3,768 research outputs found
Benchmarking Blocking Algorithms for Web Entities
An increasing number of entities are described by interlinked data rather
than documents on the Web. Entity Resolution (ER) aims to identify descriptions
of the same real-world entity within one or across knowledge bases in the Web
of data. To reduce the required number of pairwise comparisons among
descriptions, ER methods typically perform a pre-processing step, called
\emph{blocking}, which places similar entity descriptions into blocks and thus
only compare descriptions within the same block. We experimentally evaluate
several blocking methods proposed for the Web of data using real datasets,
whose characteristics significantly impact their effectiveness and efficiency.
The proposed experimental evaluation framework allows us to better understand
the characteristics of the missed matching entity descriptions and contrast
them with ground truth obtained from different kinds of relatedness links.Comment: accepted at IEEE Transactions on Big Data journa
Benchmarking SciDB Data Import on HPC Systems
SciDB is a scalable, computational database management system that uses an
array model for data storage. The array data model of SciDB makes it ideally
suited for storing and managing large amounts of imaging data. SciDB is
designed to support advanced analytics in database, thus reducing the need for
extracting data for analysis. It is designed to be massively parallel and can
run on commodity hardware in a high performance computing (HPC) environment. In
this paper, we present the performance of SciDB using simulated image data. The
Dynamic Distributed Dimensional Data Model (D4M) software is used to implement
the benchmark on a cluster running the MIT SuperCloud software stack. A peak
performance of 2.2M database inserts per second was achieved on a single node
of this system. We also show that SciDB and the D4M toolbox provide more
efficient ways to access random sub-volumes of massive datasets compared to the
traditional approaches of reading volumetric data from individual files. This
work describes the D4M and SciDB tools we developed and presents the initial
performance results. This performance was achieved by using parallel inserts, a
in-database merging of arrays as well as supercomputing techniques, such as
distributed arrays and single-program-multiple-data programming.Comment: 5 pages, 4 figures, IEEE High Performance Extreme Computing (HPEC)
2016, best paper finalis
CHORUS Deliverable 3.3: Vision Document - Intermediate version
The goal of the CHORUS vision document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area (in line with the mandate of CHORUS as a Coordination Action).
This current intermediate draft of the CHORUS vision document (D3.3) is based on the previous CHORUS vision documents D3.1 to D3.2 and on the results of the six CHORUS Think-Tank meetings held in March, September and November 2007 as well as in April, July and October 2008, and on the feedback from other CHORUS events.
The outcome of the six Think-Thank meetings will not just be to the benefit of the participants which are stakeholders and experts from academia and industry – CHORUS, as a coordination action of the EC, will feed back the findings (see Summary) to the projects under its purview and, via its website, to the whole community working in the domain of AV content search.
A few subjections of this deliverable are to be completed after the eights (and presumably last) Think-Tank meeting in spring 2009
Towards an MPI-like Framework for Azure Cloud Platform
Message passing interface (MPI) has been widely used for implementing parallel and distributed applications. The emergence of cloud computing offers a scalable, fault-tolerant, on-demand al-ternative to traditional on-premise clusters. In this thesis, we investigate the possibility of adopt-ing the cloud platform as an alternative to conventional MPI-based solutions. We show that cloud platform can exhibit competitive performance and benefit the users of this platform with its fault-tolerant architecture and on-demand access for a robust solution. Extensive research is done to identify the difficulties of designing and implementing an MPI-like framework for Azure cloud platform. We present the details of the key components required for implementing such a framework along with our experimental results for benchmarking multiple basic operations of MPI standard implemented in the cloud and its practical application in solving well-known large-scale algorithmic problems
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A distributed analysis and monitoring framework for the compact Muon solenoid experiment and a pedestrian simulation
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The design of a parallel and distributed computing system is a very complicated task. It requires a detailed understanding of the design issues and of the theoretical and practical aspects of their solutions. Firstly, this thesis discusses in detail the major concepts and components required to make parallel and distributed computing a reality. A multithreaded and distributed framework capable of analysing the simulation data produced by a pedestrian simulation software was developed. Secondly, this thesis discusses the origins and fundamentals of Grid computing and the motivations for its use in High Energy Physics. Access to the data produced by the Large Hadron Collider (LHC) has to be provided for more than five thousand scientists all over the world. Users who run analysis jobs on the Grid do not necessarily have expertise in Grid computing. Simple, userfriendly and reliable monitoring of the analysis jobs is one of the key components of the operations of the distributed analysis; reliable monitoring is one of the crucial components of the Worldwide LHC Computing Grid for providing the functionality and performance that is required by the LHC experiments. The CMS Dashboard Task Monitoring and the CMS Dashboard Job Summary monitoring applications were developed to serve the needs of the CMS community
Inefficiencies in Digital Advertising Markets
Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research
XFlow: Benchmarking Flow Behaviors over Graphs
The occurrence of diffusion on a graph is a prevalent and significant
phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart
grid failures, and similar events. Comprehending the behaviors of flow is a
formidable task, due to the intricate interplay between the distribution of
seeds that initiate flow propagation, the propagation model, and the topology
of the graph. The study of networks encompasses a diverse range of academic
disciplines, including mathematics, physics, social science, and computer
science. This interdisciplinary nature of network research is characterized by
a high degree of specialization and compartmentalization, and the cooperation
facilitated by them is inadequate. From a machine learning standpoint, there is
a deficiency in a cohesive platform for assessing algorithms across various
domains. One of the primary obstacles to current research in this field is the
absence of a comprehensive curated benchmark suite to study the flow behaviors
under network scenarios.
To address this disparity, we propose the implementation of a novel benchmark
suite that encompasses a variety of tasks, baseline models, graph datasets, and
evaluation tools. In addition, we present a comprehensive analytical framework
that offers a generalized approach to numerous flow-related tasks across
diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes
of our empirical investigation, we analyze the advantages and disadvantages of
current foundational models, and we underscore potential avenues for further
study. The datasets, code, and baseline models have been made available for the
public at: https://github.com/XGraphing/XFlo
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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