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VAT to the Future: Extrapolating Visual Complex Event Processing

12 pagesPublished: December 23, 2019


A key technology to analyze high volume spatio-temporal data streams is complex event processing (CEP). CEP is unique in its ability to not only continuously process data as it arrives through common operations such as aggregations, but also to support pattern matching queries. Pattern Matching allows to detect a user-defined sequence of temporal predicates on event streams. The high volume flight data as provided by the OpenSky Network has a lot of characteristics that make it a perfect match for CEP. In particular, pattern matching operators can be utilized to detect a plethora of movement (landing, starting, evasion) and group patterns (airplanes closing in on each other) in a timely manner. However, CEP queries can be complex in nature and may require a combination of domain expertise and historical data analysis in order to deliver the desired results. In order to address these issues, we have combined a database-backed CEP system (ChronicleDB) with a scientific toolbox for interactive data exploration and geo visualization (Vat System). This allows users to interactively execute CEP queries and visually confirm the validity of their results, thus, simplifying the parameter tuning considerably.
In addition, our solution supports efficient and interactive time travel queries. It allows to combine event streams with additional data sources (e.g., remote sensing images) and processing technologies (e.g., machine learning models) to extract higher level knowledge. Finally, our ongoing work on visual analytics explores extrapolating query results to provide more timely feedback for critical situations and multi-query optimization techniques to allow for an even more efficient system in general.

Keyphrases: Complex Event Processing, Geovisualization, spatio-temporal data

In: Christina Pöpper and Martin Strohmeier (editors). Proceedings of the 7th OpenSky Workshop 2019, vol 67, pages 25--36

BibTeX entry
  author    = {Christian Beilschmidt and Johannes Dr\textbackslash{}"onner and Nikolaus Glombiewski and Michael K\textbackslash{}"orber and Michael Mattig and Andreas Morgen and Bernhard Seeger},
  title     = {VAT to the Future: Extrapolating Visual Complex Event Processing},
  booktitle = {Proceedings of the 7th OpenSky Workshop 2019},
  editor    = {Christina P\textbackslash{}"opper and Martin Strohmeier},
  series    = {EPiC Series in Computing},
  volume    = {67},
  pages     = {25--36},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/wz4w}}
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