1,583 research outputs found
Sampling-Based Query Re-Optimization
Despite of decades of work, query optimizers still make mistakes on
"difficult" queries because of bad cardinality estimates, often due to the
interaction of multiple predicates and correlations in the data. In this paper,
we propose a low-cost post-processing step that can take a plan produced by the
optimizer, detect when it is likely to have made such a mistake, and take steps
to fix it. Specifically, our solution is a sampling-based iterative procedure
that requires almost no changes to the original query optimizer or query
evaluation mechanism of the system. We show that this indeed imposes low
overhead and catches cases where three widely used optimizers (PostgreSQL and
two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and
authors that appears in the Proceedings of the ACM SIGMOD International
Conference on Management of Data (SIGMOD 2016
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
Open issues in semantic query optimization in relational DBMS
After two decades of research into Semantic Query Optimization (SQO) there is clear agreement as to the efficacy of SQO. However, although there are some experimental implementations there are still no commercial implementations. We
first present a thorough analysis of research into SQO. We identify three problems which inhibit the effective use of SQO in Relational Database Management Systems(RDBMS). We then propose solutions to these problems and describe first steps towards the implementation of an effective semantic query optimizer for relational databases
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