Study on the Application of Data Mining Based on Campus Card Platform

2013 ◽  
Vol 846-847 ◽  
pp. 977-980 ◽  
Author(s):  
Yuan Qian ◽  
Quan Shi
Keyword(s):  
College Students ◽  
Data Mining ◽  
Time Series ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Sql Server ◽  
Policy Makers ◽  
University Policy ◽  
Development Tools ◽  
Intelligence Development

The thesis uses data in the database of campus card platform as the analysis object, combined with statistical methods and data mining technology to analyze the students consumption and the situation of the canteens. We use the Microsoft .NET and SQL Server 2008 business intelligence development tools to mine and analyze these data; know canteens consumption and learn about the business status and the popular shops of the canteen by using the K-means algorithm; analyze and predict students behavior and the situation of the canteen by using time series algorithm. It is convenient to manage the college students, and provide data support for university policy makers and shoppers to make plans.

Introduction to Data Mining and its Applications to Manufacturing

2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Introduction to Data Mining and its Applications to Manufacturing

2011 ◽  
pp. 144-166
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Business Intelligence Applications in Retail Business: OLAP, Data Mining & Reporting Services

2010 ◽  
Vol 09 (02) ◽  
pp. 171-181 ◽  
Author(s):  
Ipek Deveci Kocakoç ◽  
Sabri Erdem
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Sql Server ◽  
Daily Growth ◽  
Data Mining Algorithms ◽  
Retail Business ◽  
Information Technology Investments ◽  
Crucial Information ◽  
Mining Algorithms

As a result of today's competitive business environment, companies have been trying to improve the utilization of funds effectively in their budgets for information technology investments. These companies retrieve more information with the same set of resources by means of business intelligence methods. According to Rubin (Chabrow, 2004) IT budgets are not simply declining or levelling off, rather, companies are shifting from a pure cost-cut mode to a model that emphasises agility and efficiency. Tremendous daily growth of the company data requires more funds and investment for establishing the technologies and infrastructure necessary for gathering fast and crucial information that supports the decision making process. This necessity gave birth to various business intelligence methods, which mainly aim to process mass amount of collected data from their existing application, and represent it in a way with which companies can apply to their daily competitive decisions. This application primarily concerns the implementation of business intelligence for a retail business company. The aim is to implement built-in business intelligence solutions of the Microsoft SQL Server that holds the commercial information of the company for the past three years. The customer company has already been using Microsoft products. The key items used for analyzing data are sales, momentary inventory and logistics information. The application can be grouped in five main areas: Building the data warehouse, constructing OLAP cubes, applying data mining algorithms on OLAP cubes, representing the results in reports with reporting services, and implementation.

The Analysis and Data Mining of Students’ Online Data Based on Digital Campus

2013 ◽  
Vol 397-400 ◽  
pp. 2326-2329
Author(s):  
Quan Shi ◽  
Yuan Qian ◽  
Yan Gong ◽  
Xiao Min Zhu ◽  
Jun Qiang Yan
Keyword(s):  
Data Mining ◽  
Original Data ◽  
Sql Server ◽  
The Internet ◽  
Campus Network ◽  
Online Data ◽  
Digital Campus ◽  
Policy Makers ◽  
Internet Users ◽  
The Web

The research takes the campus network users logging on the Internet as the analysis object, using the data preprocessing technology to clean up the original data, combined with statistical analysis and data mining technology to analyze the users access log records, which will result in the form of dynamic charts for Web display, by using Microsoft SQL Server 2008 and Microsoft Visual Studio 2010. Take use of intelligent .NET platform, combined with K-means algorithm to cluster the students information. DMX (Data Mining Extensions) will be used to show the mining results on the Web. The realization of the system can not only carry on correct guide to Internet users and regulate the behavior of students, but also have important guiding meaning to managers and policy makers for analysis and making decision.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Design and Implementation of Room Management System of Hotel Based on J2EE

2014 ◽  
Vol 599-601 ◽  
pp. 2096-2099
Author(s):  
Chong Jie Dong
Keyword(s):  
Management System ◽  
Hotel Industry ◽  
Sql Server ◽  
Workflow Technology ◽  
Housing Management ◽  
J2ee Platform ◽  
Design And Implementation ◽  
Management Efficiency ◽  
Development Tools

For the growing prosperity of the hotel industry,to improve the efficiency of hotel housing management, the paper adopts J2EE platform combine with JBPM workflow technology as a workflow development tools,using the SQL Server 2008 as database to develop hotel housing management system with friendly interface,completely functions and good security.The application of the system reduces the use cost of room management of hotel, improving the room management efficiency of the hotel.

scholarly journals The Upworthy Research Archive, a time series of 32,487 experiments in U.S. media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
J. Nathan Matias ◽  
Kevin Munger ◽  
Marianne Aubin Le Quere ◽  
Charles Ebersole
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Open Access ◽  
Randomized Trial ◽  
Digital Technology ◽  
Behavioral Experiments ◽  
Media Politics ◽  
B Test ◽  
Scientific Questions ◽  
Advance Knowledge

AbstractThe pursuit of audience attention online has led organizations to conduct thousands of behavioral experiments each year in media, politics, activism, and digital technology. One pioneer of A/B tests was Upworthy.com, a U.S. media publisher that conducted a randomized trial for every article they published. Each experiment tested variations in a headline and image “package,” recording how many randomly-assigned viewers selected each variation. While none of these tests were designed to answer scientific questions, scientists can advance knowledge by meta-analyzing and data-mining the tens of thousands of experiments Upworthy conducted. This archive records the stimuli and outcome for every A/B test fielded by Upworthy between January 24, 2013 and April 30, 2015. In total, the archive includes 32,487 experiments, 150,817 experiment arms, and 538,272,878 participant assignments. The open access dataset is organized to support exploratory and confirmatory research, as well as meta-scientific research on ways that scientists make use of the archive.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Sign in / Sign up

Export Citation Format

Share Document