add bibtex citation

README.md

@@ -10,6 +10,28 @@ Please cite our work:
 
 > Jalal Mostafa, Sara Wehbi, Suren Chilingaryan, and Andreas Kopmann. 2022. SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things. In 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). Association for Computing Machinery, New York, NY, USA, Article 12, 1–11. https://doi.org/10.1145/3538712.3538723
 
+### Bibtex
+
+```bibtex
+@inproceedings{10.1145/3538712.3538723,
+    author = {Mostafa, Jalal and Wehbi, Sara and Chilingaryan, Suren and Kopmann, Andreas},
+    title = {SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things},
+    year = {2022},
+    isbn = {9781450396677},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3538712.3538723},
+    doi = {10.1145/3538712.3538723},
+    abstract = {Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.},
+    booktitle = {Proceedings of the 34th International Conference on Scientific and Statistical Database Management},
+    articleno = {12},
+    numpages = {11},
+    keywords = {time-series databases, database management systems, industrial internet of things, scientific experiments, sensor data, time-series},
+    location = {Copenhagen, Denmark},
+    series = {SSDBM '22}
+}
+```
+
 ## How to run
 
 1. Create your workload as `App.config` (case-sensitive) in `BenchmarkTool`.