Upcoming processors are combining different computing units in a tightly-coupled approach using a unified shared memory hierarchy. This tightly-coupled combination leads to novel properties with regard to cooperation and interaction. This paper demonstrates the advantages of those processors for a stream-join operator as an important data-intensive example. In detail, we propose our HELLS-Join approach employing all heterogeneous devices by outsourcing parts of the algorithm on the appropriate device. Our HELLS-Join performs better than CPU stream joins, allowing wider time windows, higher stream frequencies, and more streams to be joined as before
We present an adaptive load shedding approach for win-dowed stream joins. In contrast to the convent...
The inherently large and varying volumes of information generated in large scale systems demand near...
Data streaming systems face the possibility of having to shed load in the case of CPU or memory reso...
Upcoming processors are combining different computing units in a tightly-coupled approach using a un...
The window-based stream join is an important operator in all data streaming sys-tems. It has often h...
Data Stream Processing (DaSP) is a paradigm characterized by on-line (often real-time) applications ...
inf.mpg.de This work revisits the processing of stream joins on modern hardware architectures. Our w...
The emergence of applications producing continuous high-frequency data streams has brought forth a l...
Efficient and scalable stream joins play an important role in performing real-time analytics for man...
Summarization: Stream join is a fundamental operation that combines information from different high-...
Multi-way stream joins with expensive join predicates lead to great challenge for real-time (or clos...
Summarization: Stream join is a fundamental and computationally expensive data mining operation for ...
We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventi...
This paper introduces a class of join algorithms, termed W-join, for joining multiple infinite data ...
Dropping tuples has been commonly used for load shedding. However, tuple dropping generally is inade...
We present an adaptive load shedding approach for win-dowed stream joins. In contrast to the convent...
The inherently large and varying volumes of information generated in large scale systems demand near...
Data streaming systems face the possibility of having to shed load in the case of CPU or memory reso...
Upcoming processors are combining different computing units in a tightly-coupled approach using a un...
The window-based stream join is an important operator in all data streaming sys-tems. It has often h...
Data Stream Processing (DaSP) is a paradigm characterized by on-line (often real-time) applications ...
inf.mpg.de This work revisits the processing of stream joins on modern hardware architectures. Our w...
The emergence of applications producing continuous high-frequency data streams has brought forth a l...
Efficient and scalable stream joins play an important role in performing real-time analytics for man...
Summarization: Stream join is a fundamental operation that combines information from different high-...
Multi-way stream joins with expensive join predicates lead to great challenge for real-time (or clos...
Summarization: Stream join is a fundamental and computationally expensive data mining operation for ...
We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventi...
This paper introduces a class of join algorithms, termed W-join, for joining multiple infinite data ...
Dropping tuples has been commonly used for load shedding. However, tuple dropping generally is inade...
We present an adaptive load shedding approach for win-dowed stream joins. In contrast to the convent...
The inherently large and varying volumes of information generated in large scale systems demand near...
Data streaming systems face the possibility of having to shed load in the case of CPU or memory reso...