Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI | http://purl.tuc.gr/dl/dias/C645792E-66E5-4E59-BB7F-4457C13496AB | - |
| Identifier | https://doi.org/10.1145/3465480.3467842 | - |
| Identifier | https://dl.acm.org/doi/10.1145/3465480.3467842 | - |
| Language | en | - |
| Extent | 2 pages | en |
| Title | Real-time processing of geo-distributed financial data | en |
| Creator | Kontaxakis Antonios | en |
| Creator | Κονταξακης Αντωνιος | el |
| Creator | Deligiannakis Antonios | en |
| Creator | Δεληγιαννακης Αντωνιος | el |
| Creator | Arndt Holger | en |
| Creator | Burkard Stefan | en |
| Creator | Kettner Claus-Peter | en |
| Creator | Pelikan Elke | en |
| Creator | Noack Kathleen | en |
| Publisher | Association for Computing Machinery (ACM) | en |
| Content Summary | Enabling real-time processing of financial data streams is extremely challenging, especially considering that typical operations that interest investors often require combining data across (a potentially quadratic number of) different pairs of stocks. In this paper we present the architecture and the components of our system for the real-time processing of geo-distributed financial data at scale. Our system can scale to larger resources and utilizes a Synopses Data Engine in order to efficiently handle complex cross-stock queries, such as the ones required to detect systemic risk or to help forecast the value of some stock. The rich set of supported operations is depicted at the Visual Analytics component of our system. | en |
| Type of Item | Αφίσα σε Συνέδριο | el |
| Type of Item | Conference Poster | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-06-23 | - |
| Date of Publication | 2021 | - |
| Subject | Real-time processing | en |
| Subject | Financial data | en |
| Subject | Flink | en |
| Bibliographic Citation | A. Kontaxakis, A. Deligiannakis, H. Arndt, S. Burkard, C.-P. Kettner, E. Pelikan, and K. Noack, “Real-time processing of geo-distributed financial data,” in Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems (DEBS 2021), virtual event, 2021, pp. 190–191, doi: 10.1145/3465480.3467842. | en |
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