2,252 research outputs found
A winter index for benchmarking winter road maintenance operations on Ontario highways
Canada is one of the snowiest countries in the world, and winter road maintenance activities cost in the order of a billion dollars a year. These activities vary considerably over both space and time, partly due to differences in winter weather, but also because of differences in road and trafļ¬c mix, terrain and other factors. The thesis explores the association between winter road maintenance activities and winter weather. The ļ¬rst objective is to characterize the spatial and temporal variation in severe winter weather in Ontario. The second objective is to identify a winter weather index that is sensitive to road maintenance activities in Ontario. The third objective is to modify the identiļ¬ed winter index based on Ontarioās climate and road maintenance situations. To account for the variation in winter weather from year to year and across the Province, three winter weather indices were applied to Ontario data. Monthly index values were correlated with monthly salt usage, on a maintenance district level, for the recent ļ¬ve winters. One of these indices was then modiļ¬ed to better reļ¬ect the Ontario situation. Road information was also considered. The main results include: the spatial and temporal variation of winter weather severity in Ontario is quite large. Among the winter indices concerned, the SHRP winter index is most suitable for Ontario winter road maintenance. After assigning particular weights on certain climate variables and adding a freezing precipitation variable, the modiļ¬ed SHRP index correlated better with salt usage than the original SHRP index did in most MT O districts. Salt usage regression models with temperature and precipitation as the explanatory variables performed similarly to the SHRP index at the district level, but the model coefļ¬cients varied considerably from one district to another indicating that there are substantial place differences in how road maintenance authorities respond to winter weather. The signiļ¬cance of the results is realized when applied to winter road maintenance management procedures. The SHRP index can be used at the district level to interpret temporal differences in regional salt usage and winter severity. This index can also be used to indicate the spatial distribution of winter severity and road salt usage across the province, aiding in the allocation of maintenance resources to different regions based on winter weather
Reasoning about Record Matching Rules
To accurately match records it is often necessary to utilize the semantics of the data. Functional dependencies (FDs) have proven useful in identifying tuples in a clean relation, based on the semantics of the data. For all the reasons that FDs and their inference are needed, it is also important to develop dependencies and their reasoning techniques for matching tuples from
unreliable
data sources. This paper investigates dependencies and their reasoning for record matching. (a) We introduce a class of
matching dependencies
(MDs) for specifying the semantics of data in unreliable relations, defined in terms of
similarity metrics
and a
dynamic semantics
. (b) We identify a special case of MDs, referred to as
relative candidate keys
(RCKs), to determine what attributes to compare and how to compare them when matching records across possibly different relations. (c) We propose a mechanism for inferring MDs, a departure from traditional implication analysis, such that when we cannot match records by comparing attributes that contain errors, we may still find matches by using other, more reliable attributes. (d) We provide an
O
(
n
2
) time algorithm for inferring MDs, and an effective algorithm for deducing a set of RCKs from MDs. (e) We experimentally verify that the algorithms help matching tools efficiently identify keys at compile time for matching, blocking or windowing, and that the techniques effectively improve both the quality and efficiency of various record matching methods.
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An Iterative Scheme for Leverage-based Approximate Aggregation
The current data explosion poses great challenges to the approximate
aggregation with an efficiency and accuracy. To address this problem, we
propose a novel approach to calculate the aggregation answers with a high
accuracy using only a small portion of the data. We introduce leverages to
reflect individual differences in the samples from a statistical perspective.
Two kinds of estimators, the leverage-based estimator, and the sketch estimator
(a "rough picture" of the aggregation answer), are in constraint relations and
iteratively improved according to the actual conditions until their difference
is below a threshold. Due to the iteration mechanism and the leverages, our
approach achieves a high accuracy. Moreover, some features, such as not
requiring recording the sampled data and easy to extend to various execution
modes (e.g., the online mode), make our approach well suited to deal with big
data. Experiments show that our approach has an extraordinary performance, and
when compared with the uniform sampling, our approach can achieve high-quality
answers with only 1/3 of the same sample size.Comment: 17 pages, 9 figure
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