Gold Mine by Wilbur A. Smith

By Wilbur A. Smith

North of Johannesburg, 5 businesses percentage the Kitchenerville fields, furiously blasting and digging deep into the earth. For a few males, gold mining is a fashion of life—and dying. For a few it's only a company. For one guy, it's the main risky online game of all...

Rod Ironsides makes like to his boss's spouse and drives his miners swifter and tougher than an individual has ever dared. a couple of weeks in the past, Ironsides had a fondness for golfing and one-night stands. Now, he has been passed a prize and a curse: to blast via rock and attain a fortune—or be destroyed.

From a split-second, oxygen-sucking explosion to a looming underground wall of water, there are a dozen methods Rod can fail. yet his ardour and fury won't enable him go into reverse from a conspiracy he can't see: males who are looking to flip his mine right into a demise trap—for the bloodiest pay-day of all...

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Han, S. Chee, and J. Chiand. Issues for On-Line Analytical Mining of Data Warehouses. In Proceedings of DMKD’98. 28. J. Han, Y. Fu, K. Koperski, W. Wang, and O. Zaiane. DMQL: A Data Mining Query Language for Relational Databases. In Proceedings of DMKD’96. 29. J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-based, multidimensional data mining. Computer, 32(8):46–50, 1999. 30. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of ACM SIGMOD’00.

Wijsen. Logical Languages for Data Mining. In Logics for emerging Applications of Databases. Springer, Berlin, 2003. 25. G. Grahne, L. Lakshmanan, and X. Wang. Efficient mining of constrained correlated sets. In Proceedings of ICDE’00). 26. J. Han. Towards On-Line Analytical Mining in Large Databases. Sigmod Records, 27(1):97–107, 1998. 27. J. Han, S. Chee, and J. Chiand. Issues for On-Line Analytical Mining of Data Warehouses. In Proceedings of DMKD’98. 28. J. Han, Y. Fu, K. Koperski, W. Wang, and O.

Example 14 (Embedding the primitive in a SQL-like language). Consider the constraint-based frequent pattern query of Example 13. The following is some SQL-like syntactic sugar to express such query. MINE PATTERNS Freq_pat, Support FROM sales GROUPING item BY day,cust MINIMUM SUPPORT: 3 CONSTRAINTS ON name FROM product HAVING SUM(price) >= 30, In the first line we define the name of the two output attributes Freq pat and Support corresponding to the variables I and S of the query in Example 13. In the FROM clause we indicate the data source, or, in other words, the relation which plays the role of the mining view (V): note that according to Definition 5 the mining view can be defined by a relational expression; this means that in the FROM clause we can have a full SQL SELECT clause.

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