205,100 research outputs found
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
Inference of epidemiological parameters from household stratified data
We consider a continuous-time Markov chain model of SIR disease dynamics with
two levels of mixing. For this so-called stochastic households model, we
provide two methods for inferring the model parameters---governing
within-household transmission, recovery, and between-household
transmission---from data of the day upon which each individual became
infectious and the household in which each infection occurred, as would be
available from first few hundred studies. Each method is a form of Bayesian
Markov Chain Monte Carlo that allows us to calculate a joint posterior
distribution for all parameters and hence the household reproduction number and
the early growth rate of the epidemic. The first method performs exact Bayesian
inference using a standard data-augmentation approach; the second performs
approximate Bayesian inference based on a likelihood approximation derived from
branching processes. These methods are compared for computational efficiency
and posteriors from each are compared. The branching process is shown to be an
excellent approximation and remains computationally efficient as the amount of
data is increased
Rethinking reliability for long-delay networks
Delay Tolerant Networking (DTN) is currently an open research area following the interest of space companies in the deployment of Internet protocols for the space Internet. Thus, these last years have seen an increase in the number of DTN protocol proposals such as Saratoga or LTP-T. However, the goal of these protocols are more to send much error-free data during a short contact time rather than operating to a strictly speaking reliable data transfer. Beside this, several research work have proposed efficient acknowledgment schemes based on the SNACK mechanism. However, these acknowledgement strategies are not compliant with the DTN protocol principle. In this paper, we propose a novel reliability mechanism with an implicit acknowledgment strategy that could be used either within these new DTN proposals or in the context of multicast transport protocols. This proposal is based on a new erasure coding concept specifically designed to operate efficient reliable transfer over bi-directional links
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