ADS-BI: Compressed Indexing of ADS-B Data

The introduction of Automatic Dependent Surveillance - Broadcast (ADS-B), a satellite-based aircraft tracking technology, and the increasing installation of ADS-B receiver stations around the globe eases the tracking of aircraft, compared with traditional solutions using secondary radar. Given the large scale of ADS-B implementation and the high frequency of data collection, storing and managing ADS-B induced data has become increasingly difficult: The worldwide ADS-B data easily aggregates to several hundreds of terabyte per year, depending on the spatial coverage and temporal resolution. Standard data management solutions do not work well for ADS-B data, since they either require a large uncompressed index structure or cannot be queried efficiently. In this paper, they propose a novel compressed index structure for managing ADS-B data, called ADS-BI. The essential building blocks are spatio-temporal reference partitioning, reordering, and compression. On top of the partitioned, compressed representation, metadata is stored effectively, and exploited during query answering for typical ATM related task such as trajectory adherence evaluation, as well as complexity and safety metrics assessment by only accessing parts of the compressed data as necessary. Their novel index structure is evaluated on worldwide ADS-B data for a week in November 2016. For comparison, they implemented ten standard compression/indexing methods. The experiments reveal that none of these traditional methods can target the sweet spot between a small storage and efficient query answering. Their novel technique provides fast query answering at smallest storage costs. This paper contributes toward efficient handling of the increasing amount of traffic data in air traffic management, and eventually, toward more efficient and safer air transportation.


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01690455
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Dec 31 2018 9:08AM