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Levi Watson
Levi Watson

8.7 10 View Details

This functionality is in technical preview and may be changed or removed in a future release. Elastic will apply best effort to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

8.7 10 View Details

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Higher taxonomy data have been previously used to quantify species richness within specific areas by relating the number of species to the number of genera or families at well-sampled locations, and then using the resulting regression model to estimate the number of species at other locations for which the number of families or genera are better known than species richness (reviewed by Gaston & Williams [24]). This method, however, relies on extrapolation of patterns from relatively small areas to estimate the number of species in other locations (i.e., alpha diversity). Matching the spatial scale of this method to quantify the Earth's total number of species would require knowing the richness of replicated planets; not an option as far as we know, although May's aliens may disagree. Here we analyze higher taxonomic data using a different approach by assessing patterns across all taxonomic levels of major taxonomic groups. The existence of predictable patterns in the higher taxonomic classification of species allows prediction of the total number of species within taxonomic groups and may help to better constrain our estimates of global species richness.

The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: CM DPT BW. Analyzed the data: CM DPT. Wrote the paper: CM DPT SA AGBS BW. Reviewed higher taxonomy: CM SA AGBS.

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After an error occurs, you can use SSH to log on to the appliance operating system. View the details in the imsTrace.log file in the /opt/rsa/am/server/logs directory.

Problem: After you use the Security Console wizard or the Cloud Authentication Service Configuration page to connect Authentication Manager to the Cloud Authentication Service, the Authentication Manager User Dashboard can display Cloud Authentication Service users. If two users have the same email ID in two different Active Directory identity sources, the SecurID Authentication Manager User Profile can display details for both users, but the Cloud Authentication User Profile can only synchronize and provide details for one user.

Workaround: When you configure the SMTP Mail Service, make sure to test the connection. If a large number of invitations are sent, you do not need to wait for a response. Instead, you can view the success, warning, and error messages in the system and audit logs.

Workaround: Log back or remain logged onto the Operation Console of the replica instance during the promotion to view the Progress Monitor information. When the promotion is complete, the Operation Console confirms the promotion to a primary instance with next steps.

Workaround: This functionality is intentional. The pre-promotion check allows the administrator who is promoting the replica instance to identify and correct any issues. When the promotion for maintenance begins, any administrator can view the Progress Monitor on the replica instance that is being promoted.

An array that contains the angular view number for each frame. Required if the value of Frame Increment Pointer (0028,0009) includes the Tag for Angular View Vector (0054,0090). See Section C. for specialization.

Angular View Vector (0054,0090) is an indexing vector. The value of the nth element of this vector is the angular view number of the nth frame in this image. If Image Type (0008,0008), Value 3, is TOMO or GATED TOMO, then the value shall be from 1 to Number of Frames in Rotation (0054,0053).

Zoom Center (0028,0032) is the offset between the un-zoomed camera field of view and field of view, measured from the center of the un-zoomed camera field of view to the center the of the zoomed field of view. The offset is measured in mm in the un-zoomed camera FOV dimensions. Positive values are to the right and down from the un-zoomed center, as viewed from the image plane. When this attribute is not given, the Zoom Center is assumed to be 0\0.

Data Information Sequence (0054,0063) shall contain a single sequence item that applies to the sum of all angular views, except when Image Type (0008,0008) Value 3 is GATED TOMO. In this case it shall have either a single item that applies to the sum of all angular views, or it shall have one item for each angular view.

The view of the patient anatomy may be described using coded terminology in the View Code Sequence (0054,0220). The view is typically specified by transducer position relative to the patient anatomy and/or transducer orientation,

The view may be described by a single Code Sequence Item, or by combination of post-coordinated Code Sequence Items. The principal coded item is specified in View Code Sequence, and modifier terms in the View Modifier Code Sequence (0054,0222). The Baseline CIDs for post-coordinated encoding of view are:

Luminance of a hypothetical viewing device illuminating a piece of monochrome transmissive film, or for the case of reflective media, luminance obtainable from diffuse reflection of the illumination present. Expressed as L0, in candelas per square meter (cd/m2).

Monochrome media that is being digitized is often measured in Optical Density values. These values need to be converted to P-Values for storage and display. The P-Values used in an image correspond to the perception of a human observer viewing the film on a hypothetical viewing device (such as a light box), using the specified values of Illumination (2010,015E) and Reflected Ambient Light (2010,0160).

Frame Dimension Pointer (0028,000A) identifies attributes that vary or increment with each frame, and are clinically significant for viewing or processing the image. This is intended for SOP Instances whose preferred clinical presentation is dependent on frame relationships other than simply time.

One technique employed in data warehouses to improve performance is the creation of summaries. Summaries are special types of aggregate views that improve query execution times by precalculating expensive joins and aggregation operations prior to execution and storing the results in a table in the database. For example, you can create a summary table to contain the sums of sales by region and by product.

The summaries or aggregates that are referred to in this book and in literature on data warehousing are created in Oracle Database using a schema object called a materialized view. Materialized views can perform a number of roles, such as improving query performance or providing replicated data.

The database administrator creates one or more materialized views, which are the equivalent of a summary. The end user queries the tables and views at the detail data level. The query rewrite mechanism in the Oracle server automatically rewrites the SQL query to use the summary tables. This mechanism reduces response time for returning results from the query. Materialized views within the data warehouse are transparent to the end user or to the database application.

Although materialized views are usually accessed through the query rewrite mechanism, an end user or database application can construct queries that directly access the materialized views. However, serious consideration should be given to whether users should be allowed to do this because any change to the materialized views affects the queries that reference them.

In data warehouses, you can use materialized views to precompute and store aggregated data such as the sum of sales. Materialized views in these environments are often referred to as summaries, because they store summarized data. They can also be used to precompute joins with or without aggregations. A materialized view eliminates the overhead associated with expensive joins and aggregations for a large or important class of queries.

In distributed environments, you can use materialized views to replicate data at distributed sites and to synchronize updates done at those sites with conflict resolution methods. These replica materialized views provide local access to data that otherwise would have to be accessed from remote sites. Materialized views are also useful in remote data marts.

You can also use materialized views to download a subset of data from central servers to mobile clients, with periodic refreshes and updates between clients and the central servers. This chapter focuses on the use of materialized views in data warehouses.

You can use materialized views to increase the speed of queries on very large databases. Queries to large databases often involve joins between tables, aggregations such as SUM, or both. These operations are expensive in terms of time and processing power. The type of materialized view you create determines how the materialized view is refreshed and used by query rewrite.

Materialized views improve query performance by precalculating expensive join and aggregation operations on the database prior to execution and storing the results in the database. The query optimizer automatically recognizes when an existing materialized view can and should be used to satisfy a request. It then transparently rewrites the request to use the materialized view. Queries go directly to the materialized view and not to the underlying detail tables. In general, rewriting queries to use materialized views rather than detail tables improves response time. Figure 5-1 illustrates how query rewrite works. 041b061a72


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