scholarly journals Redips: Backlink search and analysis on the Web for business intelligence analysis

2007 ◽  
Vol 58 (3) ◽  
pp. 351-365 ◽  
Author(s):  
Michael Chau ◽  
Boby Shiu ◽  
Ivy Chan ◽  
Hsinchun Chen
Keyword(s):  
Business Intelligence ◽  
Intelligence Analysis ◽  
The Web

scholarly journals Penerapan Business Intelligence Dalam Upaya Menigkatkan Penjualan dan Pemasaran Pakaian Pada CV. Ryan Bali Garment Berbasis Web

KOMTEKINFO ◽  
2021 ◽  
pp. 220-224
Author(s):  
Wulan Ayu Safitri ◽  
Febri Hadi ◽  
Shary Armonitha Lusinia
Keyword(s):  
Business Intelligence ◽  
Intelligence Analysis ◽  
Web Based ◽  
Sales And Marketing ◽  
Web Based System ◽  
The Web

Sales and marketing on CV Ryan Bali Garment is a problem that needs serious attention. Seeing the increase in sales, there is no increase in the web-based system built with the help of Business Intelligence analysis in the hope of finding out where the problems are that have an impact on sales. The system built aims to increase sales and marketing. In this study, researchers conducted analysis and testing of problems that arise which resulted in decreased sales so that the owner can directly take action to increase sales and marketing in that place. So that the system that is built can be a solution to the problems obtained.

scholarly journals Mining the web for business intelligence: Homepage analysis in the internet era

2004 ◽  
Vol 12 (1) ◽  
pp. 32-54 ◽  
Author(s):  
Kin-nam Lau ◽  
Kam-hon Lee ◽  
Ying Ho ◽  
Pong-yuen Lam
Keyword(s):  
Business Intelligence ◽  
The Internet ◽  
The Web

Pervasive Business Intelligence Platform to Support the Decision-Making Process in Waiting Lists

2021 ◽  
pp. 848-863
Author(s):  
Marisa Esteves ◽  
Filipe Miranda ◽  
António Abelha
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Business Intelligence ◽  
Waiting Times ◽  
Waiting Lists ◽  
Decision Making Process ◽  
The North ◽  
Health Institutions ◽  
Private Network ◽  
The Web

In recent years, the increase of average waiting times in waiting lists is an issue that has been felt in health institutions. Thus, the implementation of new administrative measures to improve the management of these organizations may be required. Hereupon, the aim of this present work is to support the decision-making process in appointments and surgeries waiting lists in a hospital located in the north of Portugal, through a pervasive Business Intelligence platform that can be accessed anywhere and anytime by any device connected within the hospital's private network. By representing information that facilitate the analysis of information and knowledge extraction, the Web tool allows the identification in real-time of average waiting times outside the outlined patterns. Thereby, the developed platform permits their identification, enabling their further understanding in order to take the necessary measures. Thus, the main purpose is to enable the reduction of average waiting times through the analysis of information in order to, subsequently, ensure the satisfaction of patients.

scholarly journals Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems

2020 ◽  
Vol 202 ◽  
pp. 16005
Author(s):  
Chashif Syadzali ◽  
Suryono Suryono ◽  
Jatmiko Endro Suseno
Keyword(s):  
Business Intelligence ◽  
Nearest Neighbor ◽  
Customer Behavior ◽  
Training Data ◽  
Business Strategies ◽  
Intelligence Analysis ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

Discovering Business Intelligence from the Subjective Web Data

2011 ◽  
Vol 2 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Ranjit Bose
Keyword(s):  
Business Intelligence ◽  
Opinion Mining ◽  
Research Literature ◽  
Future Research ◽  
Sources Of Information ◽  
Web Data ◽  
Online Word Of Mouth ◽  
Marketing Intelligence ◽  
Automated Discovery ◽  
The Web

The online word-of-mouth behavior that exists today in the Web represents new and measurable sources of information. The automated discovery or mining of consumer opinions from these sources is of great importance for marketing intelligence and product benchmarking. Techniques are now being developed to effectively and easily mine the consumer opinions from the Web data and to timely deliver them to companies and individual consumers. This study investigates this emerging field named ‘opinion mining’ in terms of what it is, what it can do, and how it could be used effectively for business intelligence (BI). A rigorous review of the research literature on opinion mining is conducted to explore its current state, issues and challenges for its use in developing business applications for competitive advantage. The study aims to assist business managers to better understand the current opportunities and challenges in using opinion mining for deriving BI. Future research directions for further development of the field are also identified.

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