Mobile User Data Mining and Its Applications

2011 ◽  
pp. 216-232
Author(s):  
J. Goh
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

Mobile User Data Mining and Its Applications

2008 ◽  
pp. 1519-1538
Author(s):  
John Goh ◽  
David Taniar
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

Mobile User Data Mining and Its Applications

2008 ◽  
pp. 52-70
Author(s):  
John Goh ◽  
David Taniar
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

Mining Matrix Pattern from Mobile Users

2011 ◽  
pp. 232-259
Author(s):  
John Goh
Keyword(s):  
Data Mining ◽  
Geographical Area ◽  
Mobile Node ◽  
Mobile User ◽  
Mobile Users ◽  
Mobile Nodes ◽  
The Matrix ◽  
User Data ◽  
Performance And Evaluation ◽  
Parallel Pattern

Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as frequency pattern (Goh & Taniar, 2004), group pattern (Lim, Wang, Ong, et al., 2003; Wang, Lim, & Hwang, 2003), parallel pattern (Goh & Taniar, 2005) and location dependent mobile user data mining (Goh & Taniar, 2004). Previously proposed methods share the common drawbacks of costly resources that have to be spent in identifying the location of the mobile node and constant updating of the location information. The proposed method aims to address this issue by using the location dependent approach for mobile user data mining. Matrix pattern looks at the mobile nodes from the point of view of a particular fixed location rather than constantly following the mobile node itself. This can be done by using sparse matrix to map the physical location and use the matrix itself for the rest of mining process, rather than identifying the real coordinates of the mobile users. This allows performance efficiency with slight sacrifice in accuracy. As the mobile nodes visit along the mapped physical area, the matrix will be marked and used to perform mobile user data mining. The proposed method further extends itself from a single layer matrix to a multi-layer matrix in order to accommodate mining in different contexts, such as mining the relationship between the theme of food and fashion within a geographical area, thus making it more robust and flexible. The performance and evaluation shows that the proposed method can be used for mobile user data mining.

scholarly journals Increasing Data Anonymity using AES and Privacy Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 3243-3248
Keyword(s):  
Data Mining ◽  
Data Security ◽  
Personal Data ◽  
Data Mining Technique ◽  
Privacy And Security ◽  
Mining Technique ◽  
Before And After ◽  
User Data ◽  
Phase Combination ◽  
Mining Tool

Data security and data preserve privacy had been an important area to a huge in recent years. However, rapid developments in collecting, analyzing, and using personal data had made privacy a very important issue. This thesis had addressed the problem the protect user data in the dataset from attacks internal and attacks external by using combination techniques between security technique, and privacy technique and data mining technique. The research objectives were to determine the privacy and security technique in suitable the dataset, and to implement the combination property with chosen and security technique in order to protect user data in the dataset and to validate by comparing result before and after apply privacy techniques in dataset using chosen data mining tool. The research methodology consists of three phases. the analysis phase, combination techniques phase, and results evaluating phase and for every phase has research objective.

scholarly journals Potential Application of Genomic Technologies in Breeding for Fungal and Oomycete Disease Resistance in Pea

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1260
Author(s):  
Ambuj B. Jha ◽  
Krishna K. Gali ◽  
Zobayer Alam ◽  
V. B. Reddy Lachagari ◽  
Thomas D. Warkentin
Keyword(s):  
Disease Resistance ◽  
Genome Sequence ◽  
Potential Application ◽  
Ascochyta Blight ◽  
Gene Families ◽  
Snp Markers ◽  
Growth And Yield ◽  
Current Status ◽  
Genomic Technologies ◽  
Sequencing Platforms

Growth and yield of pea crops are severely affected by various fungal diseases, including root rot, Ascochyta blight, powdery mildew, and rust, in different parts of the world. Conventional breeding methods have led to enhancement of host plant resistance against these diseases in adapted cultivars, which is the primary option to minimize the yield losses. To support the breeding programs for marker-assisted selection, several successful attempts have been made to detect the genetic loci associated with disease resistance, based on SSR and SNP markers. In recent years, advances in next-generation sequencing platforms, and resulting improvements in high-throughput and economical genotyping methods, have been used to make rapid progress in identification of these loci. The first reference genome sequence of pea was published in 2019 and provides insights on the distribution and architecture of gene families associated with disease resistance. Furthermore, the genome sequence is a resource for anchoring genetic linkage maps, markers identified in multiple studies, identification of candidate genes, and functional genomics studies. The available pea genomic resources and the potential application of genomic technologies for development of disease-resistant cultivars with improved agronomic profile will be discussed, along with the current status of the arising improved pea germplasm.

scholarly journals Penelusuran Informasi Rekam Jejak Alumni Program Studi Informasi, Perpustakaan, dan Kearsipan Universitas Negeri Padang

2019 ◽  
Vol 8 (1) ◽  
pp. 534
Author(s):  
Sawalinar Sawalinar ◽  
Malta Nelisa
Keyword(s):  
Information Technology ◽  
Learning Technology ◽  
Tracer Study ◽  
State University ◽  
The World ◽  
Work World ◽  
High Level ◽  
Use Of Knowledge ◽  
Descriptive Method ◽  
Very High

Abstract This study aims to determine (1) the profile of the graduate, (2) the absorption of graduate in the world of work, (3) the use of knowledge gained by the graduate in the work, (4) the assessment of stakeholders on the ability of graduate. This study uses a descriptive method with a quantitative approach. Data collection uses research instruments. The population in this study was Graduate from the major of Information, Library, and Archives of Padang State University (PS IPK UNP). The sample in this study amounted to 105 graduates who returned the instrument. The results showed, First, the profile of Graduate PS IPK UNP was dominated by female graduates. Most of the graduates are graduates who graduated in 2018. Judging from the length of the study period, the average graduate has a length of the study period of 3 years. Second, the absorption of graduates in the workforce is quite high, with most graduates stating that they are currently working with the time needed to get the job <3 months. Third, the use of knowledge obtained by a graduate at the PS IPK UNP in employment is very high. The science that has a very high level of wear is fieldwork learning, technology mastery skills, and the ability to cooperate in teams. Fourth, the assessment of graduate users (stakeholders) on the ability to graduate in the work world has also been good. The ability of the graduate to be considered good by graduate users (stakeholders) is integrity, expertise based on the fields of science, mastery of information technology, and teamwork.Keywords: Information retrieval, graduate, tracer study

Secure Data Validation and Transmission In Cloud and IoT Through Ban Logic And KP-ABE

2021 ◽  
Vol 11 ◽  
Author(s):  
Narander Kumar ◽  
Jitendra Kumar Samriya
Keyword(s):  
Privacy Preservation ◽  
Data Science ◽  
User Authentication ◽  
Ban Logic ◽  
Data Validation ◽  
Security Measures ◽  
Privacy And Security ◽  
Cloud Data ◽  
Control Approach ◽  
User Data

Background: Cloud computing is a service that is being accelerating its growth in the field of information technology in recent years. Privacy and security are challenging issues for cloud users and providers. Obective: This work aims at ensuring secured validation of user and protects data during transmission for users in a public IoT-cloud environment. Existing security measures however fails by their single level of security, adaptability for large amount of data and reliability. Therefore, to overcome these issues and to achieve a better solution for vulnerable data. Method: The suggested method utilizes a secure transmission in cloud using key policy attribute based encryption (KPABE). Initially, user authentication is verified. Then the user data is encrypted with the help of KP-ABE algorithm. Finally, data validation and privacy preservation are done by Burrows-Abadi-Needham (BAN) logic. This verified, and shows that the proposed encryption is correct, secure and efficient to prevent unauthorized access and prevention of data leakage so that less chances of data/identity, theft of a user is the analysis and performed by KP-ABE, that is access control approach. Results: Here the method attains the maximum of 88.35% of validation accuracy with a minimum 8.78ms encryption time, which is better when, compared to the existing methods. The proposed mechanism is done by MATLAB. The performance of the implemented method is calculated based on the time of encryption and decryption, execution time and validation accuracy. Conclusion: Thus the proposed approach attains the high IoT-cloud data security and increases the speed for validation and transmission with high accuracy and used for cyber data science processing.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Data Mining in Health Care Applications

Data Mining ◽  
2011 ◽  
pp. 350-365 ◽  
Author(s):  
Fay Cobb Payton
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Care Delivery ◽  
Patient Data ◽  
Organizational Level ◽  
Privacy And Security ◽  
Political Issues ◽  
Health Care Applications ◽  
Community Data ◽  
The Individual

Recent attention has turned to the healthcare industry and its use of voluntary community health information network (CHIN) models for e-health and care delivery. This chapter suggests that competition, economic dimensions, political issues, and a group of enablers are the primary determinants of implementation success. Most critical to these implementations is the issue of data management and utilization. Thus, health care organizations are finding value as well as strategic applications to mining patient data, in general, and community data, in particular. While significant gains can be obtained and have been noted at the organizational level of analysis, much attention has been given to the individual, where the focal points have centered on privacy and security of patient data. While the privacy debate is a salient issue, data mining (DM) offers broader community-based gains that enable and improve healthcare forecasting, analyses, and visualization.

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