◾ Barriers to the Adoption of Big Data Applications in the Social Sector

Big Data ◽  
2015 ◽  
pp. 462-475
Keyword(s):  
Big Data ◽  
Social Sector ◽  
Big Data Applications ◽  
The Social

scholarly journals IMPLEMENTASI SOCIETY 5.0 DALAM KEBIJAKAN DAN STRATEGI PENDIDIKAN PADA PANDEMI COVID-19

2022 ◽  
Vol 2 (1) ◽  
pp. 34-43
Author(s):  
ADITYA ZULMI RAHMAWAN ◽  
ZAENURIYAH EFFENDI
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Learning Process ◽  
Research Method ◽  
Social Sector ◽  
Scientific Journals ◽  
Artificial Intelligent ◽  
The Social ◽  
The Impact ◽  
The Internet Of Things

The COVID-19 pandemic poses problems in various sectors. The most vulnerable sector in this situation is the social sector, especially education. Problems such as the learning process make the continuity of education a concern. This is a challenge for the community in the era of society 5.0 in the hope of overcoming the problems that arise due to the Covid-19 pandemic. The use of big data, artificial intelligence, and the internet of things is an alternative effort to help deal with the impact of the pandemic in accordance with the conditions in this disruptive era. This study aims to determine the policies and strategies of society 5.0 in the learning process as an effort to handle the impact of the pandemic. This study uses a systematic review research method of literature published by scientific journals in the period January 2010 to December 2021. The data used comes from published journals related to the topics studied and from various electronic media. The results of the study can find out strategies in the learning process in the implementation of society 5.0 in policies in the field of education as an effort to deal with the impact of the covid-19 pandemic. ABSTRAKPandemi covid-19 memberikan permasalahan di berbagai sektor. Sektor yang paling rentan dalam situasi ini adalah sektor sosial terutama pada pendidikan. Permasalahan seperti proses pembelajaran membuat keberlangsungan pendidikan menuai kekhawatiran. Hal ini menjadi sebuah tantangan bagi masyarakat di era society 5.0 dengan harapan dapat mengatasi permasalahan yang timbul akibat pandemi Covid-19. Pemanfaatan big data, artificial intelligent, dan internet of things menjadi upaya alternatif dalam membantu menangani dampak pandemi yang sesuai dengan keadaan di era disruptif ini. Penelitian ini bertujuan untuk mengetahui kebijakan dan strategi society 5.0 dalam proses pembelajaran sebagai upaya penanganan dampak pandemi. Penelitian ini menggunakan metode penelitian tinjauan sistematis terhadap literatur yang diterbitkan oleh jurnal ilmiah pada periode Januari tahun 2010 hingga Desember 2021. Sumber yang digunakan berasal dari jurnal-jurnal yang sudah dipublikasikan terkait dengan topik yang dikaji dan dari berbagai media elektronik. Hasil penelitian dapat mengetahui strategi dalam proses pembelajaran dalam implementasi society 5.0 pada kebijakan di bidang pendidikan sebagai upaya menghadapi dampak pandemi covid-19.

scholarly journals Power Budgeting of Big Data Applications in Container-based Clusters

2020 ◽  
Author(s):  
Jonatan Enes ◽  
Guillaume Fieni ◽  
Roberto R. Exposito ◽  
Romain Rouvoy ◽  
Juan Tourino
Keyword(s):  
Big Data ◽  
Big Data Applications

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals Computational storage: an efficient and scalable platform for big data and HPC applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mahdi Torabzadehkashi ◽  
Siavash Rezaei ◽  
Ali HeydariGorji ◽  
Hosein Bobarshad ◽  
Vladimir Alves ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance ◽  
Distributed Processing ◽  
Data Access ◽  
Distributed Applications ◽  
Process Data ◽  
Storage Devices ◽  
Hadoop Mapreduce ◽  
Big Data Applications ◽  
Application Processor

AbstractIn the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.

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