3,156 research outputs found
Hypothetical answers to continuous queries over data streams
Continuous queries over data streams may suffer from blocking operations
and/or unbound wait, which may delay answers until some relevant input arrives
through the data stream. These delays may turn answers, when they arrive,
obsolete to users who sometimes have to make decisions with no help whatsoever.
Therefore, it can be useful to provide hypothetical answers - "given the
current information, it is possible that X will become true at time t" -
instead of no information at all.
In this paper we present a semantics for queries and corresponding answers
that covers such hypothetical answers, together with an online algorithm for
updating the set of facts that are consistent with the currently available
information
Improving the Deductive System DES with Persistence by Using SQL DBMS's
This work presents how persistent predicates have been included in the
in-memory deductive system DES by relying on external SQL database management
systems. We introduce how persistence is supported from a user-point of view
and the possible applications the system opens up, as the deductive expressive
power is projected to relational databases. Also, we describe how it is
possible to intermix computations of the deductive engine and the external
database, explaining its implementation and some optimizations. Finally, a
performance analysis is undertaken, comparing the system with current
relational database systems.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
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
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