Trends, goals and outcomes for children and families using early powered mobility in a charitable loan scheme

2017 ◽  
Vol 11 (4) ◽  
pp. 138-147 ◽  
Author(s):  
Nina Evans ◽  
Rae Baines
Keyword(s):  
Young Children ◽  
Large Data ◽  
Early Years ◽  
Free Text ◽  
Research Knowledge ◽  
Data Set ◽  
Content Type ◽  
Large Data Set ◽  
Review Form ◽  
Outcomes For Children

Purpose The purpose of this paper is to explore a large data set compiled by a UK charity loan scheme to identify trends and paint a practice-based picture of how young children use early years powered mobility (EYPM). Design/methodology/approach Statistical analysis was used to investigate a database of 90 children, ranging in age from 15 to 72 months who completed use of an EYPM device (the Wizzybug, or WB) between April 2011 and December 2015. Goals were set and reviewed, and thematic analysis was used to understand families’ insights into their children’s use of EYPM, using a free-text review form. Findings Children’s mean age when joining this free loan scheme was 39.6 months. The later the child started using a Wizzybug, the less likely they were to achieve their goals. A theme of happiness and enjoyment emerged as important for both child and family. The child’s independence translated to independence for the whole family. Research limitations/implications The database was operational and incomplete. Lack of a standardised outcome measure was disadvantageous. Practical implications Challenges of translating research knowledge into practice are highlighted, supporting the need for more rigorous and standardised outcome measures. Earlier identification of children’s readiness for EYPM is required alongside research and recognition of the holistic benefits of EYPM for all the family. Originality/value This research profited from a large data set of young children with long-term access to powered mobility at home.

Collecting and evaluating large volumes of bibliographic metadata aggregated in the WorldCat database: a proposed methodology to overcome challenges

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vyacheslav I. Zavalin ◽  
Shawne D. Miksa
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Large Data ◽  
Study Data ◽  
Lessons Learned ◽  
Future Research ◽  
Data Set ◽  
Content Type ◽  
Large Data Set ◽  
Smart Data

Purpose This paper aims to discuss the challenges encountered in collecting, cleaning and analyzing the large data set of bibliographic metadata records in machine-readable cataloging [MARC 21] format. Possible solutions are presented. Design/methodology/approach This mixed method study relied on content analysis and social network analysis. The study examined subject representation in MARC 21 metadata records created in 2020 in WorldCat – the largest international database of “big smart data.” The methodological challenges that were encountered and solutions are examined. Findings In this general review paper with a focus on methodological issues, the discussion of challenges is followed by a discussion of solutions developed and tested as part of this study. Data collection, processing, analysis and visualization are addressed separately. Lessons learned and conclusions related to challenges and solutions for the design of a large-scale study evaluating MARC 21 bibliographic metadata from WorldCat are given. Overall recommendations for the design and implementation of future research are suggested. Originality/value There are no previous publications that address the challenges and solutions of data collection and analysis of WorldCat’s “big smart data” in the form of MARC 21 data. This is the first study to use a large data set to systematically examine MARC 21 library metadata records created after the most recent addition of new fields and subfields to MARC 21 Bibliographic Format standard in 2019 based on resource description and access rules. It is also the first to focus its analyzes on the networks formed by subject terms shared by MARC 21 bibliographic records in a data set extracted from a heterogeneous centralized database WorldCat.

scholarly journals Evaluating a guest satisfaction model through data mining

2019 ◽  
Vol 32 (4) ◽  
pp. 1523-1538 ◽  
Author(s):  
Sérgio Moro ◽  
Joaquim Esmerado ◽  
Pedro Ramos ◽  
Bráulio Alturas
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Online Reviews ◽  
Theory And Practice ◽  
Target Feature ◽  
Data Set ◽  
Content Type ◽  
Large Data Set ◽  
Guest Satisfaction ◽  
Satisfaction Model

Purpose This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

Does service failure criticality affect global travellers' service evaluations? An empirical analysis of online reviews

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rishi Dwesar ◽  
Debajani Sahoo
Keyword(s):  
Airline Industry ◽  
Service Failure ◽  
Service Recovery ◽  
Large Data ◽  
Online Reviews ◽  
Emotional States ◽  
Data Set ◽  
Content Type ◽  
Service Failures ◽  
Large Data Set

PurposeIncreased global air travel and competition in the airline industry entail better service delivery and failure management. This study examines how airline type, failure criticality and the traveller's culture influence travellers' airline evaluations of service failure.Design/methodology/approachThe study uses a large data set of customers' online reviews and incorporates quantitative and qualitative feedback from 20 major airlines across the world. Semantic tagging, sentiment and multivariate analyses have been used to analyse the data.FindingsFailure criticality and travellers' cultural backgrounds significantly affect airline evaluations after service failures. Moreover, failure criticality influences evaluations of travellers from individualistic cultures more severely. Contrary to expectations, full-service airlines were evaluated positively after less critical service failures.Practical implicationsThe findings support that customers undergo different emotional states when they experience service failure. Understanding these internal emotional sensitivities and how services would be judged by travellers across cultures can help airlines to better manage their service recovery efforts and to strategise prioritisation of scarce resources.Originality/valueThough airline service failure has been well researched, this study examines the role of culture in service failure evaluations. The study uses a novel method to analyse a large data set of both quantitative and qualitative traveller feedback useful in service recovery management.

scholarly journals Environmental Determinants of Vibrio parahaemolyticus in the Chesapeake Bay

2017 ◽  
Vol 83 (21) ◽  
Author(s):  
Benjamin J. K. Davis ◽  
John M. Jacobs ◽  
Meghan F. Davis ◽  
Kellogg J. Schwab ◽  
Angelo DePaola ◽  
...  
Keyword(s):  
Chesapeake Bay ◽  
Water Samples ◽  
Vibrio Parahaemolyticus ◽  
High Salinity ◽  
Environmental Parameters ◽  
Large Data ◽  
Environmental Determinants ◽  
Data Set ◽  
Content Type ◽  
Large Data Set

ABSTRACT Vibrio parahaemolyticus naturally occurs in brackish and marine waters and is one of the leading causes of seafood-borne illness. Previous work studying the ecology of V. parahaemolyticus has often been limited in geographic extent and lacked a full range of environmental measures. This study used a unique large data set of surface water samples in the Chesapeake Bay (n = 1,385) collected from 148 monitoring stations from 2007 to 2010. Water was analyzed for more than 20 environmental parameters, with additional meteorological and surrounding land use data. The V. parahaemolyticus-specific genetic markers thermolabile hemolysin (tlh), thermostable direct hemolysin (tdh), and tdh-related hemolysin (trh) were assayed using quantitative PCR (qPCR), and interval-censored regression models with nonlinear effects were estimated to account for limits of detection and quantitation. tlh was detected in 19.6% of water samples; tdh or trh markers were not detected. The results confirmed previously reported positive associations for V. parahaemolyticus abundance with temperature and turbidity and negative associations with high salinity (>10 to 23‰). Furthermore, the salinity relationship was determined to be a function of both low temperature and turbidity, with an increase of either nullifying the high salinity effect. Associations with dissolved oxygen and phosphate also appeared stronger when samples were taken near human developments. A renewed focus on the V. parahaemolyticus ecological paradigm is warranted to protect public health. IMPORTANCE Vibrio parahaemolyticus is one of the leading causes of seafood-borne illness in the United States and across the globe. Exposure is often through consuming raw or undercooked shellfish. Given the natural presence of the bacterium in the marine environment, an improved understanding of its environmental determinants is necessary for future preventative measures. This analysis of environmental Vibrio parahaemolyticus is one of only a few that utilize a large data set measured over a wide geographic and temporal range. The analysis also includes a large number of environmental parameters for Vibrio modeling, many of which have previously only been tested sporadically, and some of which have not been considered before. The results of the analysis revealed previously unknown relationships between salinity, turbidity, and temperature that provide significant insight into the abundance and persistence of V. parahaemolyticus bacterium in the environment. This information will be essential for developing environmental forecast models for the bacterium.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

User password repetitive patterns analysis and visualization

2016 ◽  
Vol 24 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Xiaoying Yu ◽  
Qi Liao
Keyword(s):  
Security Policy ◽  
Large Data ◽  
Efficient Algorithms ◽  
Privacy And Security ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Word Cloud ◽  
Individual Privacy ◽  
Password Security

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.

Main large data set features detection by a linear predictor model

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  
Keyword(s):  
Large Data ◽  
Data Set ◽  
Linear Predictor ◽  
Large Data Set ◽  
Predictor Model

Governance Problems in Closely Held Corporations

2011 ◽  
Vol 46 (4) ◽  
pp. 943-966 ◽  
Author(s):  
Venky Nagar ◽  
Kathy Petroni ◽  
Daniel Wolfenzon
Keyword(s):  
Ownership Structure ◽  
Large Data ◽  
Minority Shareholders ◽  
Return On Assets ◽  
Data Set ◽  
Large Data Set ◽  
Ownership Rights ◽  
Shared Ownership

AbstractA major governance problem in closely held corporations is the majority shareholders’ expropriation of minority shareholders. As a solution, legal and finance research recommends that the main shareholder surrender some control to minority shareholders via ownership rights. We test this proposition on a large data set of closely held corporations. We find that shared-ownership firms report a substantially larger return on assets and lower expense-to-sales ratios. These findings are robust to institutionally motivated corrections for endogeneity of ownership structure. We provide evidence on the presence of governance problems and the effectiveness of shared ownership as a solution in settings characterized by illiquidity of ownership.

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