initial release general stream slicing with count-based windows

README.md

@@ -6,6 +6,7 @@ This repository provides Scotty, a framework for efficient window aggregations f
 - High performance window aggregation with stream slicing. 
 - Scales to thousands of concurrent windows. 
 - Support for Tumbling, Sliding, and Session Windows.
+- Initial support for Count based windows.
 - Out-of-order processing.
 - Aggregate Sharing among all concurrent windows.
 - Connector for [Apache Flink](https://flink.apache.org/).
@@ -24,10 +25,13 @@ KeyedScottyWindowOperator<Tuple, Tuple2<Integer, Integer>, Tuple2<Integer, Integ
 new KeyedScottyWindowOperator<>(new SumWindowFunction());
 
 // Add multiple windows to the same operator
-windowOperator.addWindow(new TumblingWindow(1000));
-windowOperator.addWindow(new SlidingWindow(1000,5000));
+windowOperator.addWindow(new TumblingWindow(WindowMeasure.Time, 1000));
+windowOperator.addWindow(new SlidingWindow(WindowMeasure.Time, 1000, 5000));
 windowOperator.addWindow(new SessionWindow(1000));
 
+// Add count based window
+windowOperator.addWindow(new TumblingWindow(WindowMeasure.Count, 1000))
+
 // Add operator to Flink job
 stream.keyBy(0)
       .process(windowOperator)
@@ -36,7 +40,7 @@ stream.keyBy(0)
 ### Benchmark:  
 
 
-Throughput in comparison to the Flink standard window operator (Window Buckets) for Sliding Windows:  
+Throughput in comparison to the Flink standard window operator (Window Buckets) for Sliding Event-Time Windows:  
 We fix the window size to 60 seconds and modify the slide size.
 If the slide size gets smaller, Flink has to maintain a higher number of overlapping (concurrent) windows.
 
@@ -50,7 +54,7 @@ Throughput in comparison to Flink for concurrent Tumbling Windows:
 We plan to extend our framework with the following features:
 
 - Support for User-Defined windows
-- Support for count-based windows and other window measures
+- User-defined window measures
 - Support for Refinements
 - Connector for [Apache Beam](https://beam.apache.org/)
 - Support of Flink Checkpoints and State Backends
@@ -90,7 +94,7 @@ In this paper, we present the first general stream slicing technique for window
 ```
 @inproceedings{traub2019efficient,
   title={Efficient Window Aggregation with General Stream Slicing},
-  author={Traub, Jonas and Grulich, Philipp and Cu{\'e}llar, Alejandro Rodr{\'\i}guez and Bre{\ss}, Sebastian and Katsifodimos, Asterios and Rabl, Tilmann and Markl, Volker},
+  author={Traub, Jonas and Grulich, Philipp M. and Cu{\'e}llar, Alejandro Rodr{\'\i}guez and Bre{\ss}, Sebastian and Katsifodimos, Asterios and Rabl, Tilmann and Markl, Volker},
   booktitle={22th International Conference on Extending Database Technology (EDBT)},
   year={2019}
 }
@@ -133,7 +137,7 @@ solutions.
 ```
 @inproceedings{traub2018scotty,
   title={Scotty: Efficient Window Aggregation for out-of-order Stream Processing},
-  author={Traub, Jonas and Grulich, Philipp Marian and Cuellar, Alejandro Rodríguez and Breß, Sebastian and Katsifodimos, Asterios and Rabl, Tilmann and Markl, Volker},
+  author={Traub, Jonas and Grulich, Philipp M. and Cuellar, Alejandro Rodríguez and Breß, Sebastian and Katsifodimos, Asterios and Rabl, Tilmann and Markl, Volker},
   booktitle={34th IEEE International Conference on Data Engineering (ICDE)},
   year={2018}
 }