Concise Data Quality Assessment: Discovery, Measurement, and Visualization

In this era of big data, data quality will be increasingly important because people need high quality data to make decisions, analyze patterns, and discover knowledge. So, measuring data quality is a vital mission. In this book, Chapter 1 is the introduction; Chapter 2 is a literature review; Chapter 3 illustrates how to discover potentially important data based on a reference algorithm, a frequency algorithm, and an entropy algorithm; in Chapter 4, the author offers a concise five-layer data quality framework to measure stream data quality scorecards; in Chapter 5, the author shows how to visualize data quality scorecards through a three-dimensional impact model. In Chapter 6, the author compares performance results between single thread in one machine and parallel threads on a cluster platform. Chapter 7 is the conclusions; at the end of this paper, the author discusses future work.