Tongue volume and the mandibular dentition

A method utilizing the principle of fluid displacement was devised to measure the volume of the anterior portion of the tongue.A sample of thirty-nine men was used to test the relationships between measurable tongue volume, tongue length, certain angular cephalometric measurements, and lower arch dimensions. Seventeen subjects showing scalloping of the lateral borders of the tongue were compared with the main sample.Conclusions from this investigation are as follows: 1. 1. The volume of the anterior portion of the tongue can be measured with an average accuracy of 2.3 c.c. +/- 1.4 c.c., using the instrument described here.2. 2. In this sample, and with this method of measurement, the volume and length of the tongue seem to have little, if any, influence on the width and length of the lower dental arch, on the degree of interincisal relationship, and on the angle of the lower incisor teeth to the mandibular plane.3. 3. A statistically significant correlation of 0.4 exists between measurable tongue volume and arch perimeter.4. 4. Scalloping of the tongue does not appear to be an indication of a large tongue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32920/1/0000302.pd

Delta lake: high-performance ACID table storage over cloud object stores

Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects

Delta lake: high-performance ACID table storage over cloud object stores

Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects