scholarly journals Big data and learning analytics: Singular or plural?

First Monday ◽  
2017 ◽  
Vol 22 (4) ◽  
Author(s):  
Anna Wilson ◽  
Terrie Lynn Thompson ◽  
Cate Watson ◽  
Valerie Drew ◽  
Sarah Doyle
Keyword(s):  
Public Health ◽  
Big Data ◽  
Business Intelligence ◽  
Learning Analytics ◽  
Context Dependence ◽  
Physical Sciences ◽  
Explicit Consideration ◽  
Analytical Approaches ◽  
Data Context

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

Applying a Business Intelligence System in a Big Data Context: Production Companies

2020 ◽  
pp. 268-274
Author(s):  
Jesús Silva ◽  
Mercedes Gaitán ◽  
Noel Varela ◽  
Doyreg Maldonado Pérez ◽  
Omar Bonerge Pineda Lezama
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Intelligence System ◽  
Data Context

25.K. Workshop: Unleashing the power of large routinely collected data for the benefit of public health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Research ◽  
Public Health Research ◽  
Data Set ◽  
Vast Number ◽  
Research And Practice ◽  
Vast Amount ◽  
Routinely Collected Data ◽  
Analytical Approaches

Abstract As for many other scientific domains, public health is nowadays contemplating the opportunities of using but also the challenges of mastering large routinely collected data ('big data') in order to generate knowledge, and to inform and evaluate decisions and policies. In a wide perspective, “public health relevant big data” extends from the “omics” to social network postings. In this workshop we will concentrate on electronic health records and medical claims data (hereafter referred to as eHRs) which are increasingly being used in public health research and practice. Depending of the national and historical contexts, eHRs encompass details pertaining not only to hospital admissions but also to deaths and infectious diseases registrations, prescriptions and contacts with health services. As a result, eHRs datasets are both long (ie, vast number of records) and wide (i.e., vast amount of information per record). Although the usual public health and epidemiological concepts of inference and causation are still relevant for the analysis of big health data and interpretation of their results, a new set of methodological approaches is also necessary to tackle them. These include for instance quasi-experimental/natural experimental analysis, data mining and machine learning. Recent reviews have demonstrated that these data science terms have corresponding concepts in public health research. This workshop will discuss the use of large routinely collected data in public health with a specific focus on eHRs. The objectives are: to understand the specificity of eHRs in the wider domain of big data; to discuss the challenges imposed when using such data (e.g., data heterogeneity, fragmentation, handling, access, privacy), to discuss how the analysis of big data can assist public health researchers, evaluators and policy-makers; to discuss their advantages and limitations; to outline the methodological, legal and ethical challenges that this entails. Following a brief introduction, the workshop will continue with 5 presentations drawing from a variety of national contexts. The spectrum will spread from countries where eHRs and data linkage have been applied for some years (e.g., Finland, Singapore), to others where development are more recent (France) or even being initiated (Brazil). Together, the presentations will address issues related to the development of big data set, access and training of users and implementation in public health research and practice. The remaining time will be dedicated to interaction with the audience and conclusion. Key messages Access and use of big data in the context of public health practice and research is spreading fast. The challenges ahead consist of maintaining and securing vast amount of heterogeneous health and related data, and in building the capacity to analyse them using old and new analytical approaches.

C-Idea

2019 ◽  
pp. 129-169
Author(s):  
Mickaël Martin-Nevot ◽  
Sébastien Nedjar ◽  
Lotfi Lakhal ◽  
Rosine Cicchetti
Keyword(s):  
Big Data ◽  
Real World ◽  
Business Intelligence ◽  
Just In Time ◽  
Data Cubes ◽  
Improve Efficiency ◽  
Incremental Computation ◽  
Data Context ◽  
Intelligence Approach ◽  
Trend Reversal

Discovering trend reversals between two data cubes provides users with novel and interesting knowledge when the real-world context fluctuates: What is new? Which trends appear or emerge? With the concept of emerging cube, the authors capture such trend reversals by enforcing an emergence constraint. In a big data context, trend reversal predictions promote a just-in-time reaction to these strategic phenomena. In addition to prediction, a business intelligence approach aids to understand observed phenomena origins. In order to exhibit them, the proposal must be as fast as possible, without redundancy but with ideally an incremental computation. Moreover, the authors propose an algorithm called C-Idea to compute reduced and lossless representations of the emerging cube by using the concept of cube closure. This approach aims to improve efficiency and scalability while preserving integration capability. The C-Idea algorithm works à la Buc and takes the specific features of emerging cubes into account. The proposals are validated by various experiments for which we measure the size of representations.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

scholarly journals 4.C. Round table: Joining forces: frameworks for international and multi-sectoral collaborations in health information

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Information ◽  
Population Health ◽  
Round Table ◽  
Secondary Use ◽  
Use Of Data ◽  
National Initiatives ◽  
Information Research

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.

scholarly journals The academic viewpoint on Big data and patient data ownership (as seen in the scientific literature)

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Mircheva ◽  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Medical Research ◽  
Information And Communication Technologies ◽  
Patient Information ◽  
Scientific Literature ◽  
Medical Data ◽  
Patient Data ◽  
Academic Community ◽  
Ethical Challenges

Abstract Background Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully understood. There are not enough publications on the subject. Since the topic is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies, a slightly more sophisticated analysis of the issue is needed. Aim To determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data. Methods Literature search for full text publications, indexed in PubMed, Springer, ScienceDirect and Scopus identified only 27 appropriate articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Three major aspects were studied: scientific area of publications, aspects and academicians' perception of ownership in the context of Big Data. Results Publications are in the period 2014 - 2019, 37% published in health and medical informatics journals, 30% in medicine and public health, 19% in law and ethics; 78% authored by American and British academicians, highly cited. The majority (63%) are in the area of scientific research - clinical studies, access and use of patient data for medical research, secondary use of medical data, ethical challenges to Big data in healthcare. The majority (70%) of the publications discuss ownership in ethical and legal aspects and 67% see ownership as a challenge mostly to medical research, access control, ethics, politics and business. Conclusions Ownership of medical data is seen first and foremost as a challenge. Addressing this challenge requires the combined efforts of politicians, lawyers, ethicists, computer and medical professionals, as well as academicians, sharing these efforts, experiences and suggestions. However, this issue is neglected in the scientific literature. Publishing may help in open debates and adequate policy solutions. Key messages Ownership of patient information in the context of Big Data is a problem that should not be marginalized but needs a comprehensive attitude, consideration and combined efforts from all stakeholders. Overcoming the challenge of ownership may help in improving healthcare services, medical and public health research and the health of the population as a whole.

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