2017 International Conference on Smart City and Systems Engineering
Research on the Application of Data Mining Technology in Campus Card System
Dan Su, Xiaoxi Liu, Tongjun Jiang, Zhuoyue Li
Power Grid Construction Training Department, State Grid of China Technology College, Jinan 250002, China
students, at the same time, the system itself also
accumulated tens of millions of water records, the school
can grasp the user's daily life and learning behavior on
campus by these business records [4].
Abstract-Campus card system in generated a lot of data
during it’s operation, and the system itself cannot analyze
these data. How it can be learned from these massive,
outdated data for student management to assist
decision-making becomes a very realistic subject. This paper
takes the transaction data of campus card as the research
object, and uses the comprehensive application of data
warehouse, online analysis and processing, data mining and
so on to excavate useful information hidden in campus card
transaction. This paper first introduces the data warehouse,
data mining technology,then it analyzes the consumption
behavior of the student canteen, and then builds the data
warehouse model according to the analysis of the theme, sets
the dimension, selects the granularity,from the card business
system we extract the appropriate data for cleaning,
conversion and loading into the data warehouse, finally,
through the data mining technology we analyze the data to
find the rules for the school decision-makers and provide the
decision support for the relevant management departments.
II.
In recent years, in order to integrate the campus
management system to achieve unified management, the
major colleges and universities establishes college one
card system on the basis of the existing campus I C card.
In order to achieve this goal, the first thing is to build a
information platform for the campus to share[5]; but also
to take into account of all management departments for the
management of business, management processes and
management objectives of the personalized requirements
to ensure the realization of the application function. After
completion of these jobs, it can not only improves the
school's management skills, but also enhances the quality
of school services for students.
(1) Effective use of information systems
Students can easily get help in all aspects of life from
the use of college card. At the same time,by the using of
the card system, making the school can be more
convenient and effective in the use of information systems,
build a comprehensive integration of campus applications.
(2)Saving human resources, making the school devout
greater energy into teaching and research work
In the early days, campus management as a very
important work, which needs to invest a lot of manpower
in the aspects of teacher and student management,
accommodation management and the charge management,
etc.,
After the card system is put into use, we saved a lot
of human resources to ease the school's pressure and
workload by the access control, self-help payment,
deduction,
information management
and other
applications, the use of a unified database.
Key Words: One-Card; data mining; association
analysis
I.
INTRODUCTION
University "card" refers to the campus as long as
you can pay the campus internal services can use the
campus card to pay, and has a unique identity
authentication system, based on the advantages of the
application of intelligence, it’s a multi-angle school
service system based on the combination of
communication
network
technology[1],
school
information management, university network resources.
Campus card is a set of data sharing, identity
authentication, financial consumption and many other
functions in one of the information integration system[2],
university card system, includes dining, consumption,
attendance, examination registration, book borrowing and
other business systems[3]. Campus card provides quality
and efficient information services for teachers and
(3)Enhance financial management capacity
The campus card provides an effective platform for
students and teachers to make small payments, and all
consumption is handled directly by the school's back
office, reducing the need for man to deal with cash, thus
reducing the possibility of various types of economic
crime and illegal operation. You can timely, effectively
and realistically understand the business situation by one
card system, thereby enhancing the school's financial
management and regulatory capacity.
(4)Alleviate financial pressure, and enhance the
level of regulation
University card system is often more closely with the
bank management, teachers and students can use the
banking system to do transfer, which makes the school can
quickly finance. With many teachers and students in
campus, these funds flooding into the school account, will
form a huge precipitation,school can reduce the possibility
978-1-5386-1401-3/17 $31.00 © 2017 IEEE
DOI 10.1109/ICSCSE.2017.56
THE ROLE OF COLLEGE CARD
of outflow of this part of funds through macro-control.
With these funds, the school will be able to run smoothly.
And unified management of funds, can improve the
rationality of the use of funds, thereby easing the school's
financial pressure.
(5)Strengthen environmental protection
In general, I C card will choose P V C material, the
material itself is easy to cause environmental pollution.
Assuming that a college has 50,000 people, before the
usage of card system, each student needs to hold a student
identity card, dining card, borrow card, on the machine
card four cards to ensure their life in school.Assuming the
grads each year reaches 10,000 people, then you need to
write off 40,000 cards,if a city has such five schools, then
the city needs to destroy 20 million cards per year,
continued in a large number of waste of resources, at the
same time, increasing the pollution of the environment.
"Card" management system investment, making the per
199
person card volume from four to one, there is a
significantly reduction of destroyed cards during
graduation year in order to achieve the effective usage of
resources.
At the same time, the reduction in holdings, then the
cost of purchasing cards will be reduced accordingly, it is
easier for school to choose environmentally friendly
materials, reducing the impact of pollution.
III.
reasoning is the basic tool for correcting the probability
distribution of a data set after knowing the new
information, dealing with the classification problem in
data mining, regression analysis is used to find the optimal
model for the relationship between an input variable and
an output variable. There is a linear regression in the
regression analysis that describes the relationship between
the change trend of a variable and the value of another
variable, there is also a logarithmic regression for
predicting the probability of some events. The variance
analysis in the statistical method is generally used to
analyze the effects of estimating the regression line's
performance and the independent variables on the final
regression, which is one of the powerful tools of the result
of many mining applications.
(2)Association rules
The association rule is a simple and practical analysis
rule, which describes the law and pattern of some attribute
in one thing at the same time, which is one of the most
mature main technologies in data mining. Association
rules in the field of data mining is widely used in large
data sets to find a meaningful relationship between the
data, one of the reasons is that it is not only selected by a
dependent variable. Most association rule mining
algorithms can discover all the relationships that are
hidden in the mining data without omission. However,
DATA MINING TECHNOLOGY
A.The Definition of Data Mining
Data Mining (Data Mining) is a process of the
excavation of implied, unknown but potentially useful
information from a large number of incomplete, noisy,
fuzzy, random practical application data. The
manifestations of these information are: rules, concepts,
laws and patterns. It helps decision makers analyze
historical data and current data and discover hidden
relationships and patterns to predict future possible
behavior.The process of data mining is also called the
process of knowledge discovery.
B. Data mining method
(1)statistical methods
Traditional statistics provide a number of
discriminant and regression analysis methods for data
mining. Commonly used techniques such as Bayesian
reasoning, regression analysis, variance analysis. Bayesian
not all the relationships between the attributes obtained
through the association have practical application value.
We must evaluate these rules effectively, filter meaningful
association rules.
(3)Cluster analysis
Cluster analysis is based on the criteria associated
with the selected samples to be divided into several groups,
the same group of samples with high similarity, different
groups are different, commonly used techniques have split
algorithm, cohesion algorithm, Clustering and incremental
clustering. Clustering method is suitable for exploring the
internal relations between the samples, so as to make a
reasonable evaluation of the sample structure, in addition,
cluster analysis is also used for the detection of isolated
points. The classes that are not derived from the clustering
algorithm are effective for decision making. Before using
an algorithm, the clustering trend of the data is usually
checked first.
(4)Decision tree method.
Decision tree learning is a method of approximating
discrete objective functions by classifying an instance
from a root node to a leaf node to classify an instance. The
leaf node is the classification of the instance.
Each node on the tree illustrates a test of an attribute of
the instance, and each subsequent branch of the node
corresponds to a possible value of the attribute. The
method of sorting the instance is from the root node of the
tree, Test the properties specified by this node, and then
move down the corresponding branch of the attribute
value for the given instance. Decision tree method is
applied to the classification of data mining.
(5)Neural Networks
The neural network is based on the mathematical
model of self-learning, which can analyze a large number
of complex data and can complete the extremely complex
pattern extraction and trend analysis for the human brain
or other computer, neural networks can be expressed as
either a guided learning or a non-guided cluster,
whichever is the value of the input to the neural
network.Artificial neuron network simulates the structure
of human brain neurons, and establishes three kinds of
neural networks, with non-linear mapping characteristics,
information storage, parallel processing and global
collective action, high self-learning, self-organization and
self-adaptation.
(6) Genetic Algorithms
It is a method of applying genetic principles and
natural selection mechanisms to search for optimal
solutions. In data mining, it is used to find the optimal set
of parameters to achieve classification, estimation and
prediction. This method first produces a set of solutions,
and then use recombination, mutation and selection and
other evolutionary processes to get the next generation
solution. As the evolutionary process continues, the poor
solution is discarded, and the optimal solution is gradually
obtained.
(7) Rough Set
The data structure is the decision table. Each data in
the decision table is composed of condition attribute and
decision attribute. The purpose is to determine the
mapping relation between the condition attribute and the
decision attribute by simplifying the decision table, and
finally get a set of rules with attribute attributes to express
the rules of the rules.
C. Data mining process
The data mining process includes the following steps
1.Identify business objects
Accurately define the business, clear the purpose of
data mining
2.data preparation
200
3. Data mining
The resulting conversion of the data mining, in
addition to perfect from the selection of the appropriate
miningalgorithm, the rest of all work can be done
automatically.
4.Result analysis
Explain and evaluate the results, the use of analytic
methods should generally be based on data mining
Data selection: Search all object-related internal and
external data information and select data for data mining
applications;
Data preprocessing: studies the quality of data,
prepares for further analysis, and determines the type of
mining operations to be performed;
Establish an analytic model: establish an analytic
model for mining algorithms, and transform the data into
the key to the success of an analytic model.
operations.
5.Knowledge assimilation
Integrate the knowledge gained from the analysis
into the organizational structure of the business
information system.
IV.
between 400-500 thousand times,the peak hour range of
three meals is 7 -8:00, 11 - 13˗00, 17-20:00 respectively,
breakfast time is relatively concentrated, lunch and dinner
was scattered. 8:00 to 10:00 there are nearly 200,000 times
the amount of credit card, indicating that some students do
not have the first class or late, late to have meal.
APPLICATION AND ANALYSIS OF DATA MINING
TECHNOLOGY IN CATERING DATA
V.
SUMMARY AND PROSPECT
A. Poor students prediction
As the canteen food is relatively cheaper than the
school, by analyzing the number of students dining card,
the amount of consumption and other data, to a certain
extent providing a reference for the selection of poor
students. From the student's credit card number, the
average amount of credit each time, the total amount of
consumption of three angles to study, restrictions are as
follows: the total number of credit cards during the
semester is higher than 350 times, the average amount of
each credit card is less than 2 yuan, the total amount of
consumption is less than 1 000 yuan. By the statistical
summary of the number of dining in the canteen more
consumption but less consumption of some of the student
information, the number of poor students in the school
about 321 people.
With the in-depth use of card system in Shijiazhuang
Institute, there will be more and more relevant data into
the system backstage database in future, such as on the
Internet, hospital clinics, bathing, hairdressing and other
consumer data; Books borrowing, access control, student
attendance and other management data, based on these
data can be multi-angle, deep-seated large data analysis,
the development of the corresponding data analysis and
early warning system for the school's scientific
management and decision support.
Finally, in the process of large data processing, to do
the relevant identity authentication, permission
classification, network isolation, data backup and other
protection work to ensure data security.
B.Count the number of credit card sales
The number of credit cards can reflect the quality of
their meals, cost-effective and other information, the
canteen of all sales outlets to count the number of credit
card, before and after the list of 10 information, after
statistical analysis, can be drawn, the top 10 selling mouth
are close to or more than 50,000 times, of which the
second canteen 6 stalls far ahead, close to 30 million times,
showing its popularity;
After the 10 rice window card number are 8,000 times
the following, at least for the potato powder No. 30 stalls,
less than thousands of times.
Based on the above statistics, indicating that some of
the food or food taste or the price of students not get the
identity of the students, resulting in fewer meals.
C. Meal time distribution statistics
In order to examine the time distribution of students
breakfast, lunch and supper, the meal time distribution
map can be drawn, from which we can see the total
number of credit card consumption is roughly the same,
We would like to acknowledge the support of the
Large data analysis based on the college one-card system
Foundation of State Grid of China Technology College.
ACKNOWLEDGEMENTS
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