scholarly journals Firefly Algorithm based Map Reduce for Large-Scale Data Clustering

2019 ◽  
Vol 8 (9) ◽  
pp. 1505-1511
Keyword(s):  
Large Scale ◽  
Firefly Algorithm ◽  
Programming Model ◽  
Map Reduce ◽  
Technological Advancement ◽  
Digital World ◽  
Large Scale Data ◽  
Mapreduce Model

The technological advancement plays a major role in this era of digital world of growing data. Hence, there is a need to analyse the data so as to make good decisions. In the domain of data analytics, clustering is one of the significant tasks. The main difficulty in Map reduce is the clustering of massive amount of dataset. Within a computing cluster, Map Reduce associated with the algorithm such as parallel and distributed methods serve as a main programming model. In this work, Map Reduce-based Firefly algorithm known as MR-FF is projected for clustering the data. It is implemented using a MapReduce model within the Hadoop framework. It is used to enhance the task of clustering as a major role of reducing the sum of Euclidean distance among every instance of data and its belonging centroid of the cluster. The outcome of the experiment exhibits that the projected algorithm is better while dealing with gigantic data, and also outcome maintains the quality of clustering level

Study of Map-Reduce over Hadoop Based Cloud Computing Environment

2014 ◽  
Vol 509 ◽  
pp. 175-181
Author(s):  
Wu Min Pan ◽  
Li Bai Ha
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Large Scale ◽  
Programming Model ◽  
Sql Server ◽  
Map Reduce ◽  
Data Set ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

Popularity for the term Cloud-Computing has been increasing in recent years. In addition to the SQL technique, Map-Reduce, a programming model that realizes implementing large-scale data processing, has been a hot topic that is widely discussed through many studies. Many real-world tasks such as data processing for search engines can be parallel-implemented through a simple interface with two functions called Map and Reduce. We focus on comparing the performance of the Hadoop implementation of Map-Reduce with SQL Server through simulations. Hadoop can complete the same query faster than SQL Server. On the other hand, some concerned factors are also tested to see whether they would affect the performance for Hadoop or not. In fact more machines included for data processing can make Hadoop achieve a better performance, especially for a large-scale data set.

scholarly journals A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code

SLEEP ◽  
2020 ◽  
Author(s):  
Luca Menghini ◽  
Nicola Cellini ◽  
Aimee Goldstone ◽  
Fiona C Baker ◽  
Massimiliano de Zambotti
Keyword(s):  
Open Source ◽  
Validation Studies ◽  
Large Scale ◽  
Analytical Framework ◽  
Clinical Settings ◽  
Large Scale Data ◽  
Fast Pace ◽  
Epoch Analysis ◽  
Tracking Devices

Abstract Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

The Role of African Students in Technology Transfer

1979 ◽  
Vol 9 (4) ◽  
pp. 50-51
Author(s):  
Ephraim Nkwute Aniebona
Keyword(s):  
Human Society ◽  
Technological Advancement ◽  
African Students ◽  
Human Needs ◽  
Power Resources ◽  
Human Effort ◽  
Basic Human Needs ◽  
Developed Nations

The term, “technology,” as used here, refers basically to: (1) the science or art of devising tools and instruments and how to use them; (2) the development of new materials and substances and their application; (3) the development of machines to supplement or replace human effort, where desirable and feasible; (4) the development of energy and power resources for running the machines; and (5) the development of efficient methods of doing work—that is, using tools, machines, and instruments. From an observation of human efforts throughout the world, it is clear that every human society is concerned with technology, for it is a proven means by which man has extended his power beyond his physical capacity and gained some control over his environment. Although technology exists in every society, it is the amount and quality of the technology that separates nations today on a scale of economic development. Whilst the developing, technologically backward countries of Africa constantly face the basic human needs of food, shelter, and clothing, the developed nations consume and enjoy a disproportionate amount of the world’s resources and wealth by reason of their technological advancement.

The cultural dividend: a hidden source of economic growth in emerging countries

2017 ◽  
Vol 24 (4) ◽  
pp. 590-616 ◽  
Author(s):  
Shaomin Li ◽  
Seung Ho Park ◽  
David Duden Selover
Keyword(s):  
Economic Growth ◽  
Labor Productivity ◽  
Productivity Growth ◽  
Large Scale ◽  
Cultural Factors ◽  
Content Type ◽  
Economic Productivity ◽  
Large Scale Data ◽  
Productivity Gains

Purpose The purpose of this paper is to develop the theoretical linkage between culture and economic growth and empirically test the relationship by measuring culture and how it affects labor productivity. Design/methodology/approach This study uses a cross-section study of developing countries and regresses economic productivity growth on a set of control variables and cultural factors. Findings It is found that three cultural factors, economic attitudes, political attitudes, and attitudes towards the family, affect economic productivity growth. Originality/value Many economists ignore culture as a factor in economic growth, either because they discount the value of culture or because they have no simple way to quantify culture, resulting in the role of culture being under-researched. The study is the first to extensively examine the role of culture in productivity growth using large-scale data sources. The authors show that culture plays an important role in productivity gains across countries, contributing to the study of the effects of culture on economic development, and that culture can be empirically measured and linked to an activity that directly affects the economic growth – labor productivity.

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

Vertical Integration Between Providers With Possible Cloud Migration

2019 ◽  
pp. 274-284
Author(s):  
Aleksandra Kostic-Ljubisavljevic ◽  
Branka Mikavica
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Wholesale Price ◽  
Rejection Rate ◽  
Large Scale Data ◽  
Cloud Migration ◽  
Computational Resources ◽  
Charging Strategy ◽  
Scale Data

All vertically integrated participants in content provisioning process are influenced by bandwidth requirements. Provisioning of self-owned resources that satisfy peak bandwidth demand leads to network underutilization and it is cost ineffective. Under-provisioning leads to rejection of customers' requests. Vertically integrated providers need to consider cloud migration in order to minimize costs and improve quality of service and quality of experience of their customers. Cloud providers maintain large-scale data centers to offer storage and computational resources in the form of virtual machines instances. They offer different pricing plans: reservation, on-demand, and spot pricing. For obtaining optimal integration charging strategy, revenue sharing, cost sharing, wholesale price is applied frequently. The vertically integrated content provider's incentives for cloud migration can induce significant complexity in integration contracts, and consequently improvements in costs and requests' rejection rate.

Vertical Integration Between Providers With Possible Cloud Migration

2018 ◽  
pp. 1164-1173 ◽  
Author(s):  
Aleksandra Kostic-Ljubisavljevic ◽  
Branka Mikavica
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Wholesale Price ◽  
Rejection Rate ◽  
Large Scale Data ◽  
Cloud Migration ◽  
Computational Resources ◽  
Data Centres ◽  
Charging Strategy

All vertically integrated participants in content provisioning process are influenced by bandwidth requirements. Provisioning of self-owned resources that satisfy peak bandwidth demand leads to network underutilization and it is cost ineffective. Under-provisioning leads to rejection of customers' requests. Vertically integrated providers need to consider cloud migration in order to minimize costs and improve Quality of Service and Quality of Experience of their customers. Cloud providers maintain large-scale data centres to offer storage and computational resources in the form of Virtual Machines instances. They offer different pricing plans: reservation, on-demand and spot pricing. For obtaining optimal integration charging strategy, Revenue Sharing, Cost Sharing, Wholesale Price is applied frequently. The vertically integrated content provider's incentives for cloud migration can induce significant complexity in integration contracts, and consequently improvements in costs and requests' rejection rate.

Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

2014 ◽  
Vol 5 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Shiho Kitajima ◽  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Quality Of Life ◽  
Natural Language ◽  
Large Scale ◽  
Medical Information ◽  
Drug Effects ◽  
Large Scale Data ◽  
Medical Documents ◽  
Baseline System ◽  
Scale Data

Obtaining medical information has a beneficial influence on patients' treatment and QOL (quality of life). The authors aim to make a system that helps patients to collect narrative information. Extracting information from data written by patients will allow the acquisition of information which is easy to understand and provides encouragement. Additionally, by using large-scale data, the system can be utilized for discovering unknown effects or patterns. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions from illness survival blogs' snippets. This paper proposes a method to extract the triplets using specific clue words and parsing the results in order to extract from blogs written in free natural language. Moreover, recall was improved by combining their proposed method and a baseline system, and precision was improved by filtering using dictionaries we created from existing medical documents.

scholarly journals How ‘Networked’ are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the ‘Networkedness’ of Collaboratively Constructed Content

2020 ◽  
Vol 10 (10) ◽  
pp. 267
Author(s):  
Noa Sher ◽  
Carmel Kent ◽  
Sheizaf Rafaeli
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Concept Maps ◽  
Quality Measures ◽  
Thought Process ◽  
Academic Settings ◽  
New Challenges ◽  
Complex Phenomena

With the growing role of online multi-participant collaborations in shaping the academic, professional, and civic spheres, incorporating collaborative online practices in educational settings has become imperative. As more educators include such practices in their curricula, they are faced with new challenges. Assessment of collaborations, especially in larger groups, is particularly challenging. Assessing the quality of the collaborative “thought process” and its product is essential for both pedagogical and evaluative purposes. While traditional quantitative quality measures were designed for individual work or the aggregated work of individuals, capturing the complexity and the integrative nature of high-quality collaborative learning requires novel methodologies. Network analysis provides methods and tools that can identify, describe, and quantify non-linear and complex phenomena. This paper applies network analysis to the content created by students through large-scale online collaborative concept-mapping and explores how these can be applied for the assessment of the quality of a collective product. Quantitative network structure measures are introduced for this purpose. The application and the affordances of these metrics are demonstrated on data from six large-group online collaborative discussions from academic settings. The metrics presented here address the organization and the integration of the content and enable a comparison of collaborative discussions.

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