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Big data management in participatory sensing : issues, trends and future directions

- Karim, Ahmad, Siddiqa, Aisha, Safdar, Zanab, Razzaq, Maham, Imran, Muhammad

  • Authors: Karim, Ahmad , Siddiqa, Aisha , Safdar, Zanab , Razzaq, Maham , Imran, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 107, no. (2020), p. 942-955
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  • Description: Participatory sensing has become an emerging technology of this era owing to its low cost in big sensor data collection. Prior to participatory sensing, large-scale deployment complexities were found in wireless sensor networks when collecting data from widespread resources. Participatory sensing systems employ handheld devices as sensors to collect data from communities and transmit to the cloud, where data are further analyzed by expert systems. The processes involved in participatory sensing, such as data collection, transmission, analysis, and visualization, exhibit certain management issues. This study aims to identify big data management issues that must be addressed at the cloud side during data processing and storing and at the participant side during data collection and visualization. It then proposes a framework for big data management in participatory sensing to resolve the contemporary big data management issues on the basis of suggested principles. Moreover, this work presents case studies to elaborate the existence of the highlighted issues. Finally, the limitations, recommendations, and future research directions for academia and industry in the domain of participatory sensing are discussed. © 2017. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

Deep learning-based approach for detecting trajectory modifications of cassini-huygens spacecraft

- Aldabbas, Ashraf, Gal, Zoltan, Ghori, Khawaja, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Aldabbas, Ashraf , Gal, Zoltan , Ghori, Khawaja , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 39111-39125
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  • Description: There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life. © 2013 IEEE.

Semantic manipulation and business context in big data analytics

- Dinh, Loan

  • Authors: Dinh, Loan
  • Date: 2018
  • Type: Text , Thesis , PhD
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  • Description: Business organisations receive a huge amount of data from many sources every day. These data are known as big data. Since they are mostly unstructured, big data creates a complex problem of how to capture, manage, analyse and then derive meaningful information from them. To deal with the challenges that big data has brought, this research proposes a new technique in big data analytics in the business area to integrate semantically meaningful information relevant to textual queries and business context. To achieve this aim, this study makes three major related contributions. Firstly, the relationship between business processes and strategies is established using the concept of a rule-based inference model via facts and annotations. This relationship is required to determine the importance of a big data query for a business organisation. Secondly, we introduce approaches to determine the significance level of a query, by incorporating the processstrategy relationship, process contributions and priority of business strategies. Thirdly, the proposed data analytic technique embeds business context into the bedrock of data collection and analysis process. The first two contributions were implemented using Python programming language including the Pyke package (Pyke is built in the Python environment and has an artificial intelligence tool for the development of expert systems) and their performances were analysed based on a business use case. The last contribution was implemented mainly in the Hadoop and Java programs. Results show that the first contribution successfully establishes the processstrategy relationship, the second calculates the significance level of a query in relation to a business organisation, while the third reveals the huge impact of query significance level and business context on big data collection and captures deep business insights.
  • Description: Doctor of Philosophy

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

- Benke, Kurt, Norng, Sorn, Robinson, Nathan, Chia, K., Rees, David, Hopley, J.

  • Authors: Benke, Kurt , Norng, Sorn , Robinson, Nathan , Chia, K. , Rees, David , Hopley, J.
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Geoderma Vol. 366, no. (2020), p.
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  • Description: The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases with measurement sets that may have limited transferability. The pedotransfer function generation was based on a machine learning algorithm incorporating the Generalized Linear Mixed Model with interactions and nested terms, with Residual Maximum Likelihood estimation, and a predictor-frequency ranking system with step-wise reduction of predictors to evaluate the predictive errors in reduced models. The source of the data was the Victorian Soil Information System (VSIS), which is a database administered for soil information and mapping purposes. The database contains soil measurements and information from locations across Victoria and is a repository of historical data, including monitoring studies. In total, data from 93 projects were available for inputs to modelling and analysis, with 5158 samples used to derive predictors for EC and 1954 samples used to derive predictors for OC. Over 500 models were tested by systematically reducing the number of predictors from the full model. Five-fold cross-validation was used for estimation of model mean-squared prediction error (MSPE) and mean-absolute percentage error (MAPE). The results were statistically significant with only a gradual reduction in error for the top-ranked 50 models. The prediction errors (MSPE and MAPE) of the top ranked model for EC are 0.686 and 0.635, and 0.413 and 0.474 for OC respectively. The four most frequently occurring predictors both for EC and OC prediction across the full set of models were found to be soil depth, pH, particle size distribution and geomorphological mapping unit. The possible advantages and disadvantages of this approach were discussed with respect to other machine learning approaches. © 2020 Elsevier B.V.

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

- Hassan, Shafiqul, Dhali, Mohsin, Zaman, Fazluz, Tanveer, Muhammad

  • Authors: Hassan, Shafiqul , Dhali, Mohsin , Zaman, Fazluz , Tanveer, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Vol. 7, no. 6 (2021), p.
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  • Description: Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology. © 2021 The Author(s)

The spectrum of big data analytics

- Sun, Zhaohao, Huo, Yanxia

  • Authors: Sun, Zhaohao , Huo, Yanxia
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Journal of Computer Information Systems Vol. 61, no. 2 (2021), p. 154-162
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  • Description: Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems.

The role of big data analytics in industrial internet of things

- Rehman, Muhammad, Yaqoob, Ibrar, Salah, Khaled, Imran, Muhammad, Jayaraman, Prem, Perera, Charith

  • Authors: Rehman, Muhammad , Yaqoob, Ibrar , Salah, Khaled , Imran, Muhammad , Jayaraman, Prem , Perera, Charith
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 99, no. (2019), p. 247-259
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  • Description: Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V.

Secure big data ecosystem architecture : challenges and solutions

- Anwar, Memoona, Gill, Asif, Hussain, Farookh, Imran, Muhammad

  • Authors: Anwar, Memoona , Gill, Asif , Hussain, Farookh , Imran, Muhammad
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
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  • Description: Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems. © 2021, The Author(s).

Methods and applications of social media monitoring of mental health during disasters : scoping review

- Teague, Samantha, Shatte, Adrian, Weller, Emmelyn, Fuller-Tyszkiewicz, Matthew, Hutchinson, Delyse

  • Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
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  • Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.

Deep learning and big data technologies for IoT security

- Amanullah, Mohamed, Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Ahmed, Ejaz, Nainar, Abdul, Akim, Nazihah, Imran, Muhammad

  • Authors: Amanullah, Mohamed , Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Ahmed, Ejaz , Nainar, Abdul , Akim, Nazihah , Imran, Muhammad
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Computer Communications Vol. 151, no. (2020), p. 495-517
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  • Description: Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. © 2020 Elsevier B.V.

Relevance of frequency of heart-rate peaks as indicator of ‘Biological’ Stress level

- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Aryal, Sunil, Klein, Britt

  • Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: ICONIP 2018 International on Neural Information Processing; Siem Reap, Cambodia; 13th-16th December, 2018 p. 598-609
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  • Description: The biopsychosocial (BPS) model proposes that health is best understood as a combination of bio-physiological, psychological and social determinants, and thus advocates for a far more comprehensive investigation of the relationships between ‘mind-body’ health. For this holistic analysis, we need a suitable measure to indicate participants’ ‘biological’ stress. With the advent of wearable sensor devices, health monitoring is becoming easier. In this study, we focus on bio-physiological indicators of stress, from wearable devices using the heart-rate data. The analysis of such heart-rate data presents a set of practical challenges. We review various measures currently in use for stress measurement and their relevance and significance with the wearables’ heart-rate data. In this paper, we propose to use the novel ‘peak heart-rate count’ metric to quantify level of ‘biological’ stress. Real life biometric data obtained from digital health intervention program was considered for the study. Our study indicates the significance of using frequency of ‘peak heart-rate count’ as a ‘biological’ stress measure.

Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection

- Aryal, Sunil, Ting, Kaiming, Haffari, Gholamreza

A survey on context awareness in big data analytics for business applications

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
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  • Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

The role of big data analytics in internet of things

- Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Khan, Imran, Imran, Muhammad

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Khan, Imran , Imran, Muhammad
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 129, no. (2017), p. 459-471
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  • Description: The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions. © 2017 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

Efficient prefetching and client-side caching algorithms for improving the performance of read operations in distributed file systems

- Nalajala, Anusha, Ragunathan, Thirumalaisamy, Naha, Ranesh

  • Authors: Nalajala, Anusha , Ragunathan, Thirumalaisamy , Naha, Ranesh
  • Date: 2022
  • Type: Journal article
  • Relation: IEEE access Vol. 10, no. (2022), p. 126232-126252
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  • Description: Modern web applications are deployed in cloud computing systems because they support unlimited storage and computing power. One of the main back-end storage components of this cloud computing system is the distributed file system which allows massive amounts of data to be stored and accessed. In most web applications deployed in such systems, read operations are performed more frequently than write operations. Consequently, increasing the efficiency of read operations in distributed file systems is a challenging and important research problem. The two main procedures used in distributed file systems to improve the performance of read operations are prefetching and caching. In this paper, we proposed novel prefetching and multi-level caching algorithms based on the Access-Frequency and Access-Recency ranking of file blocks that were previously accessed by client application programs. We also proposed new augmented ranking algorithms for prefetching file blocks by combining the Access-Frequency and Access-Recency ranking of the file blocks. We used rank-based replacement algorithms to replace file blocks in the cache. The simulation results show that, the proposed algorithms improve the performance of read operations on distributed file systems by 29% to 77% in comparison to algorithms proposed in the literature.

Managing big RDF data in clouds : challenges, opportunities, and solutions

- Elzein, Nahla, Majid, Mazlina, Hashem, Ibaker, Yaqoob, Ibrar, Alaba, Fadele, Imran, Muhammad

  • Authors: Elzein, Nahla , Majid, Mazlina , Hashem, Ibaker , Yaqoob, Ibrar , Alaba, Fadele , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Sustainable Cities and Society Vol. 39, no. (2018), p. 375-386
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  • Description: The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. This paper presents an overview of the existing challenges, evolving opportunities, and current developments towards managing big RDF data in clouds and provides guidance and substantial lessons learned from research in big data management. In particular, it highlights the basic principles of RDF data management, which allow researchers to know the most recent stage in developing RDF graphs and its achievement. Additionally, the research provides comparative studies among current storage systems and query processing approaches in understanding their efficiency. The paper also provides a vision for long-term future research directions by providing highlights on future challenges and opportunities in RDF domain. © 2018 Elsevier Ltd

A mathematical foundation of big data

- Sun, Zhaohao, Wang, Paul

  • Authors: Sun, Zhaohao , Wang, Paul
  • Date: 2017
  • Type: Text , Journal article
  • Relation: New Mathematics and Natural Computation Vol. 13, no. 2 (2017), p. 83-99
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  • Description: The recent research evolution on big data has brought exciting aspiration to mathematicians, computer scientists and business professionals alike. However, the lack of a sound mathematical foundation presents itself as a real challenge amidst the swarm of big data marketing activities. This paper intends to propose a possible mathematical theory as a foundation for big data research. Specifically, we propose the concept of the adjective "big" as a mathematical operator, furthermore, the concept of so-called "big" logically and naturally fits the concept of being "linguistics variable" as per fuzzy logic research community for decades. The consequence of adopting such a mathematical modeling can be profoundly considered as an abstraction of the technologies, systems, tools for data management and processing that transforms data into big data. In addition, the concept of infinity of the big data is based on the theory of calculus and the set theory. Furthermore, the concept of relativity of the big data, as we find out, is based on the operations of the fuzzy subsets theory. The proposed approach in this paper, we hope, can facilitate and open up more opportunities for big data research and developments on big data analytics, business analytics, big data intelligence, big data computing as well as big data science. © 2017 World Scientific Publishing Company.

Themes in data mining, big data, and crime analytics

- Oatley, Giles

  • Authors: Oatley, Giles
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Vol. 12, no. 2 (2022), p.
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  • Description: This article examines the impact of new AI-related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI-powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools. © 2021 Wiley Periodicals LLC.
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