Regular Paper

The VLDB Journal

, Volume 21, Issue 3, pp 287-307

First online:

Real-time creation of bitmap indexes on streaming network data

  • Francesco FuscoAffiliated withIBM Research - Zurich Email author 
  • , Michail VlachosAffiliated withIBM Research - Zurich
  • , Marc Ph. StoecklinAffiliated withIBM Research - T. J. Watson Research Center

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High-speed archival and indexing solutions of streaming traffic are growing in importance for applications such as monitoring, forensic analysis, and auditing. Many large institutions require fast solutions to support expedient analysis of historical network data, particularly in case of security breaches. However, “turning back the clock” is not a trivial task. The first major challenge is that such a technology needs to support data archiving under extremely high-speed insertion rates. Moreover, the archives created have to be stored in a compressed format that is still amenable to indexing and search. The above requirements make general-purpose databases unsuitable for this task and dedicated solutions are required. This work describes a solution for high-speed archival storage, indexing, and data querying on network flow information. We make the two following important contributions: (a) we propose a novel compressed bitmap index approach that significantly reduces both CPU load and disk consumption and, (b) we introduce an online stream reordering mechanism that further reduces space requirements and improves the time for data retrieval. The reordering methodology is based on the principles of locality-sensitive hashing (LSH) and also of interest for other bitmap creation techniques. Because of the synergy of these two components, our solution can sustain data insertion rates that reach 500,000–1 million records per second. To put these numbers into perspective, typical commercial network flow solutions can currently process 20,000–60,000 flows per second. In addition, our system offers interactive query response times that enable administrators to perform complex analysis tasks on the fly. Our technique is directly amenable to parallel execution, allowing its application in domains that are challenged by large volumes of historical measurement data, such as network auditing, traffic behavior analysis, and large-scale data visualization in service provider networks.


Bitmap index Locality sensitive hashing Data stream Data archive