Merge pull request #37 from taehyounpark/taehyounpark-patch-2

Update description of ana -> queryosity

Author: masonproffitt

README.md

@@ -35,7 +35,7 @@ Language implementations
 |[groot](https://github.com/go-hep/examples/tree/master/groot/bench-opendata)|[Go](https://golang.org)|Part of the [Go-HEP](https://go-hep.org/) project, `groot` is a pure Go package that provides read/write access to ROOT files|
 |[coffea](https://github.com/CoffeaTeam/coffea-benchmarks/tree/master)|Python + Numpy|[Coffea](https://github.com/CoffeaTeam/coffea) builds on numpy and awkward-array for columnar data analysis in Python|
 |[bamboo](https://github.com/pieterdavid/bamboo-adl-benchmarks)|Python + RDataFrame|The [bamboo](https://gitlab.cern.ch/cp3-cms/bamboo) analysis framework provides a high-level Python interface to RDataFrame (technically an embedded domain-specific language)|
-|[ana](https://github.com/taehyounpark/ana-benchmarks)| C++ | [ana](https://github.com/taehyounpark/ana) is a columnar data analysis interface, with support for arbitrary dataset formats, columnar data types, and results output through user-side implementations. |
+|[queryosity](https://github.com/taehyounpark/queryosity-benchmarks)| C++ | [Queryosity](https://queryosity.readthedocs.io/en/latest/) is a (semi-)structured data analysis library with support for arbitrary data types. |
 |[Rumble](https://github.com/RumbleDB/hep-iris-benchmark-jsoniq)|[JSONiq](https://www.jsoniq.org/) (an [XQuery](https://en.wikipedia.org/wiki/XQuery) dialect for [JSON](https://en.wikipedia.org/wiki/JSON) data)|Most data in ROOT files can be exposed in the JSON data model and can thus be processed by JSONiq. This implementation is targeted to be run on [Rumble](https://rumbledb.org/), a JSONiq implementation on top of Spark, but could be run by any other JSONiq processor.|
 |[BigQuery](https://github.com/RumbleDB/iris-hep-benchmark-bigquery)|[BigQuery's dialect](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) of [SQL](https://en.wikipedia.org/wiki/SQL)|SQL is arguably the most wide-spread language for querying structured data. Since SQL:1999, it supports arrays and structured types and is thus, in principle, suited for typical HEP analyses, though not many implementations support these features. BigQuery's dialect is based on SQL:2011, supports the mentioned features, and has a few additional language constructs that make queries more concise.|
 |[PrestoDB](https://github.com/RumbleDB/iris-hep-benchmark-presto)|[PrestoDB's dialect](https://prestodb.io/docs/current/sql/select.html) of [SQL](https://en.wikipedia.org/wiki/SQL) |Like BigQuery, Presto has some support for arrays and structured types; however, it only has limited support for nested queries and a more verbose syntax than BigQuery.|