Text database cleaning by filling the Missing values using Object Oriented Intelligent Multi - Agent System Data Cleaning Architecture

Agents are software programs that perform tasks on behalf of others and they are used to clean the text database with their characteristics. Agents are task oriented with the ability to learn by themselves and they react to the situation. Learning characteristics of an agent is done by verifying its previous experience from its knowledgebase. An agent concept is a complementary approach to the Object Oriented paradigm with respect to the design and implementation of the autonomous entities driven by beliefs, goals and plans. Text database cleaning process detects and cleans the wrong data or duplicates data or missing data by identifying the outliers. Cleaning of Text Databases focuses on incomplete data cleaning. Incomplete data cleaning is performed using the attribute missing rate. Agents incorporated in the architectural design of a Text database cleaning process combines both the features of Multi-Agent System (MAS) Framework and MAS with learning (MAS-L) Framework. MAS framework reduces the development time and the complexity of implementing the software agents. MAS-L framework incorporates the intelligence and learning properties of agents present in the system. MAS-L Framework makes use of the Decision Tree learning and an evaluation function to decide the next best decision that applies to the machine learning technique. This paper proposes the design for Multi-Agent based Data Cleaning Architecture that incorporates the structural design of agents into object model. The Design of an architectural model for Multi-Agent based Data Cleaning inherits the features of the Multi-Agent System (MAS) and uses the MAS-L framework to design the intelligence and learning characteristics. Keywords— Text database, Incomplete data, Agents, MAS, MASL, Architecture, Data Cleaning