Publications by Louis%20Woods

@article{abc,
	author = {Louis Woods and Gustavo Alonso and Jens Teubner},
	journal = {TRETS},
	title = {Parallelizing Data Processing on FPGAs with Shifter Lists.},
	url = {http://doi.acm.org/10.1145/2629551},
	year = {2015}
}

@inproceedings{abc,
	author = {Louis Woods and Zsolt Istv{\'a}n and Gustavo Alonso},
	booktitle = {PVLDB},
	title = {Ibex - An Intelligent Storage Engine with Support for Advanced SQL Off-loading.},
	url = {http://www.vldb.org/pvldb/vol7/p963-woods.pdf},
	year = {2014}
}

@phdthesis{abc,
	author = {Louis Woods},
	school = {21967},
	title = {FPGA-Enhanced Data Processing Systems},
	year = {2014}
}

@inproceedings{abc,
	author = {Zsolt Istv{\'a}n and Louis Woods and Gustavo Alonso},
	booktitle = {International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA},
	title = {Histograms as a side effect of data movement for big data.},
	url = {http://doi.acm.org/10.1145/2588555.2612174},
	year = {2014}
}

@inproceedings{abc,
	author = {Louis Woods and Zsolt Istv{\'a}n and Gustavo Alonso},
	booktitle = {23rd International Conference on Field programmable Logic and Applications, FPL 2013, Porto, Portugal},
	title = {Hybrid FPGA-accelerated SQL query processing.},
	url = {http://dx.doi.org/10.1109/FPL.2013.6645619},
	year = {2013}
}

@inproceedings{abc,
	author = {Louis Woods and Jens Teubner and Gustavo Alonso},
	booktitle = {Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, New York, NY, USA},
	title = {Less watts, more performance: an intelligent storage engine for data appliances.},
	url = {http://doi.acm.org/10.1145/2463676.2463685},
	year = {2013}
}

@inproceedings{abc,
	author = {Louis Woods and Gustavo Alonso and Jens Teubner},
	booktitle = {21st IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2013, Seattle, WA, USA},
	title = {Parallel Computation of Skyline Queries.},
	url = {http://doi.ieeecomputersociety.org/10.1109/FCCM.2013.18},
	year = {2013}
}

@article{abc,
	author = {Chongling Nie and Louis Woods and Jens Teubner},
	journal = {ACM Transactions on Database Systems (TODS), vol. 38(4)},
	title = {XLynx\&$\#$151;An FPGA-based XML Filter for Hybrid XQuery Processing.},
	year = {2013}
}

@inproceedings{abc,
	author = {Cagri Balkesen and Louis Woods and Jens Teubner},
	booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW), - Workshopband, 15. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 11.-15.3.2013 in Magdeburg, Germany. Proceedings},
	title = {Tutorium: Neue Hardwarearchitekturen f{\"u}r das Datenmanagement (DPMH).},
	year = {2013}
}

@inproceedings{abc,
	author = {Jens Teubner and Louis Woods and Chongling Nie},
	booktitle = {Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, Scottsdale, AZ, USA},
	title = {Skeleton automata for FPGAs: reconfiguring without reconstructing.},
	url = {http://doi.acm.org/10.1145/2213836.2213863},
	year = {2012}
}

@inproceedings{abc,
	author = {Louis Woods and Ken Eguro},
	booktitle = {2012 IEEE 20th Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2012},
	title = {Groundhog - A Serial ATA Host Bus Adapter (HBA) for FPGAs.},
	url = {http://doi.ieeecomputersociety.org/10.1109/FCCM.2012.45},
	venue = {Toronto, Ontario, Canada},
	year = {2012}
}

@misc{abc,
	abstract = {Field-programmable gate arrays (FPGAs) are chip devices that can be runtime-reconfigured to realize arbitrary processing tasks directly in hardware. Industrial products as well as research prototypes demonstrated how this capability can be exploited to build highly efficient processors for data warehousing, data mining, or stream analysis tasks. 
On the flip side, the construction of dedicated hardware circuits requires considerable engineering efforts and skills that are often not available in application-focussed development teams. To bridge this gap, at ETH we have developed a set of tools that aid developers of high-performance stream processing solutions and enable agile hardware generation for changing application demands. 
In this demonstration, we showcase Snowfall, a compiler tool for low-level stream analysis. Comparable to scanner generators for software-based systems (e.g., lex/flex), Snowfall can be used to decode incoming data streams in hardware, react to low-level patterns in a stream, and perform initial input data analysis. Snowfall plays well together with Glacier, a query-to-hardware compiler that we described and demonstrated earlier. A typical use case is to use Snowfall for input parsing and pre-processing, then perform SQL-style query processing on top with a hardware query plan obtained with the help of Glacier. 
In the demo, we illustrate Snowfall based on a real-world use case with exceptionally high demands for throughput and latency. With the help of Snowfall, we perform risk checking for financial trading applications. Snowfall allows for a declarative description of the problem, yet will generate a hardware circuit that can process input streams in real time.},
	author = {Jens Teubner and Louis Woods},
	title = {Snowfall: Hardware Stream Analysis Made Easy},
	year = {2011}
}

@misc{abc,
	abstract = {We demonstrate a hardware implementation of a
complex event processor, built on top of field-programmable gate
arrays (FPGAs). Compared to CPU-based commodity systems,
our solution shows distinctive advantages for stream monitoring
tasks, e.g., wire-speed processing and predictable performance.
The demonstration is based on a query-to-hardware compiler
for complex event patterns that we presented at VLDB 2010 [1].
By example of a click stream monitoring application, we illustrate
the inner workings of our compiler and indicate how FPGAs can
act as efficient and reliable processors for event streams.},
	author = {Louis Woods and Jens Teubner and Gustavo Alonso},
	title = {Real-Time Pattern Matching with FPGAs},
	url = {http://dx.doi.org/10.1109/ICDE.2011.5767937},
	year = {2011}
}

@inproceedings{abc,
	abstract = {The rapidly increasing amount of data available for
real-time analysis (i.e., so-called operational business intelligence)
is creating an interesting opportunity for creative approaches to
speeding up data processing algorithms. One such approach that
is starting to become more common is using hardware accelerators
for stream processing. Typically these accelerators are
implemented on top of reconfigurable hardware, known as fieldprogrammable
gate arrays (FPGAs). Though the value of FPGAs
for data warehouses is gradually recognized by the database
community, their true potential for various business analytic tasks
is yet unexplored. In this line of research, we investigate FPGA
technology in the context of extreme data processing looking for
opportunities where FPGAs can be exploited to improve over
classical CPU-based architectures. We introduce a framework
for FPGA-accelerated (real-time) analytics including a query-tohardware
compiler for static complex event detection, an XPath
engine for dynamic query workloads, and templates for highspeed
data mining operators in hardware.},
	author = {Louis Woods and Gustavo Alonso},
	booktitle = {Workshops Proceedings of the 27th International Conference on Data Engineering, ICDE 2011},
	title = {Fast Data Analytics with FPGAs},
	url = {http://dx.doi.org/10.1109/ICDEW.2011.5767669},
	venue = {Hannover, Germany},
	year = {2011}
}

@article{abc,
	author = {Louis Woods and Jens Teubner and Gustavo Alonso},
	journal = {PVLDB},
	title = {Complex Event Detection at Wire Speed with FPGAs.},
	year = {2010}
}

@mastersthesis{abc,
	author = {Louis Woods},
	school = {ETH Z{\"u}rich},
	title = {FPGA-Accelerated Pattern Matching over Streams of Events},
	year = {2009}
}