Across a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational theories and tools to assist humans in（1） useful information (knowledge) from the rapidly growing（2）of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in database (KDD) . At an abstract level, the KDD field is concerned with the development of methods and techniques for making （3）of data. The basic problem addressed by the KDD process is one of mapping low-level data (witch are typically too voluminous to understand and digest easily) into other form that might be more （4）(for example, a short report) , more （5）(for example, a descriptive approximation or model of the process that generated the data) , or more useful (for example, a predictive model for estimating the value of future cases) , At the core of the process is the application of specific data-mining methods for pattern discovery and extraction.
（1）A、expiring 
B、examining 
C、extracting 
D、extricating
（2）A、voice 
B、volumes 
C、ststes 
D、speed
（3）A、reason 
B、sense 
C、specification 
D、summary
（4）A、dense 
B、compact 
C、extensile 
D、short
（5）A、theoretical 
B、meaningful 
C、sophisticated 
D、abstract