Big Data Collection Research Articles

DiScO: novel rapid systems mapping to inform digital transformation of health systems

BackgroundGlobal health systems are confronting challenges that intersect climate change with evolving communicable and non-communicable public health risks. Addressing these challenges requires systems integration via citizen big data that exist outside health systems. However, systems integration across jurisdictions is a complex challenge that requires stakeholder input. This study’s purpose was to conduct rapid systems mapping with international health system stakeholders to inform the development and implementation of a global digital citizen science observatory (DiScO), which aims to catalyze digital transformation of health systems across jurisdictions.MethodsA rapid qualitative systems mapping study was conducted during the International Society for Behavioral Nutrition and Physical Activity Annual Global Summit in Uppsala, Sweden, in June 2023. The choice of the venue and approach was informed by three key criteria: (1) Established evidence linking physical activity and nutrition with non-communicable diseases; (2) Concrete existing methods of obtaining citizen big data by physical activity and nutrition researchers; (3) Precedence of physical activity and nutrition researchers conducting citizen science as well behavioral/clinical big data collection. The design of this study was an innovative pre-post systems map development, which consisted of (1) real-time rapid systems mapping (pre/initial map) by engaging with international stakeholders and (2) adjustment of the real-time systems map (post/final map) after analyzing stakeholder discussion data.ResultsRapid systems mapping resulted in a complex network that included key themes to successfully develop and implement DiScO: priorities, opportunities, risks, challenges, partnerships, and resources. Additionally, a new theme emerged organically through stakeholder group discussions – mitigation strategies. The adapted rapid systems map (i.e., after data analyses) depicts 23 key nodes of intervention across the seven key themes.ConclusionRapid systems mapping at international symposia is a novel methodological approach to capture stakeholder input, particularly to understand complexity across international jurisdictions – an approach that can be replicated across disciplines and sectors to inform digital transformation of health systems. The development and implementation of DiScO, a platform for decentralization and democratization of technology, will take into consideration all the key nodes of intervention identified in the rapid systems map to promote digital health for equity across global jurisdictions.

Research on the Impact of Big Data and Enterprise Digitalization on the E-commerce Industry and Coping Strategies

Abstract: Big data, enterprise digitalization, and e-commerce all have one thing in common: they were born from the Internet. Since the onset of the 21st century, the global Internet industry has experienced rapid growth, with e-commerce emerging as a key player. Participants in e-commerce engage with both the global online marketplace and the physical market, utilizing modern communication networks and information technology to conduct business activities through e-commerce platforms or service platforms. E-commerce embodies characteristics such as market globalization, rapid transactions, virtualization, and cost-effectiveness, making it highly favored by enterprises. Presently, e-commerce relies heavily on robust databases, as well as the data collection, processing, and analysis capabilities of big data. This reliance compels traditional enterprises to undergo digital transformation. This study delves into the impact of big data and enterprise digitalization on the e-commerce industry, exploring the mechanisms underlying their influence on cross-border e-commerce. By examining these dynamics, the study aims to provide insights into the evolving landscape of e-commerce and the transformative potential of big data and digitalization in shaping its future trajectory.

Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use.

Evaluation of the Implications of Big Data Analytics with Organizational Performance in Small and Medium Enterprises and Its Associated Role of Knowledge Management

Introduction: Big data are distinguished by their quantity, speed, variety, and reliability. Big data presents opportunity for “small and medium-sized” businesses (SME), in addition to being a reality for giant firms. The term "big data" denotes to the fact that numerous varieties of data have become more easily accessible, and its consequences for various organizational types may vary from one another. SME can benefit greatly from creating and utilizing big data. Aim and objectives: The main purpose of the study is to assess the implications of “big data analytics” with organizational performance in small and medium enterprises and its associated role of knowledge management. Methods: This study's main goal was to develop a big data KM model for SME through an examination of several business situations. This study used the qualitative analysis of data methodology. The study gathered examples of large data for SME, which it then used to test a KM model. Data collection, coding, and analysis are the three main components of qualitative data analysis. Results: Big data collection is a business endeavor for SMEs. An SME needs to specify its big data plans and deal with all potential organizational difficulties. Big data must be strategically used in a way that is consistent with the SME's business strategy and embraces a long-term strategy for competitive sustainability. To tackle the big data challenge, IT solutions are chosen based on the definition of data requirements.

뉴욕경찰청의 알고리즘 예측치안 전략의 논쟁점

The purpose of this study is to examine the controversies surrounding the algorithmic predictive policing strategy adopted by the New York Police Department. NYPD is very active in pursuing algorithmic predictive policing strategies, but it is facing a variety of criticisms, including: First, personal information collection activities for predictive security violate the ideology of the Fourth Amendment to the U.S. Constitution, which prohibits invasion of privacy. Second, as the police rely on instructions generated by algorithmic predictions, police operations are becoming dehumanized. Third, the police exercise their police authority intensively against dangerous people or dangerous areas, and there is a risk that these people will eventually be negatively stigmatized and become criminals. Fourth, the police unilaterally exercise police power based on algorithmic predictions, rather than a two-way police service that reflects the needs of citizens or the community. Fifth, the efficiency of algorithmic predictive policing has not been verified, and the problem of contamination of used big data is being raised. Sixth, it is impossible to confirm whether the data provided by the police to private companies is opaque and secure. Seventh, there is the irrationality of Police power being replaced by private companies' for-profit businesses. Eighth, the New York Police Department is subject to monitoring of algorithmic predictive policing through the Local Law in Relation to Automated Decision Systems Used by Agencies and Public Oversight of Surveillance Technology(POST) Act, but the problems remain unresolved. Ninth, the New York Police Department continues to engage in legal disputes with human rights watchdog groups such as the Brennen Center over the non-disclosure of algorithmic predictive security information, losing most cases, raising questions about the legality of big data collection and management.

Visibility in the Italian Art World: How Far Along We Are

Even though the concept of “nationality” when we talk about an internationally acclaimed artists is more and more blurred and has little to do with the importance of the artworks, it is evident how small the Italian art presence in great international art manifestations is, sometimes almost negligible. It is then necessary to understand the causes of this situation, to draw a map of the current state of the contemporary Italian scene and state some resolutions from it. The first 2022 Report “How (well) known is Italian art abroad?” by BBS-Lombard Benefit Corporation, together with Arte Generali has investigated how much Italian contemporary artists are (in)visible in the international art market, laying the groundwork for a much more in-depth research of the art system, with the ultimate purpose of giving new solutions to overcome this situation, wandering whether Italian art has a business model with (still) some gray areas - unlike almost all other product sectors – and how building an economy to bridge the gap, having a market strategy and being attractive in the international positioning can be a strategic move. The study was conducted carrying out both qualitative and quantitative analysis throughout more than 20 interviews to Italian curators internationally known that have helped us understand causes, strengths and weaknesses of the Italian art system and a big data collection about the presence of Italian artist in international museums, art manifestations, biennials, such as Biennale di Venezia and documenta, international art galleries and auctions. The global media coverage of Italian artists was analyzed with the help of Artiker and through the artificial intelligence of Wondeur AI, partner of Arte Generali, it was possible to analyze the relationship between art development and cities, considering the city’s overall success rate (percentages of artists showing growth in the city) and the city’s risk propensity (ability to attract artists). The instrument of the Italian Council was also studied, the main MIC project (Italian Ministry for Culture) in support of Italian art and the initiatives of Italian cultural institutes abroad. From the overall analysis some relevant names have emerged: Francesco Vezzoli, Monica Bonvicini, Enrico David, Paola Pivi, Tatiana Trouvè, Roberto Cuoghi, Rosa Barba, Massimiliano Cattelan (above all) and a few others, all united by a time of study or work abroad which allowed them to create valuable relationships with curators, galleries and museums abroad.

Control and management of geomechanical state and stability of structural elements of mining engineering elements in open-pit mines based on Big Data collection and analysis

Control and management of geomechanical state and stability of structural elements of mining engineering elements in open-pit mines based on Big Data collection and analysis

Research on Urban Street Spatial Quality Based on Street View Image Segmentation

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage.

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs.

The Technology Evolution and Future Trend of Smart Blackboard

As an important tool for educational digitization, the function of the smart blackboard has gradually evolved from a single teaching information-sharing tool to a smart system for knowledge expression, sharing and generation. Smart blackboard technology has been experienced from chalk + blackboard to projection equipment + blackboard, to interactive electronic whiteboard, and then to today's integrated touch, high-definition display and artificial intelligence technology. The evolution process of the smart blackboard is characterized by interactivity, effectiveness and intelligence. At present, the smart blackboard has become a collection of many advanced technologies. It is gradually integrated into modern technologies such as artificial intelligence, cloud computing, big data analysis and the Internet of Things, making remote collaboration and intelligent classroom management possible. It has become a smart system for big data collection, mining and analysis, evaluation and feedback, and teaching decision-making. Looking forward to the future, the smart blackboard will pay more attention to user experience design, provide an immersive learning experience through Augmented Reality and Virtual Reality technology, and optimize personalized teaching content in combination with machine learning, to lay a foundation for building an intelligent and personalized teaching environment.

Arabic text classification based on analogical proportions

AbstractText classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k‐NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form ‘x is to as is to ’), which have recently been shown to be effective in classifying ‘structured’ data, to classify ‘unstructured’ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a ‘valid’ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC‐Arabic, CNN‐Arabic and AlKhaleej‐2004. We also compare analogical classifiers with both classical ML‐based and Deep Learning‐based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC‐Arabic corpus and AlKhaleej‐2004, and the best average accuracy (85.64%) for CNN‐Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier.

Expert Consensus on Big Data Collection of Skin and Appendage Disease Phenotypes in Chinese.

The collection of big data on skin and appendage phenotypes has revolutionized the field of personalized diagnosis and treatment by enabling the evaluation of individual characteristics and early detection of abnormalities. To establish a standardized system for collecting and measuring big data on phenotypes, a systematic categorization of measurement entries has been undertaken, accompanied by recommendations on measurement entries, environmental equipment requirements, and collection processes, tailored to the needs of different usage scenarios. Specific collection sites have also been recommended based on different index characteristics. A multi-center, multi-regional collaboration has been initiated to collect big date on phenotypes of healthy and diseased skin in the Chinese population. This data will be correlated with patient disease information, exploring the factors influencing skin phenotype, analyzing the phenotypic data features that can predict prognosis, and ultimately promoting the exploration of the pathophysiology and pathogenesis of skin diseases and therapeutic approaches. Non-invasive skin measurement robots are also in development. This consensus aims to provide a reference for the study of phenomics and the standardization of phenotypic measurements of skin and appendages in China.

Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system

It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative for environmentally sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has emerged as a promising solution. Smart Agriculture can significantly bolster agricultural development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging the insights from this data can facilitate informed decision-making regarding water management, thereby fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather prediction services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study provides a clear roadmap that can be readily adapted for similar research endeavors.© 2023 Elsevier Inc. All rights reserved.

Form generative approach for front face design of electric vehicle under female aesthetic preferences

Vehicles are the most representative product of both transportation and industry. Fueled by the growing popularity of energy-saving and environmentally friendly ideas and policies, new energy technologies and their supporting industries are experiencing constant development and refinement. As a result, electric vehicles (EVs) are steadily displacing traditional fuel-powered vehicles as the dominant force in the vehicle market. The experience economy is driving a significant shift in the direction of EV development, which is transitioning from improving functionality to optimizing the emotional connection consumers have with their vehicles. This means the form factor of the vehicle itself is becoming a key element in attracting consumers and influencing purchasing decisions. Over half of China's EV consumer market is comprised of females. However, many companies continue to develop products solely from a male perspective, missing out on a vast market opportunity. Therefore, incorporating design elements that cater to female aesthetic preferences into EVs can undoubtedly play a crucial role in unlocking this significant segment of the market. Based on this, the paper proposes a form generative design approach that specifically caters to the preferences of female consumers within the theoretical framework of Kansei engineering.The research process begins by obtaining Kansei imagery through the collection and analysis of big data. Subsequently, eye-tracking experiments are conducted to define the key design elements of the automobiles. Concurrently, appropriate aesthetic measurement indicator (AMI) system is established. Next, AMI values are computed for the sample set. This data is then leveraged to create a mapping relationship with the Kansei imagery, enabling the objective reflection of subjective aesthetic evaluations. Finally, an integrated Kansei evaluation equation serves as the fitness function within a generative approach that utilizes genetic algorithms and visual AI to produce form design solutions for electric vehicles. The evaluation confirms that the proposed design approach successfully generates a substantial number of high-quality creative solutions that align with female aesthetic preferences, while maintaining low cost and high efficiency.

Measurement and Countermeasures for College Students’ Emotions and Attitudes Post-popular Feelings Events: An Analysis Based on Smart PLS Model Data

To accurately gauge the emotional and attitudinal responses of college students post-popular feelings events, and to formulate scientifically sound strategies for managing public sentiment effectively and forestalling secondary quandaries, a pioneering big data simulation approach is introduced. This method, utilizing big data collection and processing techniques, entails the random sampling of a substantial number of college students to gather evaluative data on their emotional and attitudinal shifts before and after popular feelings events, as well as the efficacy of countermeasures. Subsequent to this, the amassed data undergoes meticulous fitting analysis via the Smart PLS 4.0 software. Noteworthy among the impact coefficients are negative emotions (r=0.146), negative attitudes (r=0.132), and the pivotal role of student opinion leaders (Loading=0.921). The standardized root mean square residual (SRMR) of 0.049 underscores the confirmatory model’s relevance. Results underscore the need for countermeasures to place a significant emphasis on mitigating negative emotions among college students. Selection of student opinion leaders and the establishment of rumor-refutation teams emerge as vital components of these response initiatives.

Key Issues of Private Law Transformation under Influence of Behavioural Economics and Data Science

Corporations are now increasingly embracing the advances of Data Science and behavioural economics. It will undoubtedly have far-reaching implications for many areas of legal regulation . In author’s opinion, private law institutions aimed at regulating relations between business and customers will be the first to deal with transformation. The paper outlines the main questions and issues that lawyers will face in the next five to ten years as the ideas of behavioural economy and Data Science spread to private law, and offers some thoughts addressing these issues. In the beginning the author briefly reviews the progress of behavioural economy and how its achievements help to attain the aims of legal regulation. In particular, the author surveys private law tools such as discretionary rules and information disclosure for “pushing” individuals to a more rational behaviour. The author then analyses how the current level of Big Data collection, processing and use can affect the discretionary rules and information disclosure in corporate contracts with consumers, including the possibility of private law institution “personalisation” with account of the individual features of the parties to the transactions. Further on, the asks and attempts to answer the key question of the article: What regulatory environment should be in place to enable behaviourally informed personalisation of private law institutions using Big Data? In responding to this question, the author analyses three related problems arising at the intersection of law, Data Science, psychology, and economics: How to ensure freedom of choice and autonomy of will of individuals while using information and behavioural innovations? How much information should legal actors be able to receive in order to make the best decision? How to find a balance between private law “personalisation” and personal data protection? In conclusion, the author summarises the results of the study and concludes that to date there are no universal rules and algorithms for private law personalisation, and the introduction of Data Science and behavioural economics into law is still taking place in individual legal relations on case-by-case basis.

Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories

A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of the action-research case-study methodology and customized machine learning (ML) techniques, supplemented by a workflow diagram. The ML techniques included language preprocessing, normalization, tokenization, keyword association analytics, learning tree analysis, credibility/reliability/validity checks, heatmaps, and scatter plots. The author analyzed over 8 GB of employee behavior big data from a multinational Fintech company global intranet. The five-factor personality theory (FFPT) from the psychology discipline was integrated into semi-supervised ML to classify retrospective employee behavior and then identify cybercrime risk. Higher levels of employee neuroticism were associated with a greater organizational cybercrime risk, corroborating the findings in empirical publications. In stark contrast to the literature, an openness to new experiences was inversely related to cybercrime risk. The other FFPT factors, conscientiousness, agreeableness, and extroversion, had no informative association with cybercrime risk. This study introduced an interdisciplinary paradigm shift for big data cognitive computing by illustrating how to integrate a proven scientific construct into ML—personality theory from the psychology discipline—to analyze human behavior using a retrospective big data collection approach that was asserted to be more efficient, reliable, and valid as compared to traditional methods like surveys or interviews.

A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing

As one of important Edge-Cloud solutions, mobile crowd sensing (MCS) platform resides in the cloud, and recruits massive workers in the edge network to sense data, so big data can be collected to build various applications or services to consumers, which is a promising computing paradigm. There are two critical properties that are not well addressed. One is current privacy preserving (PP) scheme pays little attention to quality-ware based data collection, so malicious or low-trust workers can cause false or low-quality data attacks, which will seriously affect the foundation of data-based services. The second is the lack of an intelligent worker selection scheme so to reduce costs and improve data quality under PP condition. To address the above issues, in this paper, we first propose a big data collection framework that not only has the function of PP but also can effectively identify worker trust, which is not achieved by previous studies. In particular, the worker trust identification approach in this paper abandons the past unreasonable assumption that data quality can be identified by the obtained data of data requester (DR). Then, we propose a reinforcement-based worker selection scheme that comprehensively considers workers’ trust, workers’ quotation and workers’ locations, so as to overcome the problem of worker recruitment that cannot be quality-ware in the past location privacy protection. Theoretical analysis and massive experimental results demonstrate that the proposed strategy is better than the state-of-the-art strategies in data quality, system cost and other performance indicator