27,362 research outputs found
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
The OU Linked Open Data: production and consumption
The aim of this paper is to introduce the current efforts toward the release and exploitation of The Open University's (OU) Linked Open Data (LOD). We introduce the work that has been done within the LUCERO project in order to select, extract and structure subsets of information contained within the OU data sources and migrate and expose this information as part of the LOD cloud. To show the potential of such exposure we also introduce three different prototypes that exploit this new educational resource: (1) the OU expert search system, a tool focused on fnding the best experts for a certain topic within the OU staff; (2) the Buddy Study system, a tool that relies on Facebook information to identify common interest among friends and recommend potential courses within the OU that `buddies' can study together, and; (3) Linked OpenLearn, an application that enables exploring linked courses, Podcasts and tags to OpenLearn units. Its aim is to enhance the browsing experience for students, by detecting relevant educational resources on fly while reading an OpenLearn unit
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
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