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Given new classes of business problems that can be solved using this platform, data warehousing workloads will continue to grow more complex. Queries and analyses will be performed against ever-growing data volumes. Data loads will move toward near real-time in response to the need for faster tactical business decisions based on current information. As these loads become trickle feeds, the platform must not only perform traditional data warehousing workloads efficiently, but also exhibit excellence in performance and loading characteristics that can appear more akin to transaction processing workloads. The Oracle Exadata Database Machine is designed to handle workloads that cross this entire spectrum of workload demands. Since the platform is balanced out-of-the box and incrementally grows in a balanced fashion, the CPUs, memory, flash, storage, and interconnect scale as workload demands grow, enabling consistent query response times to be delivered. Automatic Storage Management (ASM) provides a critical role in distributing data in a striped fashion across the disk. The Oracle Exadata Storage Server Software further speeds obtaining results to queries by processing more of the data in storage, where the data lives. As you plan your data warehouse deployment, traditional Oracle and data warehousing best practices do apply to the Oracle Exadata Database Machine. But you will also find this platform to be a lot more forgiving and easier to manage than traditional Oracle database platforms. Of course, the business justification exercise will likely be similar to other data warehousing initiatives, but with new potential areas to explore due to new levels of performance delivered. The end result for you should be faster deployment of a platform that delivers the query response and analytics needed by your business analysts and delivers measurable business value.
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lnspectionof a plot of the function (Fig. 3.2) showsthat this resultis a reasonable estimate of the curve's average heieht.
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Tuning the application(s) Attempting to reduce CPU consumption by tuning Oracle SQL access paths through analysis of data structures and usage patterns by the application(s). This process involves investigation of SQL statements that may be causing excessive CPU usage, as well as selective denormalization, partitioning, compression, index reviews, and other schema adjustments that are typically used to improve access paths. Once these alterations have been made, the execution plan is frozen using a SQL Plan Baseline or a SQL Profile.
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ilarities with cytophagic histiocytic panniculitis and distinct from macrophagic myofasciitis. J Neuropath Exp Neurol 62:464, 2003. BROUWER R, HENSGSTMAN GJD, EGBERTS WV, et al.: Autoantibody pro les in the sera of European patients with myositis. Ann Rheum Dis 60:116, 2001. BUCHBINDER R, HILL CL: Malignancy in patients with in amatory myopathy. Curr Rheumatol Rep 4:415, 2002. COLE AJ, KUZNIESKY R, KARPATI G, et al: Familial myopathy with changes resembling inclusion body myositis and periventricular leukoencephalopathy. Brain 111:1025, 1988. CUMMING WJK, WEISER R, TEOH R, et al: Localized nodular myositis: A clinical and pathological variant of polymyositis. Q J Med 46:531, 1977. DALAKAS MC: Intravenous immune globulin therapy for neurological diseases. Ann Neurol 126:721,1997. DALAKAS MC, HOHLFELD R: Polymyositis and dermatomyositis. Lancet 362:971, 2003. DALAKAS MC, ILLA I, PEZESHKPOUR GH, et al: Mitochondrial myopathy caused by long-term zidovudine therapy. N Engl J Med 322:1098, 1990. DEVERE R, BRADLEY WG: Polymyositis, its presentation, morbidity and mortality. Brain 98:637, 1975. DION E, CHERIN P, PAYAN C, et al: Magnetic resonance imaging criteria for distinguishing between inclusion body myositis and polymyositis. J Rheumatol 29:1897, 2000. DUA HS, SMITH FW, SINGH AK, Forrester JV: Diagnosis of orbital myositis by nuclear magnetic resonance imaging. Br J Ophthalmol 71:54, 1987. EISENBERG I, AVIDAN N, POTIKHA T, et al: The UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase gene is mutated in recessive hereditary inclusion body myopathy. Nat Genet 29:83, 2001. EMSLIE-SMITH AM, MAYENO AN, NAKANO S, et al: 1,1-Ethylidenebis[tryptophan] induces pathologic alterations in muscle similar to those observed in the eosinophilia-myalgia syndrome. Neurology 44:2390, 1994. ENGEL AG, ARAHATA K: Mononuclear cells in myopathies: Quantitation of functionally distinct subsets, recognition of antigen-speci c cell-mediated cytotoxicity in some diseases and implications for the pathogenesis of the different in ammatory myopathies. Hum Pathol 17:704, 1986. ENGEL AG, EMSLIE-SMITH AM: In ammatory myopathies. Curr Opin Neurol Neurosurg 2:695, 1989. ENGEL AG, HOHLFELD R, BANKER BQ: The polymyositis and dermatomyositis syndromes, in Engel AG, Franzini-Armstrong C (eds): Myology, 3rd ed. New York, McGraw-Hill, 2004, pp 1321 1366. GAMBOA ET, EASTWOOD AB, HAYS AP, et al: Isolation of in uenza virus from muscle in myoglobinuric polymyositis. Neurology 29:556, 1979. GARLEPP MJ, MASTAGLIA FL: Inclusion body myositis. J Neurol Neurosurg Psychiatry 60:251, 1996. GHERARDI R, BAUDRIMONT M, LIONNET F, et al: Skeletal muscle toxoplasmosis in patients with acquired immunode ciency syndrome: A clinical and pathological study. Ann Neurol 32:535, 1992. GRIGGS RC, ASKANAS V, DIMAURO S, et al: Inclusion body myositis and myopathies. Ann Neurol 38:705, 1995. GROSS B, OCHOA J: Trichinosis: A clinical report and histochemistry of muscle. Muscle Nerve 2:394, 1979. HART MN, LINTHICUM DS, WALDSCHMIDT MM, et al: Experimental autoimmune in ammatory myopathy. J Neuropathol Exp Neurol 46:511, 1987. HENGSTMAN GJD, VAN ENGELEN BGM, VAN EGBERTS WTMV, et al: Myositis-speci c autoantibodies: overview and recent developments. Curr Op Neurol 13:476, 2001. KAKULAS BA, ADAMS RD: Diseases of Muscle: Pathological Foundations of Clinical Myology, 4th ed. Philadelphia, Harper & Row, 1985. KASS EH, ANDRUS SB, ADAMS RD, et al: Toxoplasmosis in the human adult. Arch Intern Med 89:759, 1952. KISSEL JT, MENDELL JR, RAMMOHEN KW: Microvascular deposition of complement membrane attack complex in dermatomyositis. N Engl J Med 314:329, 1986.
Figure 31-21. Paracarcinomatous cerebellar degeneration. MRI showing subtle diffuse enhancement of the cerebellar cortex.
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2 .47 B6 2.3593 1.8607 In order to compute 12 and s"7-, first compute the sum of the squares of the residuals: , > > S r = s u m( ( y - Z * a ) . ^ 2 )
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