17,099 research outputs found
Measure based metrics for aggregated data
Aggregated data arises commonly from surveys and censuses where groups of individuals are studied as coherent entities. The aggregated data can take many forms including sets, intervals, distributions and histograms. The data analyst needs to measure the similarity between such aggregated data items and a range of metrics are reported in the literature to achieve this (e.g. the Jaccard metric for sets and the Wasserstein metric for histograms). In this paper, a unifying theory based on measure theory is developed that establishes not only that known metrics are essentially similar but also suggests new metrics
On central tendency and dispersion measures for intervals and hypercubes
The uncertainty or the variability of the data may be treated by considering,
rather than a single value for each data, the interval of values in which it
may fall. This paper studies the derivation of basic description statistics for
interval-valued datasets. We propose a geometrical approach in the
determination of summary statistics (central tendency and dispersion measures)
for interval-valued variables
Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance
In this paper we present a linear regression model for modal symbolic data.
The observed variables are histogram variables according to the definition
given in the framework of Symbolic Data Analysis and the parameters of the
model are estimated using the classic Least Squares method. An appropriate
metric is introduced in order to measure the error between the observed and the
predicted distributions. In particular, the Wasserstein distance is proposed.
Some properties of such metric are exploited to predict the response variable
as direct linear combination of other independent histogram variables. Measures
of goodness of fit are discussed. An application on real data corroborates the
proposed method
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
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