Docs: Landing page cleaned, more JOSS compatible, removed links and o…

…ld references

README.md

@@ -97,8 +97,6 @@ Run the CLI command: `dbmsbenchmarker run -e yes -b -f ./config`
 
 This is equivalent to `python benchmark.py run -e yes -b -f ./config`
 
-For more options, see the [documentation](Options.html#command-line-options-and-configuration)
-
 After benchmarking has been finished we will see a message like
 ```
 Experiment <code> has been finished
@@ -116,7 +114,7 @@ Visit the address in a browser and select the experiment `<code>`.
 
 This is equivalent to `python dashboard.py`.
 
-Alternatively you may use a [Jupyter notebook](Evaluation-Demo.ipynb).
+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).
 
 ## Benchmarking in a Kubernetes Cloud
 

docs/Options.md

@@ -12,8 +12,6 @@ This tool can be [used](#usage) to
 
 Basically this can be done running `dbmsbenchmarker run` or `dbmsbenchmarker continue` with additional parameters.
 
-# Options
-
 ## Featured Parameters
 
 The lists of [DBMS](#connection-file) and [queries](#query-file) are given in config files in dict format.
@@ -38,6 +36,8 @@ Benchmarks can be [randomized](#randomized-query-file) (optionally with specifie
 
 This is inspired by [TPC-H](http://www.tpc.org/tpch/) and [TPC-DS](http://www.tpc.org/tpcds/) - Decision Support Benchmarks.
 
+# Options
+
 ## Command Line Options and Configuration
 
 How to configure the benchmarker can be illustrated best by looking at the source code of the [command line tool](../benchmark.py), which will be described in the following.

paper.bib

@@ -10,4 +10,16 @@ @InProceedings{Erdelt20
 pages="75--92",
 abstract="Performance benchmarking of Database Management Systems (DBMS) is an important yet complicated process. We motivate and present two supporting Python packages which help to avoid common pitfalls and in particular improve reproducibility and transparency in heterogeneous systems with hardware accelerators. The first addresses operational aspects by providing dynamic testbeds using Docker images, especially for cloud-based systems. The second helps planning and recurrently running experiments in a predefined setup via JDBC/SQL, and analyzing results with automated reports and an interactive dashboard. The purpose of this is to thoroughly evaluate aspects of performances of DBMS based on real-life measurements, runtime and hardware metrics, depending on various parameters including the hardware, and with high repeatability. We present a series of TPC-H inspired example benchmarks in a Kubernetes cluster for demonstration, and some lessons learned.",
 isbn="978-3-030-84924-5"
-}
+}
+
+
+@InProceedings{ErdeltOrchestrator,
+author="Erdelt, Patrick K.",
+editor="Nambiar, Raghunath
+and Poess, Meikel",
+title="Orchestrating DBMS Benchmarking in the Cloud with Kubernetes",
+booktitle="Performance Evaluation and Benchmarking",
+year="2022",
+publisher="Springer International Publishing",
+address="Cham",
+}