CHALLENGES IN PARALLEL GRAPH PROCESSING

2007 ◽  
Vol 17 (01) ◽  
pp. 5-20 ◽  
Author(s):  
ANDREW LUMSDAINE ◽  
DOUGLAS GREGOR ◽  
BRUCE HENDRICKSON ◽  
JONATHAN BERRY
Keyword(s):  
Data Mining ◽  
Research Agenda ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Graph Processing ◽  
Graph Problems ◽  
Current State ◽  
Software And Hardware ◽  
Parallel Graph

Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications are not necessarily effective for large-scale graph problems. In this paper we present the inter-relationships between graph problems, software, and parallel hardware in the current state of the art and discuss how those issues present inherent challenges in solving large-scale graph problems. The range of these challenges suggests a research agenda for the development of scalable high-performance software for graph problems.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Using plants to remediate or manage metal-polluted soils: an overview on the current state of phytotechnologies

2021 ◽  
Vol 43 ◽  
pp. e58283
Author(s):  
Clístenes Williams Araújo do Nascimento ◽  
Caroline Miranda Biondi ◽  
Fernando Bruno Vieira da Silva ◽  
Luiz Henrique Vieira Lima
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Cost Effective ◽  
Time Frame ◽  
Contaminated Site ◽  
Polluted Soils ◽  
Mining Sites ◽  
Current State ◽  
Remedial Actions ◽  
The Tropics

Soil contamination by metals threatens both the environment and human health and hence requires remedial actions. The conventional approach of removing polluted soils and replacing them with clean soils (excavation) is very costly for low-value sites and not feasible on a large scale. In this scenario, phytoremediation emerged as a promising cost-effective and environmentally-friendly technology to render metals less bioavailable (phytostabilization) or clean up metal-polluted soils (phytoextraction). Phytostabilization has demonstrable successes in mining sites and brownfields. On the other hand, phytoextraction still has few examples of successful applications. Either by using hyperaccumulating plants or high biomass plants induced to accumulate metals through chelator addition to the soil, major phytoextraction bottlenecks remain, mainly the extended time frame to remediation and lack of revenue from the land during the process. Due to these drawbacks, phytomanagement has been proposed to provide economic, environmental, and social benefits until the contaminated site returns to productive usage. Here, we review the evolution, promises, and limitations of these phytotechnologies. Despite the lack of commercial phytoextraction operations, there have been significant advances in understanding phytotechnologies' main constraints. Further investigation on new plant species, especially in the tropics, and soil amendments can potentially provide the basis to transform phytoextraction into an operational metal clean-up technology in the future. However, at the current state of the art, phytotechnology is moving the focus from remediation technologies to pollution attenuation and palliative cares.

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

scholarly journals Electrode Materials for High-Performance Sodium-Ion Batteries

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1952 ◽  
Author(s):  
Santanu Mukherjee ◽  
Shakir Bin Mujib ◽  
Davi Soares ◽  
Gurpreet Singh
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Electrode Materials ◽  
State Of The Art ◽  
Electronic Conductivity ◽  
Lithium Ion ◽  
Cycle Life ◽  
Sodium Ion ◽  
Sodium Ion Batteries ◽  
Grid Storage

Sodium ion batteries (SIBs) are being billed as an economical and environmental alternative to lithium ion batteries (LIBs), especially for medium and large-scale stationery and grid storage. However, SIBs suffer from lower capacities, energy density and cycle life performance. Therefore, in order to be more efficient and feasible, novel high-performance electrodes for SIBs need to be developed and researched. This review aims to provide an exhaustive discussion about the state-of-the-art in novel high-performance anodes and cathodes being currently analyzed, and the variety of advantages they demonstrate in various critically important parameters, such as electronic conductivity, structural stability, cycle life, and reversibility.

High-performance computing systems: Status and outlook

Acta Numerica ◽  
2012 ◽  
Vol 21 ◽  
pp. 379-474 ◽  
Author(s):  
J. J. Dongarra ◽  
A. J. van der Steen
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
State Of The Art ◽  
Computing Systems ◽  
Future Developments ◽  
Steady Growth ◽  
Current State ◽  
Near Future ◽  
Performance Computing ◽  
Shed Light

This article describes the current state of the art of high-performance computing systems, and attempts to shed light on near-future developments that might prolong the steady growth in speed of such systems, which has been one of their most remarkable characteristics. We review the different ways devised to speed them up, both with regard to components and their architecture. In addition, we discuss the requirements for software that can take advantage of existing and future architectures.

AN APPLICATION CONCEPT OF THE FIBRE-OPTICAL TECHNOLOGY IN GEODETIC MONITORING OF ENGINEERING OBJECTS

2017 ◽  
Vol 166 (46) ◽  
pp. 60-74
Author(s):  
Krzysztof Karsznia ◽  
Konrad Podawca
Keyword(s):  
Risk Management ◽  
Large Scale ◽  
State Of The Art ◽  
Monitoring Systems ◽  
Management Approach ◽  
Engineering Structures ◽  
Geoinformation Systems ◽  
Current State ◽  
Modern Engineering ◽  
Integrated Risk

Monitoring of structures and other different field objects undoubtedly belongs to the main issues of modern engineering. The use of technologies making it possible to implement structural monitoring makes it possible to build an integrated risk management approach combining instrumental solutions with geoinformation systems. In the studies of engineering structures, there is physical monitoring mainly used for examining the physical state of the object - so-called SHM ("Structural Health Monitoring"). However, very important role is also played by geodetic monitoring systems (GMS). The progress observed in the field of IT and automatics has opened new possibilities of using integrated systems on other, often large-scale objects. Based on the current state-of-the-art, the article presents the concept of integration approaches of physical and geodetic monitoring systems in order to develop useful guidelines for further construction of an expert risk management system.

Real Time Simulation for High Fidelity Multibody Dynamics and Mechatronic Systems

2014 ◽  
Author(s):  
William Prescott
Keyword(s):  
Real Time ◽  
Multibody Dynamics ◽  
Large Scale ◽  
State Of The Art ◽  
High Fidelity ◽  
Implicit Integration ◽  
Mechatronic Systems ◽  
Vehicle Simulation ◽  
Current State ◽  
Time Simulation

This paper will investigate the use of large scale multibody dynamics (MBD) models for real-time vehicle simulation. Current state of the art in the real-time solution of vehicle uses 15 degree of freedom models, but there is a need for higher-fidelity systems. To increase the fidelity of models uses this paper will propose the use of the following techniques: implicit integration, parallel processing and co-simulation in a real-time environment.

scholarly journals Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

2021 ◽  
Vol 13 (22) ◽  
pp. 4599
Author(s):  
Félix Quinton ◽  
Loic Landrieu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Rotation ◽  
Large Scale ◽  
State Of The Art ◽  
Crop Rotations ◽  
Learning Approach ◽  
Type Mapping ◽  
Current State ◽  
Crop Type

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

scholarly journals Corporate entrepreneurship: a systematic literature review and future research agenda

2022 ◽  
Author(s):  
David Urbano ◽  
Andreu Turro ◽  
Mike Wright ◽  
Shaker Zahra
Keyword(s):  
Research Agenda ◽  
State Of The Art ◽  
Corporate Entrepreneurship ◽  
Global Perspective ◽  
Future Research ◽  
Future Research Agenda ◽  
Current State ◽  
Literature Reviews ◽  
Antecedents And Consequences ◽  
State Of Research

AbstractThis article analyzes the state of the art of the research on corporate entrepreneurship, develops a conceptual framework that connects its antecedents and consequences, and offers an agenda for future research. We review 310 papers published in entrepreneurship and management journals, providing an assessment of the current state of research and, subsequently, we suggest research avenues in three different areas: corporate entrepreneurship antecedents, dimensions and consequences. Even though a significant part of the overall corporate entrepreneurship literature has appeared in the last decade, most literature reviews were published earlier. These reviews typically cover a single dimension of the corporate entrepreneurship phenomenon and, therefore, do not provide a global perspective on the existing literature. In addition, corporate entrepreneurship has been studied from different fields and there are different approaches and definitions to it. This limits our understanding of accumulated knowledge in this area and hampers the development of further research. Our review addresses these shortcomings, providing a roadmap for future research.

Parallel, Distributed, and Grid-Based Data Mining

2011 ◽  
pp. 90-119
Author(s):  
Moez Ben HajHmida ◽  
Antonio Congiusta
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Life Sciences ◽  
Business Organizations ◽  
Data Mining Techniques ◽  
Software And Hardware ◽  
Mining Methods ◽  
High Performance Computers ◽  
Grid Based ◽  
Major Data

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.

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