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The different storage models that are available in OLAP are as follows:
They are advantages and disadvantages of each of these storage models that are available in OLAP.
As the name itself depicts “MOLAP”, i.e. Multidimensional.
In this type of data storage, the data is stored in multidimensional cubes and not in the standard relational databases.
The advantage of using MOLAP is:
The query performance is excellent, this is because the data is stored in multidimensional cubes. Also, the calculations are pre-generated when a cube is created.
The disadvantage of using MOLAP is:
As the name suggests that, the data is stored in the form of a relational database.
The advantages of using ROLAP is:
1. As the data is stored in relational databases, it can handle a huge amount of data storage.
2. All the functionalities are available as this is a relational database.
The disadvantages of using ROLAP is:
1. It is comparatively slow.
2. All the limitations that apply to SQL, the same applies to ROLAP too
The advantages of using MOLAP is:
It is a process where the data is extracted from various sources. Further, the data is cleansed and stored.
|Related Article: Data Warehousing Introduction|
The above can be explained by taking a simple example:
Let’s take a software company where all of their project information is stored. This is nothing but Data Warehousing.
Accessing a particular project and identifying the Profit and Loss statement for that project can be considered as Data Mining.
|Related Article: Define Data Mining|
Data purging is an important step in maintaining appropriate data in the database.
Basically deleting unnecessary data or rows which have NULL values from the database is nothing but data purging. So if there is a need to load fresh data into the database table we need to utilized database purging activity. This will clear all unnecessary data in the database and helps in maintaining clean and meaningful data.
Data purging is a process where junk data that exists in the database gets cleared out.
Cube is nothing but a data storage place where the data can be stored and makes it easier for the user to deal with his/her reporting tasks. It helps expedite the data analysis process.
Let’s say the data related to an employee is stored in the form of a cube. If you are evaluating the user performance based on a weekly, monthly basis then week and month are considered to be the dimensions of the cube.
Data Mining is a very important process where it could be used to validate and screen the data how it is coming through and the process can be defined based on the data mining results. By doing these activities, the existing process can be modified.
They are widely used in the following industries :
By following the standard principles a lot of illegal activities can be identified and dealt with. As the internet has evolved a lot of loops holes also evolved at the same time.
|Related Article: Difference between OLAP and OLTP|
They are three different stages in Data Mining, they are as follows:
Exploration is a stage where a lot of activities revolve around the preparation and collection of different data sets. So activities like cleaning, transformation are also included. Based on the data sets available, different tools are necessary to analyze the data.
In this stage, the data sets are validated by applying different models where the data sets are compared for best performance. This particular step is called pattern identification. This is a tedious process because the user has to identify which pattern is best suitable for easy predictions.
Based on the previous step, the best pattern is applied for the data sets and it is used to generate predictions and helps in estimating expected outcomes.
Discrete data can be classified as a defined data or finite data. That has meaning to itself. For example Mobile numbers, gender.
Continuous data is nothing but data that continuously changes in an orderly fashion. The example for continuous data is “Age”.
Model is an important factor in Data Mining activities, it defines and helps the algorithms in terms of making decisions and pattern matching. The second step is that they evaluate different models that are available and select the best suitable model for validating the data sets.
Ans: The Naive Bayes Algorithm is widely used to generate mining models. These models are generally used to identify the relationship between the input columns and the predicated columns that are available. This algorithm is widely used during the initial stages of the explorations.
This algorithm is mainly used in recommendation engines for a specific market-based analysis.
So the input for this algorithm would be the products or items that are bought by a specific customer, based on that purchase a recommendation engine will predict the best suitable products for the customers.
As the name itself states that the data is collected at different points which occurs at the sequence of events. The different data sets are analyzed based on the sequence of data sets that occur based on the events. The data sets are analyzed and then the best possible data input will be determined for clustering.
A sequence clustering algorithm will help the organization to specify a particular path to introduce a new product that has similar characteristics in a retail warehouse.
So Data Mining is primarily responsible to understand and get meaningful data from the data sets that are stored in the database.
In terms of exploring the data in data mining is definitely helpful because it can be used in the following areas:
A large amount of data is cleaned as per the requirement and can be transformed into meaningful data which can be helpful for decision making at the executive level.
Data mining is really helpful with the following types of data:
Based on the data analyzed, the information can be analyzed and appropriate relationships are defined.
With the use of SQL Server, data mining offers an add-on for MS office 2007. This will help to identify and discover the relationships with the data. This data is helpful in the future for enhanced analysis.
The add-on is called “ Data Mining client for excel”. With this the users will be able to first prepare data, build and further manage and evaluate the data where the final output will predicting results.
DMX consists of two types of statements in general.
This is used to define and create new models and structures.
As the name itself depicts, the data is manipulated based on the requirement.
The usage is explained in detail by picking up an example:
With the syntax, it is
SELECT FROM. CONTENT (DMX)
The different functions of data mining are as follows:
As the name itself is self-explanatory, the data is aggregated altogether where a cube can be constructed for data analysis purposes.
It is a process where low-level data is replaced by high-level concepts so the data can be generalized and meaningful.
This is a model which is primarily used to analyze a particular training data set and it has training data samples that are selected from a selected population.
This model is primarily used for providing estimation for a particular class by selecting test samples randomly. The term classification is usually determined by identifying a known class for specific unknown data.
The term cluster analysis is an important human activity that is widely used in different applications. To be specific, this type of analysis is used in market research, pattern recognition, data analysis, and image processing.
The main aspect of a data warehouse is that the data evolves based on the time frame and it is difficult to predict the behavior because of its ad hoc environment. The database tuning is much difficult in an OLTP environment because of its ad hoc and real-time transaction loads. Due to its nature, the need for data warehouse tuning is necessary and it will change the way how the data is utilized based on the need.
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.