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OLAP applications all require (1) multi-...View Answer
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OLAP applications all require (1) multi-...
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Students should identify the factors such as:
Adoption of data mining tools is nature path following on from the success of data warehousing and OLAP.
Data mining tools are more intuitive to use and there is less need for the specialised staff.
Data mining tools are often bundled together with other BI tools.
DBMS vendors are offering data mining functionality as an inexpensive add-on.
Growing number of data mining tools on the marketplace so there is more user choice.
Growing number of data mining tools are appearing to meet needs of a specific domain.
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Students should identify the factors such as:
Adoption of data mining tools is nature path following on from the success of data warehousing and OLAP.
Data mining tools are more intuitive to use and there is less need for the specialised staff.
Data mining tools are often bundled together with other BI tools.
DBMS vendors are offering data mining functionality as an inexpensive add-on.
Growing number of data mining tools on the marketplace so there is more user choice.
Growing number of data mining tools are appearing to meet needs of a specific domain.
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Common OLAP operations include:
(1) Roll...View Answer
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Common OLAP operations include:
(1) Roll...
(1) Roll...
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See introductory par...View Answer
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See introductory par...
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The student should discuss key features of these tools such as:
data preparation facilities;
selection of data mining operations (algorithms);
product scalability and performance;
facilities for visualization of results.
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The student should discuss key features of these tools such as:
data preparation facilities;
selection of data mining operations (algorithms);
product scalability and performance;
facilities for visualization of results.
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Data mining and OLAP are complementary B...View Answer
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Data mining and OLAP are complementary B...
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A data warehouse is well equipped for pr...View Answer
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A data warehouse is well equipped for pr...
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OLAP operations include:
Roll-up, drill-...View Answer
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OLAP operations include:
Roll-up, drill-...
Roll-up, drill-...
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The ROLLUP and CUBE extensions to GROUP BY generate OLAP-type summaries of the data with subtotals and totals. The columns to be defined are defined similarly to how grouping sets can define GROUP BY columns. ROLLUP generates subtotal and total rows for the GROUP BY columns. CUBE extends the capabilities by generated subtotal rows for every combination of GROUP BY columns. ROLLUP and CUBE also generate a grand total row.
A simple example of a ROLLUP query is shown below:
SELECT a, b, c, sum(x)
FROM t
GROUP BY ROLLUP (a,b,c)
Produces aggregates of x for (a,b,c), (a,b), a, (grand total)
A simple example of a CUBE query is shown below:
SELECT a, b, c, sum(x)
FROM t
GROUP BY CUBE (a,b,c)
Produces aggregates of x for (a,b,c), (a,b), (a,c), a, (b,c), b, c, (grand total). The rows that are produced by ROLLUP and CUBE are shown in bold.
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verified
The ROLLUP and CUBE extensions to GROUP BY generate OLAP-type summaries of the data with subtotals and totals. The columns to be defined are defined similarly to how grouping sets can define GROUP BY columns. ROLLUP generates subtotal and total rows for the GROUP BY columns. CUBE extends the capabilities by generated subtotal rows for every combination of GROUP BY columns. ROLLUP and CUBE also generate a grand total row.
A simple example of a ROLLUP query is shown below:
SELECT a, b, c, sum(x)
FROM t
GROUP BY ROLLUP (a,b,c)
Produces aggregates of x for (a,b,c), (a,b), a, (grand total)
A simple example of a CUBE query is shown below:
SELECT a, b, c, sum(x)
FROM t
GROUP BY CUBE (a,b,c)
Produces aggregates of x for (a,b,c), (a,b), (a,c), a, (b,c), b, c, (grand total). The rows that are produced by ROLLUP and CUBE are shown in bold.
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The extensions to SQL are collectively r...View Answer
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The extensions to SQL are collectively r...
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