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.
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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. The type of data that is stored and processed by the system. It also gives the implication to its dynamics and organizations. The data can be of the following types:
Sl. No Parts Function 1 Devices The component that is used for storing and processing the data. Eg: Personal Computers, Super Computers 2 Communication The mode in which devices communicate with each other. Eg: Internet, via Centralized System 3 Users The user who would interact with the system. It can have a Single User or can have Multiple Users. 4 Control The component that administers all the above-mentioned parts. It can either be controlled by a single administrator or by multiple administrators. 5 Data 6 Infrastructure The infrastructure that the system employs for data discovery. Eg: Web, Database
The type of data that is stored and processed by the system. It also gives the implication to its dynamics and organizations. The data can be of the following types:
Application of UDM:
- 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.
- 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.
- 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
- Search Engines
- Intelligent Database System
- e-mail managers