131,641 research outputs found
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Monitoring of gas emissions at landfill sites using autonomous gas sensors
Executive Summary
This report details the work carried out during the Smart
Plant project (2005-AIC-MS-43-M4). As part of this
research, an autonomous platform for monitoring
greenhouse gases (methane (CH4), carbon dioxide
(CO2)) has been developed, prototyped and field
validated. The modular design employed means that the
platform can be readily adapted for a variety of
applications involving these and other target gases such
as hydrogen sulfide (H2S), ammonia (NH3) and carbon
monoxide (CO) and the authors are in the process of
completing several short demonstrator projects to
illustrate the potential of the platform for some of these
applications. The field validation for the greenhouse gas
monitoring platform was carried out at two landfill sites in
Ireland. The unit was used to monitor the concentration of
CO2 and CH4 gas at perimeter borehole wells. The final
prototype was deployed for over 4 months and
successfully extracted samples from the assigned
perimeter borehole well headspace, measured them and
sent the data to a database via a global system for mobile
(GSM) communications. The data were represented via
an updating graph in a web interface. Sampling was
carried out twice per day, giving a 60-fold increase on
current monitoring procedures which provide one gas
concentration measurement per month.
From additional work described in this report, a
number of conclusions were drawn regarding lateral
landfill gas migration on a landfill site and the
management of this migration to the site’s perimeter.
To provide frequent, reliable monitoring of landfill gas
migration to perimeter borehole wells, the unit needs
to:
• Be fully autonomous;
• Be capable of extracting a gas sample from a
borehole well independently of personnel;
• Be able to relay the data in near real time to a base
station; and
• Have sensors with a range capable of adequately
monitoring gas events accurately at all times.
The authors believe that a unit capable of such
monitoring has been developed and validated. This
unit provides a powerful tool for effective management
of landfill site gases. The effectiveness of this unit has
been recognised by the site management team at the
long-term deployment trial site, and the data gathered
have been used to improve the day-to-day operations
and gas management system on-site.
The authors make the following recommendations:
1. The dynamics of the landfill gas management
system cannot be captured by taking
measurements once per month; thus, a minimum
sampling rate of once per day is advised.
2. The sampling protocol should be changed:
(i) Borehole well samples should not be taken
from the top of the well but should be
extracted at a depth within the headspace
(0.5–1.0 m). The measurement depth will be
dependent on the water table and headspace
depth within the borehole well.
(ii) The sampling time should be increased to 3
min to obtain a steady-state measurement
from the headspace and to take a
representative sample; and
(iii) For continuous monitoring on-site, the
extracted sample should be recycled back
into the borehole well. However, for
compliance monitoring, the sample should
not be returned to the borehole well.
3. Devices should be placed at all borehole wells so
the balance on the site can be maintained through
the gas management system and extraction
issues can be quickly recognised and addressed
before there are events of high gas migration to
the perimeter.
4. A pilot study should be carried out by the EPA
using 10 of these autonomous devices over three
to five sites to show the need and value for this
type of sampling on Irish landfill sites
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
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