Docs: Typos

README.md

@@ -17,6 +17,7 @@ See the [homepage](https://github.com/Beuth-Erdelt/DBMS-Benchmarker) and the [do
 ## Key Features
 
 DBMS-Benchmarker
+
 * is Python3-based
 * helps to **benchmark DBMS**
   * connects to all DBMS having a JDBC interface - including GPU-enhanced DBMS
@@ -52,6 +53,7 @@ As a result we obtain an interactive dashboard to inspect timing aspects.
 ### Configuration
 
 We need to provide
+
 * a [DBMS configuration file](Options.html#connection-file), e.g. in `./config/connections.config`  
 ```
 [
@@ -112,9 +114,7 @@ Run the command: `dbmsdashboard`
 This will start the evaluation dashboard at `localhost:8050`.
 Visit the address in a browser and select the experiment `<code>`.
 
-This is equivalent to `python dashboard.py`.
-
-Alternatively you may use a [Jupyter notebooks](https://github.com/Beuth-Erdelt/DBMS-Benchmarker/blob/master/Evaluation-Demo.ipynb), see a [rendered example](https://beuth-erdelt.github.io/DBMS-Benchmarker/Evaluation-Demo.html).
+Alternatively you may use a [Jupyter notebook](https://github.com/Beuth-Erdelt/DBMS-Benchmarker/blob/master/Evaluation-Demo.ipynb), see a [rendered example](https://beuth-erdelt.github.io/DBMS-Benchmarker/Evaluation-Demo.html).
 
 ## Benchmarking in a Kubernetes Cloud
 

docs/Concept.md

@@ -96,7 +96,7 @@ In the end we have
 * Per DBMS and Query:
   * Time per session
   * Time per run
-  * Time per run, split up in: connection / execution / data transfer
+  * Time per run, split up into: connection / execution / data transfer
   * Latency and Throughputs per run
   * Latency and Throughputs per session
 
@@ -113,10 +113,9 @@ Each query will be sent to every DBMS in the same number of runs.
 </p>
 
 This also respects randomization, i.e. every DBMS receives exactly the same versions of the query in the same order.
-
 We assume all DBMS will give us the same result sets.
 Without randomization, each run should yield the same result set.
-This tool automatically can check these assumptions by **comparison**.
+This tool can check these assumptions automatically by **comparison**.
 The resulting data table is handled as a list of lists and treated by this:
 ```
 # restrict precision
@@ -130,7 +129,6 @@ columnnames = [[i[0].upper() for i in connection.cursor.description]]
 hashed = columnnames + [[hashlib.sha224(pickle.dumps(data)).hexdigest()]]
 ```
 Result sets of different runs (not randomized) and different DBMS can be compared by their sorted table (small data sets) or their hash value or size (bigger data sets).
-
 In order to do so, result sets (or their hash value or size) are stored as lists of lists and additionally can be saved as csv files or pickled pandas dataframes.
 
 ## Monitoring Hardware Metrics

docs/Options.md

@@ -17,6 +17,7 @@ Basically this can be done running `dbmsbenchmarker run` or `dbmsbenchmarker con
 The lists of [DBMS](#connection-file) and [queries](#query-file) are given in config files in dict format.
 
 Benchmarks can be [parametrized](#query-file) by
+
 * number of benchmark runs: *Is performance stable across time?*
 * number of benchmark runs per connection: *How does reusing a connection affect performance?*
 * number of warmup and cooldown runs, if any: *How does (re)establishing a connection affect performance?*
@@ -26,6 +27,7 @@ Benchmarks can be [parametrized](#query-file) by
 * optional [comparison](#results-and-comparison) of result sets: *Do I always receive the same results sets?*
 
 Benchmarks can be [randomized](#randomized-query-file) (optionally with specified [seeds](#random-seed) for reproducible results) to avoid caching side effects and to increase variety of queries by taking samples of arbitrary size from a
+
 * list of elements
 * dict of elements (one-to-many relations)
 * range of integers

paper.md

@@ -70,7 +70,7 @@ As a result we obtain an interactive dashboard to inspect timing aspects.
 ### Configuration
 
 We need to provide
-* a [DBMS configuration file](Options.html#connection-file), e.g. in `./config/connections.config`  
+* a [DBMS configuration file](#connection-file), e.g. in `./config/connections.config`  
 ```
 [
   {
@@ -86,7 +86,7 @@ We need to provide
 ]
 ```
 * the required JDBC driver, e.g. `mysql-connector-java-8.0.13.jar`
-* a [Queries configuration file](Options.html#query-file), e.g. in `./config/queries.config`  
+* a [Queries configuration file](#query-file), e.g. in `./config/queries.config`  
 ```
 {
   'name': 'Some simple queries',
@@ -266,7 +266,7 @@ In order to do so, result sets (or their hash value or size) are stored as lists
 
 ## Monitoring Hardware Metrics
 
-To make hardware metrics available, we must [provide](Options.html#connection-file) an API URL for a Prometheus Server.
+To make hardware metrics available, we must [provide](#connection-file) an API URL for a Prometheus Server.
 The tool collects metrics from the Prometheus server with a step size of 1 second.
 
 ![Caption for example figure.\label{fig:Concept-Monitoring}](docs/Concept-Monitoring.png){ width=320 }
@@ -914,7 +914,7 @@ optional arguments:
 
 It has two options:
 * `--result-folder`: Path of a local folder containing result folders. This parameter is the same as for `benchmark.py`
-* `--anonymize`: If this flag is set, all DBMS are anonymized following the parameters in their [configuration](Options.html#connection-file).
+* `--anonymize`: If this flag is set, all DBMS are anonymized following the parameters in their [configuration](#connection-file).
 
 When you start the dashboard it is available at `localhost:8050`.
 
@@ -926,7 +926,7 @@ Optionally you can activate to have some default panels that will be included at
 
 ## Concept
 
-The dashboard analyzes the data in [three dimensions](Concept.html#evaluation) using various [aggregation functions](Concept.html#aggregation-functions):
+The dashboard analyzes the data in [three dimensions](#evaluation) using various [aggregation functions](#aggregation-functions):
 <p align="center">
 <img src="https://raw.githubusercontent.com/Beuth-Erdelt/DBMS-Benchmarker/master/docs/Evaluation-Cubes.png">
 </p>
@@ -1006,7 +1006,7 @@ In the settings panel you can select the
 
 * [Kind of measure](#data) you want to inspect (kind, name)
 * [Type](#graph-panels) of plot (graph type, x-axis, annotate)
-* [Aggregation functions](Concept.html#aggregation-functions).
+* [Aggregation functions](#aggregation-functions).
   The order of aggregation is
   1. Query (run dimension)
   1. Total (query dimension)
@@ -1032,11 +1032,11 @@ In the filter panel you can
   * single queries
 * receive details about
   * the connections (configurations)
-    * [Configuration](Options.html#connection-file)
+    * [Configuration](#connection-file)
     * DBMS
     * Resources
   * and the queries like
-    * [Configuration](Options.html#query-file)
+    * [Configuration](#query-file)
     * Number of runs
     * Result sets