Using Dashboards and the User Portal
Using the Pivot Analysis Window
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This chapter describes how to use the Pivot Analysis window, which you can access via the Analysis button
on pivot table widgets. This chapter discusses the following:
The system provides an analysis window that you can use for several specialized kinds of analysis. In each case, you first select one or more cells, and the analysis considers the lowest-level data associated with those cells. To access this window:
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Select the data cells in the row or rows.
To select multiple cells, hold the
Shift key down while selecting the cells.
To select an entire row, select the row label on the left. To select an entire column, select the column header.
The analysis option is not available for cells in a total row or a total column.
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Select the Analysis button
.
Depending on how the widget is configured, the system either provides a choice of analysis options or displays one of them without any choice.
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The
iKnow Plug-ins options are applicable only if your cube includes NLP measures.
The following sections provide the details.
Note:
This window is also available in the Analyzer.
Cluster analysis or
clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and is a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics.
For a cluster analysis, the top area of the page looks something like the following:
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Specify details of the analysis in the top area of the page.
Details are beyond the scope of this book; it is assumed that the reader is familiar with cluster analysis.
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The bottom area of the page displays the results. For example:
A
distribution analysis shows the number of occurrences of different values of specific measurement, across a set of records.
For a distribution analysis, the system displays something like the following:
To use this page, select a measure (such as
Age in this example).
The horizontal axis then shows all the values of this measure for the selected cells. For each measure value, the vertical axis shows the number of source records (for the selected cells) that have that value.
The top of the page shows the following values:
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Items — Number of source records for the selected cells of the pivot table.
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Missing — Number of source records (for the selected cells) that do not have a value for this measure.
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Min value — Lowest value for this measure among these source records.
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Max value — Highest value for this measure among these source records.
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Avg — Average of these values.
A
regression analysis attempts to determine the relationship between independent variables and a dependent variable. (The InterSystems IRIS Business Intelligence regression analysis considers only one independent variable.)
For a regression analysis, the system displays something like the following:
To use this page, specify the following details:
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Input (X) Measure — Select the measure that you want to treat as independent (such as
Age in this example). These values are shown on the horizontal axis.
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Response (Y) Measure — Select the measure that you believe is dependent on the first measure (such as
Encounter Count in this example). These values are shown on the vertical axis.
When you do so, the page displays a data point for each lowest-level record associated with the selected cells.
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Fit — Select the type of fitting to perform:
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line — Performs a linear regression.
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exp — Performs an exponential regression.
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log — Performs a logarithmic regression.
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power — Performs a power regression.
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Confidence Level — Optionally select the confidence level to use when calculating error bars for the values being determined.
In each case, the system determines the details of the equation that predicts Y as a function of X, and it plots the predicted X-Y curve on the page. The area above the chart displays details of the fit. For example, for a linear regression:
For another example, the page displays the following details for an exponential regression:
The
iKnow content analysis option displays information about the most typical and least typical unstructured text values. This analysis option is applicable only if your cube includes NLP measures.
For the content analysis, the system displays something like the following:
Each table lists the records and displays the fields listed in the selected detail listing.
On this page, you can do the following:
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See the entire unstructured text for a given record. To do so, select the magnifying glass icon shown by that record.
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Select a different NLP measure to analyze. To do so, select a choice for
Measure.
An NLP measure is different from other kinds of measures and cannot be used in the same way as other measures. It is based on a source value that contains unstructured text such as a free-form text report. When you select an NLP measure to analyze here, you are examining results of the Analytics Engine for those source values.
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Select a different detail listing to use. To do so, select a choice for
Display Listing.
The system initially displays the listing named
ShortListing (if that is defined) or the default listing for the subject area.
This example uses the Aviation Events demo in the
SAMPLES namespace. For reasons of space, this demo is not initialized when you install InterSystems IRIS™. To set up the demo, open the Terminal and enter the following commands:
zn "SAMPLES"
do ##class(Aviation.Utils).Setup()
The
iKnow entity analysis option displays information about the entities in your unstructured text values. This analysis option is applicable only if your cube includes NLP measures.
For the entity analysis, the system displays something like the following:
This page provides three tabs, which display information about the records associated with the pivot table cells that you selected before you selected the Analysis button
. These tabs work together as follows:
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The
Overview tab displays information for the 20 most common entities among those records. You use the
Select by option to control how the system selects these entities; see the
first subsection.
Select a rectangle in this chart to see further detail; see the next bullet item.
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Select a bar in this chart to see further detail; see the next bullet item.
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This example uses the Aviation Events cube demo in the
SAMPLES namespace. For reasons of space, this demo is not initialized when you install InterSystems IRIS. For information on setting it up, see the
previous topic.
The
Overview tab displays information about the top entities among the records associated with the pivot table cells that you selected before you selected the Analysis button
. The chart on this tab displays one rectangle for each of the top 20 entities, according to your choice of metric.
Selecting the Top Entities
To determine how the system selects the top entities, select a metric from the
Select by drop-down list. The options are as follows:
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spread, the default, lists the top entities by their spread (total number of facts in which the entity appears).
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BM25 lists the top entities using a frequency calculation algorithm in descending order by calculated BM25 score. It calculates this score using an algorithm based on the Okapi BM25 standard, which combines an entity's Term Frequency with its Inverse Document Frequency (IDF), taking into account document length.
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TFIDF lists the top entities using a frequency calculation algorithm in descending order by calculated TFIDF score. It calculates this score by combining an entity’s Term Frequency (TF) with its Inverse Document Frequency (IDF). The Term Frequency counts how often the entity appears in a single source. The Inverse Document Frequency counts the number of times the entity appears in the collection of sources, and uses this overall frequency to diminish the Term Frequency. Thus an entity that appears multiple times in a small percentage of the sources is given a high TFIDF score; an entity that appears multiple times in a large percentage of the sources is given a low TFIDF score.
Note:
The
BM25 and
TFIDF options are computation-intensive and can take some time to complete.
The colors in this chart indicate how well each entity serves as an indicator for a given pivot table cell, based on the Naive Bayes probability for that entity, as follows:
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If only one cell was originally selected in the pivot table, the colors indicate how well each entity serves as an indicator for that pivot table cell, compared to
all other records.
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If multiple cells were originally selected in the pivot table, the chart displays the
Color-code by drop-down list, which lists the pivot table cells by name and includes the
all option.
To use a different pivot table cell as the basis of comparison, select an option from the
Color-code by drop-down list. For example:
The
total option considers all the originally selected cells as a group.
Green generally denotes good indicators, and red denotes bad indicators. Solid green means that the term is a very good indicator, pale green means that the term is a good indicator, pale red means that it is a bad indicator, and solid red means that it is a very bad indicator. Note that in some cases, an entity is a bad indicator because it is common to all sources and thus does not enable you to discriminate categories of sources.
For example, suppose that we use a pivot table that displays aircraft type as rows. As the starting point for analysis, we select the pivot table cells
Airplane and
Helicopter. If we use
Airplane to color-code, this chart looks as follows:
Notice that the entities
airplane and
runway are good indicators for the pivot table cell
Airplane. The other entities (especially
ground) are bad indicators.
In contrast, if we use
Helicopter to color-code, the chart would be colored as follows:
This shows that the entities
airplane and
runway are bad indicators for the pivot table cell
Helicopter. The other entities (especially
ground) are good indicators.
On the
Overview tab, you can also do the following:
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Select a different NLP measure to analyze. To do so, select a choice for
Measure.
An NLP measure is different from other kinds of measures and cannot be used in the same way as other measures. It is based on a source value that contains unstructured text such as a free-form text report. When you select an NLP measure to analyze here, you are examining results of the Analytics Engine for those source values.
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Specify an additional string to analyze. To do so, type a string into
Analyze string and then press
analyze. The system then includes additional information for that string.
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Select a rectangle in this chart to display a breakdown for the corresponding entity. See the
next section.
The
Cell breakdown tab is useful only if you started by selecting multiple cells of a pivot table.
This tab shows how an entity is distributed among the pivot table cells from which you started. When you select a rectangle in the
Overview tab, the system displays the
Cell breakdown tab with information for that entity.
For the given entity, this tab shows how that entity is distributed among the pivot table cells that you selected before you selected the Analysis button. For each cell, the chart shows five color-coded bars, which display the following series:
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entities — indicates the count of distinct, similar entities in records associated with the given pivot table cell or cells. This count includes all entities that are similar to the given entity.
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entity freq — indicates the
entity frequency; that is, the number of times the given entity occurred in records associated with the given pivot table cell or cells.
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entity spread — indicates the
entity spread; that is, the total number of records in which the given entity appears, for the given pivot table cell or cells.
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cell probability — indicates how well the given entity serves as an indicator for the selected pivot table cell or cells, based on the Naive Bayes probability for that entity.
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Important:
These series are scaled separately so the chart can show them all. The scale shown on the chart applies only to the
entities series. To see the actual numeric value for any bar, hover the cursor over the bar. For example:
On this tab, you can do the following:
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Select a different NLP measure to analyze. To do so, select a choice for
Measure.
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Specify an different string to analyze. To do so, type a string into
Analyze string and then press
analyze.
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Select a bar in this chart to display related entities. See the
next section.
The
Entities tab displays related entities in the given pivot table cell or cells. When you select a bar in the
Cell breakdown tab, the system displays the
Entities tab with the associated details.
On this tab, you can do the following:
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Select a different NLP measure to analyze. To do so, select a choice for
Measure.
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Specify a different string to analyze. To do so, type a string into
Analyze string and then press
analyze. The system then shows information for that string.
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Page through the results. To do so, use the
<< and
>> buttons.
Content Date/Time: 2019-02-18 22:34:10
