Seminars

Title: General Dynamic Yannakakis: Conjunctive Queries with Theta Joins Under Updates
Abstract:
The ability to efficiently analyze changing data is a key requirement
of many real-time analytics applications like Stream Processing,
Complex Event Recognition, Business Intelligence, and Machine
Learning.

Traditional approaches to this problem are based either on the
materialization of subresults (to avoid their recomputation) or on the
recomputation of subresults (to avoid the space overhead of
materialization). Both techniques have recently been shown suboptimal:
instead of fully materializing results and subresults, one can
maintain a data structure that supports efficient maintenance under
updates and can quickly enumerate the full query output, as well as
the changes produced under single updates.

In our work we are concerned with designing a practical family of
algorithms for dynamic query evaluation based on this idea, and for
queries featuring both equi-joins and inequality joins, as well as
certain forms of aggregation. Our main insight is that, for acyclic
conjunctive queries, such algorithms can naturally be obtained by
modifying Yannakakis' seminal algorithm for processing acyclic joins
in the static setting.

In this talk I present the main ideas behind this modfication,
offset it against the traditional ways of doing incremental view
maintenance, and discuss recent extensions such as dealing with
general theta-joins.