scholarly journals FluSpider as a new vision of digital influenza surveillance system: based on Big Data technologies and Massive Data Mining techniques

2020 ◽  
Author(s):  
Kaouther Nouira ◽  
Nesrine Ben Njima
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Surveillance System ◽  
Influenza Surveillance ◽  
Massive Data ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
New Vision

Data Mining Problems Classification and Techniques

2020 ◽  
pp. 70-93
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Data Mining Problems Classification and Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 246-264
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

A Review of Different Data Mining Techniques Used in Big Data Applications

2021 ◽  
pp. 59-89
Author(s):  
Chandrakanta Mahanty ◽  
Devpriya Panda ◽  
Brojo Kishore Mishra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Mining Techniques ◽  
Big Data Applications

Big Data: Technologies, Challenges, Data Analytics and Management: A Review

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Shweta Kaushik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Information Base ◽  
Information Order ◽  
The World ◽  
Big Data Technologies ◽  
Business Profitability ◽  
The Web

Internet assumes an essential part in giving different learning sources to the world, which encourages numerous applications to give quality support of the customers. As the years go on the web is over-burden with parcel of data and it turns out to be difficult to extricate the applicable data from the web. This offers path to the advancement of the Big Data and the volume of the information continues expanding quickly step by step. Enormous Data has increased much consideration from the scholarly world and the IT business. In the advanced and figuring world, data is produced and gathered at a rate that quickly surpasses the limit go. Data mining procedures are utilized to locate the concealed data from the huge information. This Technique is utilized store, oversee, and investigate high speed of information and this information can be in any shape organized or unstructured frame. It is hard to handle substantial volume of information utilizing information base strategy like RDBMS. From one perspective, Big Data is amazingly important to deliver efficiency in organizations and transformative achievements in logical controls, which give us a considerable measure of chances to make incredible advances in many fields. There is most likely the future rivalries in business profitability and advances will without a doubt merge into the Big Data investigations. Then again, Big Data likewise emerges with many difficulties, for example, troubles in information catch, information stockpiling, information investigation and information perception. In this paper we concentrate on the audit of Big Data, its information order techniques and the way it can be mined utilizing different mining strategies.

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