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In order for everything in your Smart Home to work together, there has to be some sort of standard. If everything was left up to the whims of manufacturers, products simply couldn t work together. It s a lot like trying to put the air filter for a 1988 Ford Mustang into a 1997 Jeep Wrangler because the manufacturers have
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What is pregestational diabetes mellitus (GDM) and how is it characterized It is a diagnosis of diabetes mellitus prior to pregnancy. It occurs in 1% of all pregnancies and includes both Type I diabetes and Type II diabetes. Type II pregestational diabetes is more common and is characterized by onset later in life, obesity, peripheral insulin resistance, some insulin deficiency, and end-organ complications (renal, vascular, nervous). Type I pregestational diabetes is less common and occurs most often early in life. It is characterized by an autoimmune process that destroys the pancreatic cells It is a diagnosis of diabetes mellitus defined as glucose intolerance with onset or first recognition during pregnancy. 90% of diabetes cases encountered during pregnancy are GDM and more than one half of those patients at risk will end up developing pregestational diabetes later in life Placental secretion of anti-insulin and diabetogenic hormones that contribute to the diabetic state include: Growth hormone Corticotropin-releasing hormone Human placental lactogen Prolactin Progesterone
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Contexts allow a universe to contain multiple dimensional schemas and allow users to query these multiple schemas simultaneously. In addition, contexts prevent another possible join problem from occurring when you may have a common dimension table and two fact tables. Refer back to Figure 8-3. A loop exists when the dimension tables ARTICLE_ LOOKUP and CALENDAR_YEAR_LOOKUP are both referenced via either fact table. However, a loop is not present if CALENDAR_YEAR_LOOKUP is not part of the universe. The universe would pass an integrity check without its being in the universe; however, your users may get incorrect results if their query contains measures from both fact tables. Two things will go wrong. First, promotion costs will be significantly overstated as the one-to-many join between ARTICLE_LOOK_UP and SHOP_FACTS essentially results in a many-to-many join and Cartesian product between SHOP_FACTS and PRODUCT_PROMOTION_FACTS. If you care to test this to understand what is going on, note that there are 529 unique rows in the SHOP_ FACTS table for the ARTICLE_ID=145404. When the join is processed via the PRODUCT_ PROMOTION_FACT table, the one row in this fact table is repeated 529 times. Second, just as a loop will cause rows to be dropped from a query, so will accessing two fact tables via a common dimension table. Thus users will not get all sales for all products; instead, they will get only sales in which a corresponding ARTICLE_ID exists in the PRODUCT_PROMOTION_FACT table. The vendor refers to this as a chasm trap, as results appear to disappear into a chasm. Additionally, if there are multiple rows in the sales table, rows from the promotion table are overstated. Figure 8-5 shows how a chasm trap causes you to lose rows, as well as overstates rows. The table on the left shows the chasm trap. The two right-hand tables show the correct results when these individual facts are used in separate queries. At first glance, the results in the left-hand table seem to answer a valid
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Reference performance objective Unacceptable >10 DEGRADED ________________ UNACCEPTABLE Unacceptable >10 DEGRADED __________ ______ UNACCEPTABLE
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