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DTSTART:20240310T100000
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DTSTART:20241103T090000
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DTSTART;TZID=America/Los_Angeles:20240510T130000
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SUMMARY:Mike Heddes: Efficient Cardinality Estimation of Multi-Join Queries using Count Sketches
DESCRIPTION:Abstract: \nCardinality estimates are a primary input to query optimizers to determine an appropriate join order. The seminal AMS sketch can estimate the cardinality of an equi-join between two relations using little space. Since then\, two important advancements are the Count sketch\, a method which significantly improves upon the sketching time\, and secondly\, an extension of the AMS sketch to accommodate multi-join queries. However\, combining the strengths of these methods to maintain sketches for multi-join queries while ensuring fast update times is a non-trivial task\, and has remained an open problem for decades as highlighted in the existing literature. This talk will address this problem by introducing a novel sketching method which has fast updates\, even for sketches capable of accurately estimating the cardinality of complex multi-join queries. Experimental results confirm the significant improvement in update time complexity\, resulting in orders of magnitude faster estimates\, with equal or better estimation accuracy.\n\n\nBio:\nMike Heddes is a 4th-year PhD candidate at the University of California\, Irvine under supervision of Alex Nicolau and Tony Givargis. His research focusses on efficient algorithms for big data applications in machine learning and data mining. He has publications in prestigious venues such as SIGMOD\, KDD\, and JMLR. Mike has interned at Intel Labs as well as with the Advanced Concepts Team of the European Space Agency.
URL:https://isg.ics.uci.edu/event/mike-heddes-efficient-cardinality-estimation-of-multi-join-queries-using-count-sketches/
LOCATION:DBH 4011
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