Applied Drilling Engineering by Jr. Adam T. Bourgoyne, Keith K. Millheim, Martin E.

By Jr. Adam T. Bourgoyne, Keith K. Millheim, Martin E. Chenevert, Jr. F. S. Young

An and educational usual, utilized Drilling Engineering offers engineering technology basics in addition to examples of engineering purposes regarding these basics. appendices are incorporated, in addition to a variety of examples. solutions are integrated for each end-of-chapter query. Contents: Rotary drilling - Drilling fluids - Cements - Drilling hydraulics - Rotary drilling bits - Formation pore strain and fracture resistance - Casing layout - Directional drilling and deviation regulate - Appendix: improvement of equations for non-Newtonian drinks in a rotational viscometer - Appendix: improvement of slot movement approximations for annular stream for non-Newtonian fluids.

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2000) . Intrusion detection using autonomous agents. Computer N etworks, 34(4) :547-570. , Crawford, R. , and Zerkle, D. (1996). GrIDS-A Graph Based Intrusion Detection System for Large Networks. In 19th National Information Systems Security Conference, pages 361-370, Baltimore, MD. NIST and NSA. Vaccaro, H. and Liepins, G. (1989). Detection of anomalous computer session activity. In IEEE Symposium on Security and Privacy. IEEE Computer Society. Valdes, A. and Skinner, K. (2000). Adaptive, model-based monitoring for cyber attack detection.

3 concludes the discussion by summarizing several open research challenges in the field of data mining. 1 Data Mining, KDD, and Related Fields The term data mining is frequently used to designate the process of extracting useful information from large databases. In this chapter, we adopt a slightly different view, which is identical to the one expressed by Fayyad et al. (1996b, Chapter 1) 1 . In this view, the term knowledge discovery in databases (KDD) is used to denote the process of extracting useful knowledge from large data sets.

The data mining literature contains several variants of frequent episode rules (Mannila et al. , 1997; Lee et al. , 1998). e. at approximately the same time). 2) where P , Q, and Rare predicates over a user-defined dass of admissible predicates (Hätönen et al. , 1996) . Intuitively, this rule says that two records that satisfy P and Q, respectively, are generally accompanied by a third record that satisfies R . The parameters s, c, and ware called support, confidence, and window width, and their interpretation in this context is as follows: The support s is the probability that a time window of w seconds contains three records p, q, and r that satisfy P, Q, and R, respectively.

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