Search results

Big Data Analytics provides a multitude of opportunities for organizations to improve service operations, but it also increases the threat of external parties gaining unauthorized access to sensitive customer data. With data breaches now a common occurrence, it is becoming increasingly plain that while modern organizations need to put into place measures to try to prevent breaches, they must also put into place processes to deal with a breach once it occurs. Prior research on information technology security and services failures suggests that customer compensation can potentially restore customer sentiment after such data breaches. The paper aims to discuss these issues.

In this study, the authors draw on the literature on personality traits and social influence to better understand the antecedents of perceived compensation and the effectiveness of compensation strategies. The authors studied the propositions using data collected in the context of Target’s large-scale data breach that occurred in December 2013 and affected the personal data of more than 70 million customers. In total, the authors collected data from 212 breached customers.

The results show that customers’ personality traits and their social environment significantly influences their perceptions of compensation. The authors also found that perceived compensation positively influences service recovery and customer experience.

The results add to the emerging literature on Big Data Analytics and will help organizations to more effectively manage compensation strategies in large-scale data breaches.

Fabricated data jeopardize the reliability of large-scale population surveys and reduce the comparability of such efforts by destroying the linkage between data and measurement constructs. Such data result in the loss of comparability across participating countries and, in the case of cyclical surveys, between past and present surveys. This paper aims to describe how data fabrication can be understood in the context of the complex processes involved in the collection, handling, submission and analysis of large-scale assessment data. The actors involved in those processes, and their possible motivations for data fabrication, are also elaborated.

Computer-based assessments produce new types of information that enable us to detect the possibility of data fabrication, and therefore the need for further investigation and analysis. The paper presents three examples that illustrate how data fabrication was identified and documented in the Programme for the International Assessment of Adult Competencies (PIAAC) and the Programme for International Student Assessment (PISA) and discusses the resulting remediation efforts.

For two countries that participated in the first round of PIAAC, the data showed a subset of interviewers who handled many more cases than others. In Case 1, the average proficiency for respondents in those interviewers’ caseloads was much higher than expected and included many duplicate response patterns. In Case 2, anomalous response patterns were identified. Case 3 presents findings based on data analyses for one PISA country, where results for human-coded responses were shown to be highly inflated compared to past results.

This paper shows how new sources of data, such as timing information collected in computer-based assessments, can be combined with other traditional sources to detect fabrication.

The purpose of this paper is to describe a large-scale algorithm for generating a catalogue of scientific publication records (citations) from a crowd-sourced data, demonstrate how to learn an optimal combination of distance metrics for duplicate detection and introduce a parallel duplicate clustering algorithm.

The authors developed the algorithm and compared it with state-of-the art systems tackling the same problem. The authors used benchmark data sets (3k data points) to test the effectiveness of our algorithm and a real-life data ( > 90 million) to test the efficiency and scalability of our algorithm.

The authors show that duplicate detection can be improved by an additional step we call duplicate clustering. The authors also show how to improve the efficiency of map/reduce similarity calculation algorithm by introducing a sampling step. Finally, the authors find that the system is comparable to the state-of-the art systems for duplicate detection, and that it can scale to deal with hundreds of million data points.

Academic researchers can use this paper to understand some of the issues of transitivity in duplicate detection, and its effects on digital catalogue generations.

Industry practitioners can use this paper as a use case study for generating a large-scale real-life catalogue generation system that deals with millions of records in a scalable and efficient way.

In contrast to other similarity calculation algorithms developed for m/r frameworks the authors present a specific variant of similarity calculation that is optimized for duplicate detection of bibliographic records by extending previously proposed e-algorithm based on inverted index creation. In addition, the authors are concerned with more than duplicate detection, and investigate how to group detected duplicates. The authors develop distinct algorithms for duplicate detection and duplicate clustering and use the canopy clustering idea for multi-pass clustering. The work extends the current state-of-the-art by including the duplicate clustering step and demonstrate new strategies for speeding up m/r similarity calculations.

The online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.

This research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.

This research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.

This research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.

This research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.

It also reveals the data convergence process of the social system and the technological system.

This research offers a new research method for the real-time regulation of the car-hailing platform.

Over the past few years, assemblage theory or assemblage thinking has garnered increasing attention in educational research, but has been used only tangentially in explications of the nature of comparative and international education (CIE) as a field. This conceptual examination applies an assemblage theory lens to explore the contours of CIE as a scholarly field marked by its rich and interweaved architecture. It does so by first reviewing Deleuze and Guattari’s (1987) principles of rhizomatic structures to define the emergence of assemblages. Secondly, it transposes these principles in conceiving the field of CIE as a meta-assemblage of associated and subordinated sub-assemblages of actors driven by varied disciplinary, interdisciplinary or multidisciplinary interests. Finally, it interrogates the role of Big Data technologies in exerting (re)territorializing and deterritorializing tendencies on the (re)configuration of CIE. The chapter concludes with reiterating the variable character of CIE as a meta-assemblage and proposes ways to move this conversation forward.

This paper aims to understand the character of the relationship between tourism growth and residents’ social trust.

The study uses large-scale data to model the effect of tourism on generalized trust attitudes Among advantages to analyzing data from large-scale social surveys, extensive content and representative coverage of the population are probably the most appealing. The broad coverage of the population of the large-scale social surveys allows for a broader generalization of the study results as well as comparison of areas with very different tourist activity.

This study offers two key findings. First, the effect of tourist arrivals (as per capita) on social trust attitudes is stronger in poorer regions than in wealthier regions. Second, only domestic tourism positively affects trust.

This study delivered a straightforward analysis of large data to be able to generalize findings and make a significant theoretical contribution to tourism discipline. This goal was pursued at the expense of complex or in-depth explanation of the observed phenomenon.

Findings from this study indicate that there are at least two crucial criteria for tourism to be able to strengthen residents’ social trust. First, domestic tourism should be encouraged in destination regions in their early development stages and in more homogeneous regions. Perhaps, focus on domestic tourists before internationalization of a tourism product is the most effective way to promote tourism development that is supported by local residents. Second, tourism is likely to have stronger positive effect on social trust in poorer regions. Thus, tourism policy makers should take into consideration the actual economic need for tourism. Residents in wealthier regions may show less support for tourism simply because they don’t need it and they have no economic incentives to be involved. In fact, tourism in wealthier regions is likely to diminish residents’ social trust, and thus it disrupts local social and political processes that rely on high social trust.

Social trust is considered an important measure of social cohesion and it enables modern societies to thrive. Social trust has not been problematized in the context of contemporary tourism growth. This is the first study that uses large data social survey to model the effect of tourism on social trust in European destination regions.

In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks.

Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network.

This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness.

A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

This paper describes the application of, enhancements to, and use of surface fire spread models in predicting and mitigating fire risk in the Wildland–Urban Interface (WUI). Research and fire management strategies undertaken in the East Bay Hill region (containing the 1991 Tunnel Fire) of the San Francisco Bay area over the past decade are reported. We ascertain that surface fire spread modeling has impacted policy and decision making, resulting in a regional strategic plan where large landowners and public agencies are able to implement fire mitigation practices. Although these practices involve extensive fuel management within a buffer zone between the wildland and residential properties, the residential property owners are still at risk, as no strategy within neighborhoods can be accurately mapped using the current scale of the data and models. WUI fires are eventually extinguished by fire fighters on the ground, up close, and at the backyard scale. We argue that large-scale (backyard scale) mapping and modeling of surface fire spread is necessary to engage the individual homeowner in a fuels management strategy. We describe our ongoing research and strategies, and suggest goals for future research and development in the area of large-scale WUI fire modeling and management.

The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module.

This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization.

Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.