Resumen
Data mining is increasingly recognized as a key to analyzing, digesting and understanding the flood of digital data collected by business, government and scientific applications. Achieving this goal requires the scaling of mining algorithms to very large databases. Many classic mining algorithms require multiple database scans and random access to database records. Research as of August 1, 2002 focuses on overcoming limitations imposed when it is costly or impossible to scan large databases multiple times or access records at random. Predictive modeling is often a high-level goal of data mining in practice. There are two classes of algorithms namely decision tree methods and support vector machines. Decision trees are especially attractive in data mining environments since human analysts readily comprehend the resulting models. Their construction does not require an analyst to provide input parameters. Prior knowledge about the data is also not needed. Decision tree construction algorithms consist of two stages namely tree building and pruning. Support vector machines are other powerful and popular approaches to predictive modeling with success in a number of applications, including handwritten digit recognition, charmed quark detection, face detection and text categorization.

Resumen
A group of database researchers, architects, users, and pundits met in May 2008 at the Claremont Resort in Berkeley, CA, to discuss the state of database it search and its effects on practice. This was the seventh meeting of this sort over the past 20 years and was distinguished by a broad consensus that the database community is at a turning point in its history, due to both an explosion of data and usage scenarios and major shifts in computing hardware and platforms. This article explores the conclusions of this self-assessment. The theme of the Claremont meeting was that database research and the data-management industry are at a turning point, with unusually rich opportunities for technical advances, intellectual achievement, entrepreneurship, and benefits for science and society. Given the large number of opportunities, it is important for the database research community to address issues that maximize relevance within the field, across computing, and in external fields as well.