Ubiquitous and Invisible Data Mining

Data Analytics is one of the most emerging technologies in the present-day world. With the increase in the demand for portable and remote devices like mobile phones and personal digital assistants (PDAs), the need for extracting data from these devices for analysis plays a crucial role in order to perform data analysis. Therefore, accessing data from a remote device is much needed.  

Ubiquitous Data Mining (UDM) is a process of analyzing data performing concrete mining and examination of distributed and heterogeneous systems like mobile and embedded devices. 

UDM is used for mining data from mobile environments like cell phones and sensors, which are constrained by limited computational resources and varying networks. It supports time-critical and real-time data needs. It is also used for intelligent analysis. Using UDM we can extract hidden classifiers and clusters. 

The architecture of Ubiquitous technologies

Ubiquitous Data Mining involves the collection and storage of data, processing data, and dissemination of the result that is analyzed. In order to achieve this, we make use of 6 parts, which make up the architecture of the ubiquitous system.

Application of UDM:

1. Traffic Safety: Abnormal traffic can be detected using sensors and the data can be stored and analyzed in the system. This analyzed data can then be used to detect traffic mishaps on a real-time basis using sensors. Thus, traffic and road safety is monitored.
2. Health Care: The sensors can be used in creating smart homes for elderly persons and people who are in need of continuous medical attention. The sensors can be used for notifying the immediate medical need and would be collected. The data so collected can help to provide them with timely medical aids.
3. Crisis and Calamities Management: The previous crisis and calamities data are collected and stored and analyzed. During times of crisis, sensors can detect the crisis and the data is sent to the controllers. Results are predicted before the effect can get disastrous. Thus, helps in Crisis Management.

Invisible Data Mining

Data mining is present in all the major aspects of our life. This requires effective mining of data mining without disclosing private information via extraction of data to outsiders.

Invisible Data Mining is a process of data mining where the functionalities are performed invisibly. 

Applications of Invisible Data Mining

1. Search Engines
2. Intelligent Database System
3. e-mail managers

Ubiquitous Data Mining:

Advantages:

Improved decision-making: Ubiquitous data mining can provide valuable insights into customer behavior, market trends, and other important factors, enabling organizations to make more informed decisions.

Personalized services: Ubiquitous data mining can enable personalized services, such as customized product recommendations or personalized healthcare treatments, that can improve the overall customer experience.

Improved efficiency: Ubiquitous data mining can help streamline business processes, enabling organizations to operate more efficiently.

Disadvantages:

Privacy concerns: Ubiquitous data mining can raise privacy concerns, as it involves collecting and analyzing data about individuals without their explicit consent. This can result in the disclosure of sensitive information, which can have negative consequences for individuals.

Security risks: The data used in ubiquitous data mining can be subject to security risks, such as unauthorized access or hacking, which can result in the exposure of sensitive information.

Bias: Ubiquitous data mining algorithms can be biased, which can lead to discriminatory outcomes or reinforce existing biases.

Invisible Data Mining:

Advantages:

Less intrusive: Invisible data mining can be less intrusive than other forms of data mining, as individuals may be less aware that their data is being collected and analyzed.

Improved decision-making: Invisible data mining can provide valuable insights into customer behavior, market trends, and other important factors, enabling organizations to make more informed decisions.

Personalized services: Invisible data mining can enable personalized services, such as customized product recommendations or personalized healthcare treatments, that can improve the overall customer experience.

Disadvantages:

Lack of transparency: Invisible data mining can lack transparency, as individuals may be unaware that their data is being collected and analyzed.

Privacy concerns: Invisible data mining can raise privacy concerns, as it involves collecting and analyzing data about individuals without their explicit consent.

Security risks: The data used in invisible data mining can be subject to security risks, such as unauthorized access or hacking, which can result in the exposure of sensitive information.