4,323 research outputs found
Enhancing reuse of data and biological material in medical research : from FAIR to FAIR-Health
The known challenge of underutilization of data and biological material from biorepositories as potential resources
formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved
data availability and reusabilityâentitled FAIR Principles (Findability, Accessibility, Interoperability, and
Reusability)âare likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data
should be viewed as a unified resource. This approach would facilitate access to complete provenance information,
which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for
optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the
FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility
and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological
material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human
material and data. These FAIR-Health principles should then be applied to both the biological material and data. We
also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of
volume and breadth of medical data generation, as well as the associated need to process the data efficiently.peer-reviewe
Collaborative Reuse of Streaming Dataflows in IoT Applications
Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark
Streaming enable composition of continuous dataflows that execute persistently
over data streams. They are used by Internet of Things (IoT) applications to
analyze sensor data from Smart City cyber-infrastructure, and make active
utility management decisions. As the ecosystem of such IoT applications that
leverage shared urban sensor streams continue to grow, applications will
perform duplicate pre-processing and analytics tasks. This offers the
opportunity to collaboratively reuse the outputs of overlapping dataflows,
thereby improving the resource efficiency. In this paper, we propose
\emph{dataflow reuse algorithms} that given a submitted dataflow, identifies
the intersection of reusable tasks and streams from a collection of running
dataflows to form a \emph{merged dataflow}. Similar algorithms to unmerge
dataflows when they are removed are also proposed. We implement these
algorithms for the popular Apache Storm DSPS, and validate their performance
and resource savings for 35 synthetic dataflows based on public OPMW workflows
with diverse arrival and departure distributions, and on 21 real IoT dataflows
from RIoTBench.Comment: To appear in IEEE eScience Conference 201
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
In this work we establish and investigate connections between causes for
query answers in databases, database repairs wrt. denial constraints, and
consistency-based diagnosis. The first two are relatively new research areas in
databases, and the third one is an established subject in knowledge
representation. We show how to obtain database repairs from causes, and the
other way around. Causality problems are formulated as diagnosis problems, and
the diagnoses provide causes and their responsibilities. The vast body of
research on database repairs can be applied to the newer problems of computing
actual causes for query answers and their responsibilities. These connections,
which are interesting per se, allow us, after a transition -inspired by
consistency-based diagnosis- to computational problems on hitting sets and
vertex covers in hypergraphs, to obtain several new algorithmic and complexity
results for database causality.Comment: To appear in Theory of Computing Systems. By invitation to special
issue with extended papers from ICDT 2015 (paper arXiv:1412.4311
The âRightâ Price for Art Collectibles. A Quantile Hedonic Regression Investigation of Picasso Paintings
Different art objects are likely to be priced by means of different systems of hedonic characteristics; more precisely, different evaluation procedures for high and low price items are often postulated. However, the empirical evidence on this point is scant. The main purpose of this paper is to fill this gap by using the quantile hedonic regression approach. The empirical evidence, based on a data set of 716 Picasso paintings sold at auction worldwide, highlights the critical role of the price classes in determining the evaluation criteria of art items.hedonic price; auction; quantile regression; painting; Picasso
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