SPATE: Compacting and exploring telco big data

Constantinos Costa, Georgios Chatzimilioudis, Demetrios Zeinalipour-Yazti, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this demonstration paper, we present SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time; and (ii) minimizing the response time for spatiotemporal data exploration queries over stored data. Our framework deploys lossless data compression to ingest streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. We augment our storage structures with decaying principles that lead to the progressive loss of detail as information gets older. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate SPATE in two modes: (i) Visual Mode, where attendees will be able to interactively explore synthetic telco traces we will provide; and (ii) SQL Mode, where attendees can submit custom SQL queries based on a provided schema.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages1419-1420
Number of pages2
ISBN (Electronic)9781509065431
DOIs
Publication statusPublished - 16 May 2017
Externally publishedYes
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Other

Other33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period19/4/1722/4/17

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Costa, C., Chatzimilioudis, G., Zeinalipour-Yazti, D., & Mokbel, M. (2017). SPATE: Compacting and exploring telco big data. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 1419-1420). [7930100] IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.203