagram is a static diagram. It is not only used for visualizing, describing and documenting different aspects of the system.

It describes the attributes and operations of the class .The class diagrams are widely used in modeling of object oriented systems which are directly interact with the object oriented systems.

The class diagram is a collection of classes, interfaces, associations, collaborations and constraints.

The class diagram is also known as structural diagram.

Component Diagram:

Component diagrams are nothing but components and their relationships. The components are nothing but classes, interfaces and collaborations.

It can also be represented as static implementation of the view of the system. A single component diagram cannot represent the entire system but collection of component diagrams represents the whole system.

The purpose of the component diagram is

To visualize the components of the system.

Describe the organization and relationship among those components.

Deployment Diagram:

Deployment diagram consist of nodes and their relationships. It is used to visualize the physical components of the system. Deployment diagrams are used to describe the static implementation view of the system.

Component diagrams are used to describe the components and deployment diagram shows how they are deployed in hardware.

To visualize the hardware topology of the system.

Describe the hardware components and these are used to deploy the software components.

Behavioral Diagrams:

Every system has two aspects i.e. static and dynamic. Behavioral diagram capture the dynamic aspects of the system.

The behavioral diagram can be divided into 5 types.

Activity diagram

State chart diagram

Use case diagram

Sequence diagram

Collaboration diagram

Activity Diagram: Activity diagrams are nothing but flow from one activity to another activity in a system. The flow can be either sequential or concurrent. It is used to visualize the flow of controls in a system.

Sequential diagram:

Sequence diagram

This sequence diagrams are useful to implementing the functionality of the OLAP system. And access to different data bases with the help of interface technique. First we login into the system and then check the he is authenticated or not then he is authenticated only to view the system requirements. Through that we can significance the data, modify, access.

Use case diagram:

Use case diagram

The complete system is presented as the functionality wise. The OLAP user selects the operations to data analysis, we use the multi dimensional view representation, and user chooses the drivers according to the database. Here we are mainly concentrating on data integration system with the different data bases like my sql, oracle, and flats. Then we can perform the operation like insert, retrieve, update etc…use case represents the sequence of events with user as actor.

Collaboration diagram: It is another form of the interaction diagram. It represents the structural organization of the system by using this we can send and receive the messages.

Data flow diagrams:

Data flow diagrams are uses to picture representation of data flow and show the different levels of abstractions which are facilitate to view the clear picture about the project data flow in each and every step. DFD’s are mainly used for the project requirements which make implementation part, it becomes easy to analyzation. This phase is important for designing part which used to divide the sub parts of the project from top level to bottom level.

Dataflow studies, utilization of the data in every activity. Different levels of data flow diagrams are based on the functional decomposition of the requirements. First it starts with the top layer end of project view then it goes through bottom level of data transmission.

Representations used for data flow diagrams:

Data flow process

Process

Data store

Data Source

Data flow process: it describes the flow of data between into/out of the process, data store and external entity.

Process: process is the task and generates the output from the suitable input.

Data store: it holds the information within in the system; open ended narrow rectangle box represents the data store. It extracted data from different sources.

Source: an external entity is the source of a data flow which is outside of the entity.

Constructing a DFD:

Basic rules for constructing a DFD:

Each and every process must have the data flow from into/out to it.

Data store must have minimum one data flow into/out to it.

A data flow must have from one process to one or more processes.

Data store stores the information from different data sources. Data go through the process

One organization filing system can’t communicate with another, there is process involved.

In DFD, all processes linked with another processes or data stores

Examples for constructing DFD using rules:

Each process must have the minimum one data flow into/out to it.

Each data store must have minimum one data flow from into/out to it.

A data flow must have from one process to one or more processes.

Data store stores the information from different data sources. Data go through the process

One organization filing system can’t communicate with another, there is process involved.

In DFD, all processes linked with another processes or data stores

DFD diagrams are divided into logical or physical. Logical DFD’s used to how to operate the business and it describes the business events. Physical DFD’s are shows the implementation of the system. These are categorized into four types, they are:

Current physical

Current logical

New physical

New logical

Current physical:

In this label indicate with the data entity name of the system. It provides the technology for project development. Same as data stores and data flows are label with specific names of the actual physical channel on where data to be stored like as computer files, computer tapes and documents.

Current logical:

In this frequently physical system are removed from the data processing, so that present system is minimized to its heart to data and the process that transform them in spite of actual physical form.

New physical:

It represents the one and only physical characteristic of the system.

New logical:

It is same as current logical model, but difference is that it differs in the implementation for the new model. If the user entirely satisfies the functionality of the current logical system but had a problem how it was implemented through new logical model will differs from current logical model at the same time as considering extra functions, absolute function removal and inefficient flows recognized.

Rules governing the DFD’s process:

Process can’t have only outputs.

Process can’t have only inputs. If any object has only inputs then it must be a go down.

A process has a verb phrase label.

Relationship between different entities is represented by the entity relationship diagrams. In this every actor and process is considered as an entity and relation. Each entity has its own attributes to indicate dimensions with respect to data analysis.

0.0

Analysis on

Data warehouseLevel 0 DFD:

Olap toolos

In this data warehouse used by OLAP tool. We need to make a decision for analyze on data warehouse in multi dimensional way.

1.0

Multidimensional dataDFD level 1:

2.0

Query processing

0.0

Analysis on

data warehouse

Olap tool

3.0

Data integration

In this level, we can observe the data analysis on multi tasks. In this level divided into sub level parts. The sub levels are considered to next level DFD. In order to make a multi level analysis. We need to consider different issues like data modeling to make the multi dimensional view and in OLAP focusing on cube structure to represent data in multidimensional view.

Level 2 DFD:

1.1

Data location

1.0

Multi

Dimensional data

1.2

Data

Type

In this level divided into two sub levels. If we need consider to sub level divide into multi dimensional tasks. This sub levels consider for location of the data and another sub level is consider to type of the data. This sub levels are consider to next level multi tasks.

Level 3 DFD:

2.1

Query interface

2.0

Query processing Data

Data

In this query processing sub divided into multi task this task is used to interfacing between data source and query processing. Here we considered to various resources from the different domains and we need to combine the data segments into one data warehouse.

Level 4 DFD:

3.0

Data integration Data source

Data source

Data source

In this level DFD divided into sub tasks data integration combines the different data bases like mysql, db2, oracle, flats. In this sub tasks are divided into sub task this multi dimensional tasking useful to make right decision to designing.

IMPLEMENTATION:

This implementation part gives the OLAP system for query access method with single interface to access the data from different data resources like oracle, MS-access and other data resources.

HARDWARE/SOFTWARE REQUIREMENTS:

Implementation phase is based on the design view. This development of this phase is based on the designing part what we proposed in the earlier phase.

This project is implemented by using java, windows environment.

Here we access data from different data resources by using OLAP tool for multidimensional analysis. It can be achieved through data integration then it can be considered as a single representation with the global schema. For that purpose we need prefer the query languages to access data from different data resources. But every data source has own fetching technique such as query language. So in order to overcome this problem we are initializing the interface for all data resources, we don’t want to make any additional connectivity drivers.

TO implement the proper software, we are using java as programming language which is very efficient to handle different platforms like MS-Access, Oracle and some flat files to view the result.

OBJECT ORIENTED PROGRAMING APPROACH:

Oop’s concept is the technique to create programs based on the real world. In this concept objects represents things, classes and other objects represents the certain behavior, properties, type and identity.

Java is object oriented programming language and it understands the functionality of oops in java. In this process we first understand the object fundamentals. In this process we include classes, methods, inheritance, encapsulation, abstraction, polymorphism etc.

Object:

In object oriented programming language objects are considered as basic run time entities. Here we are always focused on the objects. These are building blocks for programming languages. Objects are run time entities to achieve a particular task. Objects are classified into two types depend up on the structure. They are

Physical objects

Logical objects.

Physical objects are electrical components in circuit system and logical objects are computer elements like windows, circuit.

The collaboration between the programming objects and real world objects is to combine the data with function with existing procedural language.

Class:

It plays the important role in object oriented programming language.

Class is nothing but collection of data members and member functions. The basic difference between class and object is object is physical entity it occupies some memory space whereas class is logical entity.

INHERITENCE:

One of the main features of the object oriented programming is inheritance. Inheritance is nothing but the child class acquires the properties from parent class. In oops concept classes can inherit some common behavior and state from others. It helps the better data analysis, reduction of time.

ABSTRACTION:

The process of abstraction in java is used to hide the certain details and it only shows the essential features. It defines the common properties and behavior of the specific classes.

Example is we considered shapes as the main class with the class name where as all shapes like circle, rectangle, squares are the subclasses.

Encapsulation:

It is used to bind the code and data and it provides the security to access the data from different programs. It won’t allow the other user to use the data or code which is controlled by interfaces.

Polymorphism:

It is the ability of an object to take in different forms. The use of polymorphism in oops is when parent class reference is used to refer the child class object.

For example radius can be used for different purposes. The radius can be used with square, circle, and rectangle.

Java is the object oriented oriented programming language. So we considered as programming language for this project.

JAVA:

There are many languages existed but we are choosing java is considered it as a programming language to implement the coding phase. Java is more flexible and easy language to implement any application development and it also runs anywhere in the program.

Java is easy to learn and executing the coding part is easy on any system.

Java is object oriented programming language we can easily initiate and invoke the objects.

Java is platform independent language with the greater flexibility. Platform independency is nothing but we can run the systems on any environment without any limitations.

Java is highly reliable language. The main advantage of java is, it will be set according to the computer environment with the help of JVM.

Java virtual machine:

JVM is abbreviated as Java virtual machine. Java had the platform independent feature and flexibility because of JVM.

The following steps are process of compile and run environment in java

The program can be write using java language and can save the file using .java extension and it is called as source file.

When we compile the .java file, java architecture creates the .class file. The compile process of java is JVM converts the byte code into machine code for computer use. By compilation .java file is not converted into machine language, but intermediate code is similar to assembly language and it is also called as byte code, so byte code is equivalent to source code.

JVM has the class loader to load the .class files and execute the byte code. The

.class file of java API also loaded into the JVM.These files are easily compatible and easy to transport across the network.

Java provides the no of API’s in the form of packages and it can be used in the development of program.

http://www.google.co.in/imgres?imgurl=http://www.proetcon.de/img/fgm_java_architecture_e.png

Product future:

In this project we proposed anew interface. It can handle different data bases and data resources for data integration. Here it can enable to embed with the OLAP tool for analysis of multi dimensional data representation. Here query languages can be useful to handle the data integration and data fetching with single interface. Whenever you need data from single data mart, it can be easy to handle with proposed system.

External interface requirements:

We have external interfaces for this project such as software interface, hardware interface, user interface and communication interface. Let we explain briefly below

User interface:

User interface is designed with user friendly manner for user easily interacts with data base where user able to view the data fields for different responsibilities.

Whatever user choosing the data base to extract the information from data warehouse, we give the condition by using brows option and radio button. Even we can expression with similar type of interface functionalities for the query accessing methods.

Hardware interface:

We need the LAN network, hubs, router, and communication channel using to build a network. High end configuration system is needed to run this application.

Software interface:

Java1.5 or above is required to develop the programs for this project and JAR files for efficient execution.

Communication interface:

TCP/IP protocols used to communication purpose we are using the different types of networks like as wireless network, Bluetooth.

Other non functional requirements:

We consider some non functional requirements for performance, safety, security.

To check the data transmission with accuracy and optimum way of execution speed by using the performance requirements. Hardware component tuning like router or firewalls are related to safety requirement. Security is the one of the most important requirement for security maintains. We protect the system by using the external software like antivirus. It is protect the system from attacks and proper users.

Software quality attributes are reliability, usability, security, availability, scalability and maintainability. All of this are taken care. These descriptions are given below.

Reliability:

This application mosstly using in banking, aerospace communication and medical related fields. These fields are highly sensible and want more safety. This is main constraint in research methodology, but most of the software products ignore it.

Usability:

In general, web applications are deals with the customers and users. Users preferred to work with friendly manner interfaces and it using easy and flexible. It is difficult to expect and provide the availability feature in the software.

Scalability:

If we expanding the software functionality to large environment by using the main requirement of the scalability. In this project, we have condition to embed this software to large networks, then multicast the data by providing the high security levels.

Availability:

Availability states that software must be available at all time without any interruption. It should usable friendly manner and accessible with any OS.