Mining Domain Knowledge: Using Functional Dependencies to Profile Data

Poor data quality is one of the primary issues facing big data projects. Cleaning data and improving quality can be expensive and time-intensive. In data warehouse projects, data cleaning is estimated to account for 30% to 80% of the project\u27s development time and budget. Data quality mining is one method used to identify errors that has become increasingly popular in the past 20 years. Our research-in-progress aims to identify multi-field errors via the mining of functional dependencies. Existing research on data quality mining and functional dependencies has focused on improving algorithms to identify a higher percentage of complex errors. The proposed process strives to introduce an efficient method for expediting error identification and increasing a user\u27s domain knowledge in order to reduce the costs associated with cleaning; the process will also include an assessment of when further cleaning is unlikely to be cost effective

Design of Autonomous Cleaning Robot

Today, the research is concentrated on designing and developing robots to address the challenges of human life in their everyday activities. The cleaning robots are the class of service robots whose demands are increasing exponentially. Nevertheless, the application of cleaning robots is confined to smaller areas such as homes. Not much autonomous cleaning products are commercialized for big areas such as schools, hospitals, malls, etc. In this thesis, the proof of concept is designed for the autonomous floor-cleaning robot and autonomous board-cleaning robot for schools. A thorough background study is conducted on domestic service robots to understand the technologies involved in these robots. The components of the vacuum cleaner are assembled on a commercial robotic platform. The principles of vacuum cleaning technology and airflow equations are employed for the component selection of the vacuum cleaner. As the autonomous board-cleaning robot acts against gravity, a magnetic adhesion is used to adhere the robot to the classroom board. This system uses a belt drive mechanism to manoeurve. The use of belt drive increases the area of magnetic attraction while the robot is in motion. A semi-systematic approach using patterned path planning techniques for the complete coverage of the working environment is discussed in this thesis. The outcome of this thesis depicts a new and conceptual mechanical design of an autonomous floor-cleaning robot and an autonomous board-cleaning robot. This evidence creates a preliminary design for proof-of-concept for these robots. This proof of concept design is developed from the basic equations of vacuum cleaning technology, airflow and magnetic adhesion. A general overview is discussed for collaborating the two robots. This research provides an extensive initial step to illustrate the development of an autonomous cleaning robot and further validates with quantitative data discussed in the thesis