Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Uǧur Çetintemel"'
Autor:
Bill Howe, David Maier, Magdalena Balazinska, Samuel Madden, Jennie Duggan, Timothy G. Mattson, Manasi Vartak, Jeremy Kepner, Uǧur Çetintemel, Nesime Tatbul, Michael Stonebraker, Vijay Gadepally, Stavros Papadopoulos, Stanley B. Zdonik, Jeffrey Heer, Aaron J. Elmore, Jeff Parkhurst, Tim Kraska
Publikováno v:
other univ website
This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, d
Publikováno v:
Proceedings of the VLDB Endowment. 3:1291-1301
Prediction is emerging as an essential ingredient for real-time monitoring, planning and decision support applications such as intrusion detection, e-commerce pricing and automated resource management. This paper presents a system that efficiently su
Publikováno v:
Proceedings of the VLDB Endowment. 1:66-77
Complex Event Detection (CED) is emerging as a key capability for many monitoring applications such as intrusion detection, sensor-based activity & phenomena tracking, and network monitoring. Existing CED solutions commonly assume centralized availab
Autor:
Stan Zdonik, Anand Srinivasan, Mitch Cherniack, Uǧur Çetintemel, Namit Jain, Johannes Gehrke, Jennifer Widom, Richard Tibbetts, Shailendra Mishra, Hari Balakrishnan
Publikováno v:
Proceedings of the VLDB Endowment. 1:1379-1390
This paper describes a unification of two different SQL extensions for streams and its associated semantics. We use the data models from Oracle and StreamBase as our examples. Oracle uses a time-based execution model while StreamBase uses a tuple-bas
Publikováno v:
ACM SIGMOD Record. 34:42-47
Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, net
Publikováno v:
ICDE Workshops
We introduce a new learning-based solution for portable database workload performance prediction. The current state of the art addresses performance prediction for individual, static hardware configurations and thus cannot generalize to new platforms
Publikováno v:
Proceedings of the 1st International Workshop on Hot Topics in Cloud Data Processing.
Stream processing applications run continuously and have varying load. Cloud infrastructures present an attractive option to meet these fluctuating computational demands. Coordinating such resources to meet end-to-end latency objectives efficiently i
Publikováno v:
SIGMOD Conference
Borealis-R is a replication-based system for both fast andhighly-available processing of data streams over wide-area networks. In Borealis-R, multiple operator replicas send outputs to downstream replicas, allowing each replica to use whichever data
Publikováno v:
ICDE
We present a replication-based approach that realizes both fast and highly-available stream processing over wide area networks. In our approach, multiple operator replicas send outputs to each downstream replica so that it can use whichever data arri
Publikováno v:
ICDE
We introduce pulse, a framework for processing continuous queries over models of continuous-time data, which can compactly and accurately represent many real-world activities and processes. Pulse implements several query operators, including filters,