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A table can be considered to be relational as it collect objects of same type i.e. rows.Data gets reside in the table but the ability to get the relational data from database is more important. Relational database always follow integrity rules and it always ensure that it contain accurate information that can be easily accessible.
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Question:-An evaluation of multi-dimensional databases versus relational databases in the support of data warehouse-style queries
The relational database model uses the two dimensional tables consist of rows and columns. The entities are defined from the real world and the real world problem is simulated exactly.
The ER modeling and the nature of the data in normalized which is more popular in the designer of the database. The relational database model is simple to model and design but when it come to the end user then it become more complex than other database models. The queries become more complex and more joins are needed in order to get the required output.
The data is presented to the user in such a way as to represent a hypercube, or multi-dimensional array, where each individual data value is contained within a cell accessible by multiple indexes. To know about the depth of the queries is not best solution for the end users within an organization. In relational analysis there is one basic storage structure, a flat table. This object takes on many different roles depending on how it relates, or joins, to other tables. In a star schema, fact tables and dimension tables describe the relationship between the various tables within the schema. The fact table is usually the main driver within the query.
While multidimensional database is consist of cubes and multidimensional arrays. It means that each row or value is contained within a cell is accessible by multiple indexes.
A multidimensional database – or a multidimensional database management system relate to the ability to rapidly process the data so that the results to the query request can be generated very fast. A number of vendors provide products that use multidimensional databases. Approaches to how data is stored and the user interface vary.
Conceptually, a multidimensional database uses the idea of a data cube to represent the dimensions of data available to a user. For example, “sales” could be viewed in the dimensions of product model, geography, time, or some additional dimension. In this case, “sales” is known as the measure attribute of the data cube and the other dimensions are seen as feature attributes. Additionally, a database creator can define hierarchies and levels within a dimension (for example, state and city levels within a regional hierarchy).
The above example belongs to the student record where three dimensions are results, name and Exam. The results of a student is divided on time and this is the ability of the multi dimensional systems. The multi dimensional system is not only consist of three dimension but it belongs to more than three but when we increase the dimensions then it become more complex and huge in volume to handle.
Oracle has launched the oracle express which is specifically design for the data warehouse manager. It also include the language specifically used to write queries in multi-dimensional environment.
But this is not good approach to get the advantage of multi-dimension schema.
The multi-dimensional model can only be use from the small to medium level of database. While the relational database model can use up to any level.
In relational database schema the access to any number of dimension is possible but in multi-dimensional schema you have only access to the predefined set of dimensions.
The relational database schema uses the high level of resources while the multi-dimensional schema uses very high level of recourses so it has more overhead than the relational schema.
The main advantage of the multi-dimensional is that it is faster in queries for small data set and average for the large data set. But the relational database schema is good for the small data set but average for the medium dataset.
Enhanced Data Presentation and Navigation: The spread sheet views of the data are the output of multi-dimensional databases. These kind of views are not generated by the relational schema or very difficult to generate. You only need to write complex queries e.g. top ten exam results.
Ease of Maintenance: This kind of database is easy to maintain because there is no need to transform the data to view or query but in the relational database which needs the complex index and joins to retrieve the required data from the tables.
Increased Performance: In Multi-dimensional database the performance have achieve more performance than the relational system on the same amount of the data.
Dimension are identified and can be categorized with the business logic. The members of the dimensions are kept in the table. The columns belongs to the hierarchy and they are separated for each level. The OLAP tools keep the data and the basic information about the customer which are presented as long list for every values in the data.
At a very basic level, dimensions provide users with the ability to expand and collapse a list of members. As can be seen in the image on the left side, the member ‘Channel Total’ has been expanded to show all the members below it. Various ad-hoc query-reporting tools also provide simple drill operations. However, these drill operations tend to be unstructured in their approach and do not truly extend the query capability for the user.
Levels are used to provide the sequence of the individual entity values that belongs to each dimension.
Levels are also used in searching key groups in a particular dimension. Level helps the user to do the task very quickly and user can easily select the data from the dimension. This concept is not adopted in the relational database schema where all the data reside at the same level and if we want to select the date we need to keep in mind that there is no difference between rows of the table. But it provides a very powerful feature to the multi-dimensional query model. The dimensional model give the users an opportunity to create the static ad dynamic structures which help in query optimization and in quick response.
There can be best solution for the queries to automatically rewrite themselves. The multi-dimensional model supports this rich feature. Users can create queries based on structural conditions, which interact directly with the structural components (levels) of the dimension. So, we could rewrite the above query as follows:
A hierarchy provides order to the levels within a dimension. It also details the parent/child relationship between the dimension keys. Most hierarchies are typically defined as level-based where the structure is simple in terms of how each level relates to the next one in the hierarchy. For example, a level-based geography hierarchy might appear as follows: All Regions
A typical multi-hierarchy scenario could be created within a Geography dimension as follows: All Regions State Country City
Dimensional Query Conditions
The examples shown so far have been very simple and demonstrate that each dimension can be queried separately, within its own space. The examples have not indicated any direct link to specific columns within a fact table. And this is because multi-dimensional analysis allows users to interact with each dimension in isolation to determine which dimension values should be displayed in the target presentation.
Dimensional queries are also rarely based on a single step, but consist of a number of steps. In relational modeling users must understand the semantics of SQL and the importance of AND/OR rules in determining which values are returned. The main issue with relational modeling is not generally with the terms, but that the user has to understand the impact on the whole query and not just the single column they are trying to filter. Users typically construct queries by isolating the key components of a query and trying to define conditions in isolation. The relational model forces users to manipulate all the elements as a whole, which tends to lead to confusion and unexpected result sets.
In contrast, the multi-dimensional model allows users to filter each dimension in isolation and uses more friendly terms such as Add, Keep and Remove. Users can quickly and easily create multi step queries, like the following:
Give me my top 5 performing cities in California, based on growth in margin based on prior year
Becomes a simple two-step query:
Start with California
Keep top 5 cities based on Margin % Growth Prior Year
Notice the syntax of the query closely mirrors the way a user would describe the query. To make the creation of queries as simple as possible, Oracle OLAP BI Dimensional Query Builder provides templates that guide the user through the process of creating analytical conditions.
The following types of templates are provided to help users create both data driven queries and queries based on structural metadata:
|Exception||Sales Revenue > 1000Sales Revenue > CostsSales Revenue > Costs + 10
Sales Revenue > Costs – 10%
Sales Revenue within 10 of Costs
Sales Revenue not within 10% of Costs
Sales Revenue between 5000 and 10000
|Top/Bottom||Top 10 based on Sales RevenueTop 10% based on Sales RevenueMaking up the top 10% of Sales Revenue|
|Hierarchy||Children of All RegionsAll Levels in CaliforniaAll Regions|
This is very special in dimension model. It basically requires more information about the data, the related terms the time and when the data is recorded or what is the member date and time.
By utilizing these additional attributes the oracle uses the multi-dimensional model has some more features and enhance feature as well.
When dimension table has been built then there are some templates which required some calculations to be done. These calculations are based on the time and some other attributes as well.
• Prior Value
• Difference from Prior Period
• Percent Difference from Prior Period
• Future Value
• Moving Average
• Moving Maximum
• Moving Minimum
• Moving Total
• Year to Date
If we use the following queries in the traditional object relational database modeling
- “What is the total amount of receipts recorded last year per state and per product category?”
- “What is the relationship between the trend of PC manufacturers’ shares and quarter gains over the last five years?”
- “Which orders maximize receipts?”
It is clear that using traditional languages, such as SQL, to express these types of queries can be a very difficult task for inexperienced users. It is also clear that running these types of queries against operational databases would result in an unacceptably long response time.
product → type → category
store → city → state
In summary, a multidimensional cube hinges on a fact relevant to decision-making. It shows a set of events for which numeric measures provide a quantitative description. Each cube axis shows a possible analysis dimension. Each dimension can be analyzed at different detail levels specified by hierarchically structured attributes.
In contrast of what we believe the multi-dimensional model does not replace the conceptual or relational model. But the normalization of the conceptual model and then transformation of this conceptual model into physical model will lead to the success of any data warehouse project. In my point of view it is near to impossible to manage a set of data mart without relational schema.
Every organization knows that they will have very huge data in the near future so they want to make such model that can handle the huge amount of data.
To allow users to intelligently query and analyze information organizations spend large amounts of time and resources adding structure and order to their relational database schemas. This structure is intended to simplify the analysis process for end users. The zenith of this path to structure and order is the multi-dimensional model.
The multi dimensional schema allows the user to access the data very quickly and easy to transform into information. The multidimensional schema also become self supporting when user interact with the database.