scholarly journals Special Issue on Programming Languages for Big Data Editorial

2018 ◽  
Vol 28 ◽  
Author(s):  
James Cheney ◽  
Torsten Grust
Keyword(s):  
Big Data ◽  
Programming Languages ◽  
High Performance ◽  
Database System ◽  
Research Area ◽  
Distributed Programming ◽  
Special Issue ◽  
Creative Research ◽  
Database Programming Languages ◽  
Advanced Applications

Ideas from programming languages play an important role in a range of advanced applications of databases, in database system implementation, distributed programming (MapReduce), streaming computation, and high-performance (GPU/multicore) computation. This creative research area is broadening into a subfield of data-centric computation. Although the interaction of databases and programming has a long history (the 16th biennial Database Programming Languages symposium was held in 2017), there has been a recent renewal of interest and broadening of programming language techniques for dealing with data from several quarters in the last few years, including workshops at Microsoft Research (RADICAL 2010), ICFP (XLDI 2012), POPL (DDFP 2013, DCM 2014) and a Dagstuhl Seminar on Programming Languages for Big Data (December 2014). This special issue recognises and encourages the publication of mature research contributions in this area.

scholarly journals Special issue on Programming with Dependent Types Editorial

2017 ◽  
Vol 27 ◽  
Author(s):  
WOUTER SWIERSTRA ◽  
PETER DYBJER
Keyword(s):  
Programming Languages ◽  
Functional Programming ◽  
Wide Spectrum ◽  
Research Area ◽  
Dependent Types ◽  
Proof Assistants ◽  
Special Issue ◽  
Functional Programming Languages ◽  
The Past

There has been sustained interest in functional programming languages with dependent types in recent years. The foundations of dependently typed programming can be traced back to Martin–Löf's work in the 1970s. In the past decades, this vision has given rise to the development of proof assistants and functional programming languages based on dependent types. The increased popularity of systems such as Agda, Coq, Idris, and many others, reflects the growing momentum in this research area. After sending out our first call for papers in October 2015, we are happy to accept six articles in this special issue covering a wide spectrum of topics.

scholarly journals Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Haruna Chiroma ◽  
Shafi’i M. Abdulhamid ◽  
Ibrahim A. T. Hashem ◽  
Kayode S. Adewole ◽  
Absalom E. Ezugwu ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
High Performance ◽  
Theoretical Perspective ◽  
Big Data Analytics ◽  
Research Area ◽  
Future Research ◽  
Internet Of Vehicles ◽  
Active Attention

The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in the IoV within the context of big data analytics (BDA) are scarce. In this paper, we present a survey and explore the theoretical perspective of the role of DL in the IoV within the context of BDA. The study has unveiled substantial research opportunities that cut across DL, IoV, and BDA. Exploring DL in the IoV within BDA is an infant research area requiring active attention from researchers to fully understand the emerging concept. The survey proposes a model of IoV environment integrated into the cloud equipped with a high-performance computing server, DL architecture, and Apache Spark for data analytics. The current developments, challenges, and opportunities for future research are presented. This study can guide expert and novice researchers on further development of the application of DL in the IoV within the context of BDA.

Piezoelectric β-polymorph formation of new textiles by surface modification with coating process based on interfacial interaction on the conformational variation of poly (vinylidene fluoride) (PVDF) chains

2020 ◽  
Vol 91 (3) ◽  
pp. 31301
Author(s):  
Nabil Chakhchaoui ◽  
Rida Farhan ◽  
Meriem Boutaldat ◽  
Marwane Rouway ◽  
Adil Eddiai ◽  
...  
Keyword(s):  
High Efficiency ◽  
Vinylidene Fluoride ◽  
Research Work ◽  
Research Area ◽  
Interfacial Interaction ◽  
Tetraethyl Orthosilicate ◽  
Poly Vinylidene Fluoride ◽  
Carbon Nanofillers ◽  
The Impact ◽  
Advanced Applications

Novel textiles have received a lot of attention from researchers in the last decade due to some of their unique features. The introduction of intelligent materials into textile structures offers an opportunity to develop multifunctional textiles, such as sensing, reacting, conducting electricity and performing energy conversion operations. In this research work nanocomposite-based highly piezoelectric and electroactive β-phase new textile has been developed using the pad-dry-cure method. The deposition of poly (vinylidene fluoride) (PVDF) − carbon nanofillers (CNF) − tetraethyl orthosilicate (TEOS), Si(OCH2CH3)4 was acquired on a treated textile substrate using coating technique followed by evaporation to transform the passive (non-functional) textile into a dynamic textile with an enhanced piezoelectric β-phase. The aim of the study is the investigation of the impact the coating of textile via piezoelectric nanocomposites based PVDF-CNF (by optimizing piezoelectric crystalline phase). The chemical composition of CT/PVDF-CNC-TEOS textile was detected by qualitative elemental analysis (SEM/EDX). The added of 0.5% of CNF during the process provides material textiles with a piezoelectric β-phase of up to 50% has been measured by FTIR experiments. These results indicated that CNF has high efficiency in transforming the phase α introduced in the unloaded PVDF, to the β-phase in the case of nanocomposites. Consequently, this fabricated new textile exhibits glorious piezoelectric β-phase even with relatively low coating content of PVDF-CNF-TEOS. The study demonstrates that the pad-dry-cure method can potentially be used for the development of piezoelectric nanocomposite-coated wearable new textiles for sensors and energy harvesting applications. We believe that our study may inspire the research area for future advanced applications.

Guest Editorial: Special Issue on Big Data

2020 ◽  
pp. -1--1
Author(s):  
Arun Sangaiah ◽  
Ford Gao ◽  
Krishn Mishra
Keyword(s):  
Big Data ◽  
Guest Editorial ◽  
Special Issue ◽  
Editorial Special Issue

Call for Special Issue Papers: Multimedia Big Data Analytics for Engineering Education

Big Data ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 87-88
Author(s):  
Priyan Malarvizhi Kumar ◽  
Hari Mohan Pandey ◽  
Gautam Srivastava
Keyword(s):  
Big Data ◽  
Engineering Education ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Special Issue ◽  
Multimedia Big Data

Special Issue on Software and Systems Reuse in the Big Data Era

2021 ◽  
Vol 176 ◽  
pp. 110921
Author(s):  
Apostolos Ampatzoglou ◽  
Peng Xin
Keyword(s):  
Big Data ◽  
Special Issue

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

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