Carpooling: travelers’ perceptions from a big data analysis

2018 ◽  
Vol 30 (5) ◽  
pp. 554-571 ◽  
Author(s):  
Maria Vincenza Ciasullo ◽  
Orlando Troisi ◽  
Francesca Loia ◽  
Gennaro Maione
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Web Crawler ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Research Findings ◽  
One Year

Purpose The purpose of this paper is to provide a better understanding of the reasons why people use or do not use carpooling. A further aim is to collect and analyze empirical evidence concerning the advantages and disadvantages of carpooling. Design/methodology/approach A large-scale text analytics study has been conducted: the collection of the peoples’ opinions have been realized on Twitter by means of a dedicated web crawler, named “Twitter4J.” After their mining, the collected data have been treated through a sentiment analysis realized by means of “SentiWordNet.” Findings The big data analysis identified the 12 most frequently used concepts about carpooling by Twitter’s users: seven advantages (economic efficiency, environmental efficiency, comfort, traffic, socialization, reliability, curiosity) and five disadvantages (lack of effectiveness, lack of flexibility, lack of privacy, danger, lack of trust). Research limitations/implications Although the sample is particularly large (10 percent of the data flow published on Twitter from all over the world in about one year), the automated collection of people’s comments has prevented a more in-depth analysis of users’ thoughts and opinions. Practical implications The research findings may direct entrepreneurs, managers and policy makers to understand the variables to be leveraged and the actions to be taken to take advantage of the potential benefits that carpooling offers. Originality/value The work has utilized skills from three different areas, i.e., business management, computing science and statistics, which have been synergistically integrated for customizing, implementing and using two IT tools capable of automatically identifying, selecting, collecting, categorizing and analyzing people’s tweets about carpooling.

scholarly journals Programming big data analysis: principles and solutions

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Loris Belcastro ◽  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Alessio Orsino ◽  
Domenico Talia ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Message Passing ◽  
Global Analysis ◽  
Big Data Analysis ◽  
Digital Data ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Social Media Platforms ◽  
Many Sources

AbstractIn the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

Understanding the role of big data analytics for coordination of electronic retail service supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatao Wang ◽  
Di Wu ◽  
Hongxin Yu ◽  
Huaxia Shen ◽  
Yuanjun Zhao
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Coordination Mechanism ◽  
Market Demand ◽  
Content Type ◽  
Service Supply Chain ◽  
Retail Service

PurposeBased on the typical service supply chain (SSC) structure, the authors construct the model of e-tailing SSC to explore the coordination relationship in the supply chain, and big data analysis provides realistic possibilities for the creation of coordination mechanisms.Design/methodology/approachAt the present stage, the e-commerce companies have not yet established a mature SSC system and have not achieved good synergy with other members of the supply chain, the shortage of goods and the greater pressure of express logistics companies coexist. In the case of uncertain online shopping market demand, the authors employ newsboy model, applied in the operations research, to analyze the synergistic mechanism of SSC model.FindingsBy analyzing the e-tailing SSC coordination mechanism and adjusting relevant parameters, the authors find that the synergy mechanism can be implemented and optimized. Through numerical example analysis, the authors confirmed the feasibility of the above analysis.Originality/valueBig data analysis provides a kind of reality for the establishment of online SSC coordination mechanism. The establishment of an online supply chain coordination mechanism can effectively promote the efficient allocation of supplies and better meet consumers' needs.

scholarly journals The study of the effect of online review on purchase behavior

2020 ◽  
Vol 4 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Jinghuan Zhang ◽  
Wenfeng Zheng ◽  
Shan Wang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Online Reviews ◽  
Big Data Analysis ◽  
Influential Factors ◽  
Purchase Behavior ◽  
Behavioral Experiment ◽  
Content Type ◽  
Online Purchase ◽  
Network Big Data

Purpose The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method. Design/methodology/approach This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior. Findings Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity. Originality/value Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

scholarly journals Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Nina Jeliazkova ◽  
Vladimir Chupakhin ◽  
Jose-Felipe Golib-Dzib ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Large Scale Dataset

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

scholarly journals A big-data analysis of political rhetoric relating the developments of the United States, China, and global powers

2020 ◽  
Vol 23 (3) ◽  
pp. 227-243
Author(s):  
Patrick Carter ◽  
Jeffrie Wang ◽  
Davis Chau
Keyword(s):  
United States ◽  
Big Data ◽  
Data Analysis ◽  
Political Rhetoric ◽  
The United States ◽  
Big Data Analysis ◽  
Content Type ◽  
World Power ◽  
The U.S ◽  
Over Time

PurposeThe similarities between the developments of the United States (U.S.) and China into global powers (countries with global economic, military, and political influence) can be analyzed through big data analysis from both countries. The purpose of this paper is to examine whether or not China is on the same path to becoming a world power like what the U.S. did one hundred years ago.Design/methodology/approachThe data of this study is drawn from political rhetoric and linguistic analysis by using “big data” technology to identify the most common words and political trends over time from speeches made by the U.S. and Chinese leaders from three periods, including 1905-1945 in U.S., 1977-2017 in U.S. and 1977-2017 in China.FindingsRhetoric relating to national identity was most common amongst Chinese and the U.S. leaders over time. The differences between the early-modern U.S. and the current U.S. showed the behavioral changes of countries as they become powerful. It is concluded that China is not a world power at this stage. Yet, it is currently on the path towards becoming one, and is already reflecting characteristics of present-day U.S., a current world power.Originality/valueThis paper presents a novel approach to analyze historical documents through big data text mining, a methodology scarcely used in historical studies. It highlights how China as of now is most likely in a transitionary stage of becoming a world power.

scholarly journals Big data theory based spectrum sensing algorithm for the satellite cognitive radio network

2021 ◽  
Author(s):  
Mingchuan Yang ◽  
Xinye Shao ◽  
Guanchang Xue ◽  
Bingyu Xie
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Large Scale ◽  
Radio Spectrum ◽  
Big Data Analysis ◽  
New Approach ◽  
Analysis Theory ◽  
Sample Covariance

AbstractIn order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed.

scholarly journals Machine Vision and Big Data-Driven Sports Athletes Action Training Intervention Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Jiang ◽  
Ping wang ◽  
Lei Peng ◽  
Xiaofeng Wang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Machine Vision ◽  
Big Data Analysis ◽  
Research Field ◽  
Visual Methods ◽  
Motion Sensors ◽  
Convolutional Network ◽  
Significant Information ◽  
Depth Analysis

In recent years, athlete action recognition has become an important research field for showing and recognition of athlete actions. Generally speaking, movement recognition of athletes can be performed through a variety of modes, such as motion sensors, machine vision, and big data analysis. Among them, machine vision and big data analysis usually contain significant information which can be used for various purposes. Machine vision can be expressed as the recognition of the time sequence of a series of athlete actions captured through camera, so that it can intervene in the training of athletes by visual methods and approaches. Big data contains a large number of athletes’ historical training and competition data which need exploration. In-depth analysis and feature mining of big data will help coach teams to develop training plans and devise new suggestions. On the basis of the above observations, this paper proposes a novel spatiotemporal attention map convolutional network to identify athletes’ actions, and through the auxiliary analysis of big data, gives reasonable action intervention suggestions, and provides coaches and decision-making teams to formulate scientific training programs. Results of the study show the effectiveness of the proposed research.

Big data analysis on the business process and management for the store layout and bundling sales

2019 ◽  
Vol 25 (7) ◽  
pp. 1783-1801 ◽  
Author(s):  
Shu-hsien Liao ◽  
Yi-Shan Tasi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Analysis Data ◽  
Database System ◽  
Big Data Analysis ◽  
Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

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