First release of the Scotty window processor

.gitignore

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+*.iml
+*.idea
+*target
+*.class

.travis.yml

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+language: java

LICENSE

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README.md

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+# Scotty: Efficient Window Aggregation for out-of-order Stream Processing [![Build Status](https://travis-ci.org/TU-Berlin-DIMA/scotty-window-processor.svg?branch=master)](https://travis-ci.org/TU-Berlin-DIMA/scotty-window-processor)
+
+This repository provides Scotty, a framework for efficient window aggregations for out-of-order Stream Processing.
+
+### Features:
+- High performance window aggregation with stream slicing. 
+- Scales to thousands of concurrent windows. 
+- Support for Tumbling, Sliding, and Session Windows.
+- Out-of-order processing.
+- Aggregate Sharing among all concurrent windows.
+- Connector for [Apache Flink](https://flink.apache.org/).
+
+### Resources:
+ - [Paper: Scotty: Efficient Window Aggregation for out-of-order Stream Processing](http://www.user.tu-berlin.de/powibol/assets/publications/traub-scotty-icde-2018.pdf)
+ - [Presentations Slides FlinkForward 2018](https://www.slideshare.net/powibol/flink-forward-2018-efficient-window-aggregation-with-stream-slicing)
+ - [API Documentation](docs)
+
+### Flink Integration Example:
+
+```java
+// Instantiate Scotty window operator
+KeyedScottyWindowOperator<Tuple, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>> windowOperator =
+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 SessionWindow(1000));
+
+// Add operator to Flink job
+stream.keyBy(0)
+      .process(windowOperator)
+```
+
+### Benchmark:  
+
+
+Throughput in comparison to the Flink standard window operator (Window Buckets) for Sliding 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.
+
+![](charts/SlidingWindow.png?raw=true)
+
+Throughput in comparison to Flink for concurrent Tumbling Windows:
+
+![](charts/ConcurrentTumblingWindows.png?raw=true)
+
+### Roadmap:
+We plan to extend our framework with the following features:
+
+- Support for User-Defined windows
+- Support for count-based windows and other window measures
+- Support for Refinements
+- Connector for [Apache Beam](https://beam.apache.org/)
+- Support of Flink Checkpoints and State Backends
+
+### Setup:
+The maven package is currently not publically available.
+Therefore we have to build it from source:
+
+`
+git clone git@github.com:TU-Berlin-DIMA/scotty-window-processor.git
+`
+
+`
+mvn clean install
+`
+
+Then you can use the library in your maven project.
+
+```xml
+<dependency> 
+ <groupId>de.tub.dima.scotty</groupId>
+ <artifactId>flink-connector</artifactId>
+ <version>0.1</version>
+</dependency>
+```
+
+### Scotty: Efficient Window Aggregation for out-of-order Stream Processing at ICDE 2018
+Scotty was first published at the [34th IEEE International Conference on Data Engineering](https://icde2018.org/) in April 2018.  
+
+**Abstract:**  
+Computing aggregates over windows is at the core
+of virtually every stream processing job. Typical stream processing
+applications involve overlapping windows and, therefore,
+cause redundant computations. Several techniques prevent this
+redundancy by sharing partial aggregates among windows. However,
+these techniques do not support out-of-order processing and
+session windows. Out-of-order processing is a key requirement
+to deal with delayed tuples in case of source failures such as
+temporary sensor outages. Session windows are widely used to
+separate different periods of user activity from each other.
+In this paper, we present Scotty, a high throughput operator
+for window discretization and aggregation. Scotty splits streams
+into non-overlapping slices and computes partial aggregates per
+slice. These partial aggregates are shared among all concurrent
+queries with arbitrary combinations of tumbling, sliding, and
+session windows. Scotty introduces the first slicing technique
+which (1) enables stream slicing for session windows in addition
+to tumbling and sliding windows and (2) processes out-of-order
+tuples efficiently. Our technique is generally applicable to a
+broad group of dataflow systems which use a unified batch and
+stream processing model. Our experiments show that we achieve
+a throughput an order of magnitude higher than alternative stateof-the-art
+solutions.
+
+- Paper: [Scotty: Efficient Window Aggregation for out-of-order Stream Processing](http://www.user.tu-berlin.de/powibol/assets/publications/traub-scotty-icde-2018.pdf)
+
+- BibTeX citation:
+```
+@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},
+  booktitle={34th IEEE International Conference on Data Engineering (ICDE)},
+  year={2018}
+}
+```
+
+

_config.yml

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+theme: jekyll-theme-tactile

benchmark/configurations/random_tumbling_benchmark.json

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+{
+  "name" : "tumbling_window_benchmark",
+  "runtime" : 30,
+  "throughput": 2000000,
+  "windowConfigurations": [
+    [
+      "randomTumbling(1,1,20)"
+    ],
+    [
+      "randomTumbling(5,1,20)"
+    ],
+    [
+      "randomTumbling(10,1,20)"
+    ],
+    [
+      "randomTumbling(20,1,20)"
+    ],
+    [
+      "randomTumbling(40,1,20)"
+    ],
+    [
+      "randomTumbling(80,1,20)"
+    ],
+    [
+      "randomTumbling(500,1,20)"
+    ],
+    [
+      "randomTumbling(1000,1,20)"
+    ]
+  ],
+  "configurations": [
+    "Slicing"
+  ],
+  "sessionConfig": {
+    "gapCount": 10,
+    "minGapTime": 1000,
+    "maxGapTime": 1001
+  },
+  "aggFunctions":[
+    "Sum"
+  ]
+}

benchmark/configurations/random_tumbling_benchmark_flink.json

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+{
+  "name" : "tumbling_window_benchmark",
+  "runtime" : 30,
+  "throughput": 1600,
+  "windowConfigurations": [
+    [
+      "randomTumbling(1,1,20)"
+    ]
+  ],
+  "configurations": [
+    "Flink"
+  ],
+  "sessionConfig": {
+    "gapCount": 10,
+    "minGapTime": 1000,
+    "maxGapTime": 1001
+  },
+  "aggFunctions":[
+    "Sum"
+  ]
+}