Corpus ID: 8318965

STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion

@inproceedings{Grulich2017STREAMLINES,
  title={STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion},
  author={Philipp Marian Grulich and Tilmann Rabl and Volker Markl and Csaba Istv{\'a}n Sidl{\'o} and Andr{\'a}s A. Bencz{\'u}r},
  booktitle={EDBT/ICDT Workshops},
  year={2017}
}
STREAMLINE aims for improving the overall workflow of big data analytics systems. For this goal, it combines research in different areas to reduce the complexity of the work with data at rest and data in motion in a unified fashion. As a foundation STREAMLINE offers a uniform programming model on top of Apache Flink, for which it drives innovations in a wide range of areas, such as interactive data in motion visualization and advanced window aggregation techniques. 

References

SHOWING 1-3 OF 3 REFERENCES
I2: Interactive Real-Time Visualization for Streaming Data
TLDR
I, an interactive development environment that coordinates running cluster applications and corresponding visualizations such that only the currently depicted data points are processed and transferred, and shows how cluster programs can adapt to changed visualization properties at runtime to allow interactive data exploration on data streams. Expand
Apache Flink™: Stream and Batch Processing in a Single Engine
TLDR
This paper discusses the approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries, and presents its approach to distributed snapshot isolation and optimized two-phase commit protocols. Expand
Cutty: Aggregate Sharing for User-Defined Windows
TLDR
This paper introduces the concept of User-Defined Windows (UDWs), a simple, UDF-based programming abstraction that allows users to programmatically define custom windows, and defines semantics for UDWs, based on which Cutty, a low-cost aggregate sharing technique, is designed. Expand