#Multi Tier Annotation Search
In recent years, multiple solutions have come available providing search on huge amounts of plain text and metadata. Scalable searchability on annotated text however still appears to be problematic. Using Mtas, we not only take advantage of the strength from Lucene and Solr, but extend queries with CQL conditions on annotated text
[pos="LID"] [pos="ADJ"]? [lemma="amsterdam"]
<entity="location/> within (<s/> containing [lemma="utrecht"])
Parsers for several document formats are provided, each with extended possibilities for configuration, and advanced query features like statistics, termvectors and kwic are available.
Source code and releases are available on GitHub, see installation instructions on how to get started.
Nederlab
One of the primary use cases for Mtas, the Nederlab project, currently1 provides access, both in terms of metadata and annotated text, to over 74 million items for search and analysis as specified below.
| Total | Mean | Min | Max | |
|---|---|---|---|---|
| Solr index size | 2,715 G | 60.3 G | 75 k | 288 G |
| Solr documents | 74,762,559 | 1,661,390 | 119 | 11,912,415 |
Collections are added and updated regularly by adding new cores, replacing cores and/or merging new cores with existing ones. Currently, the data is divided over 44 separate cores. For 41,437,881 of these documents, annotated text varying in size from 1 to over 3.5 million words is included:
| Total | Mean | Min | Max | |
|---|---|---|---|---|
| Words | 18,494,454,357 | 446 | 1 | 3,537,883 |
| Annotations | 95,921,919,849 | 2,314 | 4 | 23,589,831 |
1 situation June 2018