olap for educational data analysis

1. Olap For Educational Data
Laxmi Chandolia
Central University of Rajasthan
Mentor : Dr. Deepika prakash
17 April 2017
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2. Abstract
Our aim to invesigate the problems which come in way of managment
professionals of training centers, education and research institutions across
the country and finding possible solutions. Despite having many
advantages of learning platforms, there are still problems in their efficient
use. Factors affecting sucess of students and institutions are causing
challenges for managers and other professionals in the areas of teaching
and learning. Business intelligence strategies and analysis of on line tools
can be used in order to overcome these problems. In this research on
business intelligence, analytical databases, and investigating online
processing system (OLAP) came out as possible solution for our problem.
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3. Introduction
In a successful education system takes all decisions made based on the
data and data analysis methods have played a major role in deciding the
quality of these systems. Currently, the data warehouse with on-line
analytical processing tools for database analysis is one of the best ways to
collect and analyze information on education systems have been proposed
and the main aim is to support decision making in such systems.
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4. What is Olap
OLAP- Online Analytical processing
First Olap database was Express, intoduced in 1970
OLAP (online analytical processing) is computer processing that
enables a user to easily and selectively extract and view data from
different points of view.
The term OLAP was created as a slight modification of the
traditional database term online transaction processing (OLTP).
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5. OLTP v/s OLAP
OLTP
Online transaction processing
Short transactions and Simple queries
Touch small partitions of data
Frequent updates
capturing and inputting the data
OLAP
Online analytical processing
Long transactions and Long queries
Touch large partiotions of data
Infrequent updates
Extracting and utilizing the data for analytical purposes
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6. Educational Systems
Recently, educational systems are on the one side to increase the efficiency
and quality are encountering the new difficulties and the requirements of
the dynamic economy and other native or local existing businesses. This
poses the need to establish and institutionalize a comprehensive systems
for supporting decision making and distributed training.
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7. Methodology
The shape that we utilize to put the tables is called ”Schema”.
figure 1. Student Database Schema
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8. Methodology
Star schema is suitable for our DW students system
Common schema, star schema where the warehouse with a central
table called the fact table contains a large amount of data without
repetition and smaller tables called dimension tables are defined.
Fact table
-updated frequently
-very large
Dimension table
-updated infrequently
-not as large
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9. Methodology
Kimball’s star methodology
figure 2. proposed star schema
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10. figure 3. proposed star schema
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11. Methodology
Inmon’s snowflake methodology
star schema is a schema similar to the snow flake of the table is split into
subtables. The difference is that your tables are normalized snow flake
scheme and redundancy in them has decreased.
figure 4. proposed snow flake schema
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12. types of OLAP
Multidimensional OLAP systems (MOLAP)
relational OLAP systems (ROLAP)
Hybrid OLAP systems (HOLAP)
Specialized SQL Servers
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13. Conclusion
In today’s competitive environment is changing rapidly, and a large
amount of data centers and educational institutions are generated by
online transaction processing systems.In this regard, the research
databases, online analytical processing system consisting of a warehouse
and were made to pay the other hand, data collected by students from
different systems such learning management system, e-learning
management system, etc., and on the other hand, they electronic content
integration and teachers to provide the desired form,learning centers
provide managers and other stakeholders. data warehouse also provides
the possibility to analytical data warehouses and operational data are
independent these tools make it possible for managers to provide key
performance indicators from different perspectives and at different levels of
detail are observed, and thus is an effective step in order to provide the
efficiency of the academic community information resources.
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14. Future Work
Possible follow-up work should therefore be aiming at reviewing
different parts, requirements specification, the requirements
architecture itself and the verification with practitioners who can
contribute a lot with deep implementation experience.
The verification should be extended to include this experience.
Possibilities are reviews of the document and description of the
reference architecture by practitioner, interviews to focus on certain
points of the reference architecture and to gather scenarios to apply
the reference architecture to and test its fit.
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15. References
John wiley and sons, 2005
Inmon, William H. Building the data warehouse.
John wiley and sons, 2011
Kimball, Ralph, and Margy Ross. The data warehouse toolkit: the complete guide
to dimensional modeling.
Shirin Mirabedini , Seyedeh Fatemeh Nourani
The Research on OLAP for Educational Data Analysis
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16. THANK YOU
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