An improved equivalence algorithm
Communications of the ACM
SQL.CT: Providing Data Management for Visual Exploration of CT Datasets
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
MonetDB/SQL Meets SkyServer: the Challenges of a Scientific Database
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Anomaly pattern detection in categorical datasets
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
DiscFinder: a data-intensive scalable cluster finder for astrophysics
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Scalable clustering algorithm for N-body simulations in a shared-nothing cluster
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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Large-scale N-body simulations play an important role in advancing our understanding of the formation and evolution of large structures in the universe. These computations require a large number of particles, in the order of 10-100 of billions, to realistically model phenomena such as the formation of galaxies. Among these particles, black holes play a dominant role on the formation of these structure. The properties of the black holes need to be assembled in merger tree histories to model the process where two or more black holes merge to form a larger one. In the past, these analyses have been carried out with custom approaches that no longer scale to the size of black hole datasets produced by current cosmological simulations. We present algorithms and strategies to store, in relational databases (RDBMS), a forest of black hole merger trees. We implemented this approach and present results with datasets containing 0.5 billion time series records belonging to over 2 million black holes. We demonstrate that this is a feasible approach to support interactive analysis and enables flexible exploration of black hole forest datasets.