130 research outputs found
Derivation of incremental equations for PNF nested relations
Incremental view maintenance techniques are required for many new types of data models that are being increasingly used in industry. One of these models is the nested relational model that is used in the modelling complex objects in databases. In this paper we derive a group of expressions for incrementally evaluating query expressions in the nested relational model. We also present an algorithm to propagate base relation updates to a materialized view when the view is defined as a complex query
Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but
challenging problem. Causal effect estimation with unobserved variables in data
is even more difficult. The challenges lie in (1) whether the causal effect can
be estimated from observational data (identifiability); (2) accuracy of
estimation (unbiasedness), and (3) fast data-driven algorithm for the
estimation (efficiency). Each of the above problems by its own, is challenging.
There does not exist many data-driven methods for causal effect estimation so
far, and they solve one or two of the above problems, but not all. In this
paper, we present an algorithm that is fast, unbiased and is able to confirm if
a causal effect is identifiable or not under a very practical and commonly seen
problem setting. To achieve high efficiency, we approach the causal effect
estimation problem as a local search for the minimal adjustment variable sets
in data. We have shown that identifiability and unbiased estimation can be both
resolved using data in our problem setting, and we have developed theorems to
support the local search for searching for adjustment variable sets to achieve
unbiased causal effect estimation. We make use of frequent pattern mining
strategy to further speed up the search process. Experiments performed on an
extensive collection of synthetic and real-world datasets demonstrate that the
proposed algorithm outperforms the state-of-the-art causal effect estimation
methods in both accuracy and time-efficiency.Comment: 30 page
- …