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The Line plot draws lines between the points that represent the chart items inside the plot area. Those lines can be suppressed if the isShowLines attribute is set to false.

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When we run this through autotrace (and 9i and 8i should produce the same cardinality, though the costs may differ), we get the following: Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=60 Card=50000 Bytes=1700000) 1 0 HASH JOIN (Cost=60 Card=50000 Bytes=1700000) 2 1 TABLE ACCESS (FULL) OF 'T1' (Cost=28 Card=10000 Bytes=170000) 3 1 TABLE ACCESS (FULL) OF 'T2' (Cost=28 Card=10000 Bytes=170000) The cardinalities on t1 and t2 are clearly correct; we haven t filtered out any rows on the tablescans. Consequently, our join cardinality of 50,000 must come from 100,000,000 (i.e., 10,000 * 10,000) times a join selectivity of 1/2,000 but where in our data definitions can we find that magic 2,000

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We ve covered all the preparatory material for the upload feature of our Friends Reunion application. In the next chapter, we ll take advantage of it by building an XML document that adheres to our XML Schema and writing the feature that uses the XML Schema to validate, interpret, and understand the uploaded XML.

If we think about applying the selectivity formula twice, we ll soon spot it. The critical component in our case is the bit that goes greater(num_distinct(t1.c1), num_distinct(t2.c2)) We have two join components, t1.join1 = t2.join1 and t1.join2 = t2.join2. In 3 you learned that you combine selectivities on single tables by multiplying them together (at least, that s what you do when the individual predicates have an and between them). You do exactly the same with the join selectivities: Join Selectivity = {join1 bit} * {join2 bit} = (10,000 - 0) / 10,000) * (10,000 - 0) / 10,000) / greater(30, 40) * (10,000 - 0) / 10,000) * (10,000 - 0) / 10,000) / greater(50, 40) = 1/40 * 1/50 = 1/2000

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he XML Schema that we created in 7 is a document that describes a certain data structure Specifically, it describes the XML data structure that a third party must use when it uploads an XML document (containing details of multiple attendees of a college or other institution) to the Friends Reunion web site The best way to think of an XML Schema is as a set of rules for the data structure We (as developers of the Friends Reunion application) will use our schema, our set of rules, when we write the code that interprets an uploaded XML document and processes it (As you ll see, we ll write code that assumes that any uploaded document contains only the tags and attributes that are allowed by the schema) When a user creates a data document containing attendee details, that document should use the same set of rules.

-- uses the t2 selectivity (1/40)

The Line plot also marks each item point with a small graphical shape that is different for each series in the underlying dataset. This small shape can be hidden by setting the isShowShapes flag to false.

Provided we all adhere to the same rules, as defined in the schema, each uploaded document will be compatible with the application, so the application should be able to process it without any trouble, regardless of the tool used to generate it The preparatory work that we did in 7 to create the schema document, Friendsxsd, puts us in good shape to complete the XML upload feature In this chapter, we ll make use of it as we build the feature, and we ll continue our studies of XML in the process In this chapter, we will perform the following tasks: Use the VS NET IDE to create a sample XML document that validates against the XSD Build a feature that allows a user to upload an XML file onto the web server, via a browser interface.

-- uses the t1 selectivity (1/50)

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