As computers become faster and main memory becomes cheaper, it becomes increasingly feasible to create visual presentations of data, rather than just text-based reports.
Data visualization makes it easier for users to understand the information in large complex datasets. The challenge here is to make it easy for users to develop visual presentation of their data and to interactively query such presentations. Although a number of data visualization tools are available, ecient visualization of large datasets presents many challenges.
The need for visualization is especially important in the context of decision support; when confronted with large quantities of high-dimensional data and various kinds of data summaries produced by using analysis tools such as SQL, OLAP, and data mining algorithms, the
Visualizing the data, together with the generated summaries, can be a powerful way to sift through this information and spot interesting trends or patterns. The human eye, after all, is very good at finding patterns. A good framework for data mining must combine analytic tools to process data, and
anomalies or trends, with a visualization environment in which a user can notice these patterns and interactively drill down to the original data for further analysis.
The database area continues to grow vigorously, both in terms of technology and in terms of applications. The fundamental reason for this growth is that the amount of information stored and processed using computers is growing rapidly. Regardless of the nature of the data and its intended applications, users need database management systems and their services (concurrent access, crash recovery, easy and efficient querying, etc.) as the volume of data increases. As the range of applications is broadened, however, some shortcomings of current DBMSs become serious limitations