Data-information-knowledge-wisdom Research Articles

The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact

Although there exists a rich literature on data lifecycle, a common framework for data lifecycle depicts reuse as the last stage. However, this framework fails to explain the complex lifecycle of machine learning (ML) data sets, which can have many different afterlives. Data sets for ML can be expanded to supplement previous research, and researchers can concatenate multiple data sets to develop new models. This study discusses ML dataset reuse through the lens of the data–information–knowledge–wisdom pyramid. In social science research, researchers might reuse data to analyse a new research question that is still in the context of the data domain. By contrast, research practices in ML, where researchers layer multiple data sets for training purposes, require us to ask whether the existing data lifecycle model, ending with ‘reuse’, is appropriate for explaining such an iterative and layered lifecycle. This study introduces one case of merging computer vision data set and natural language processing data set and two cases of applying ML models from outside of the ML community (hate speech detection and politeness detection) to justify a framework for a ML dataset lifecycle. Last but not least, this study proposes a ML dataset lifecycle and provides case examples to describe each stage.

A Data-driven Framework to Reduce Diesel Spillages in Underground Mines

Several methodologies have been developed to manage diesel in open-cast mining due to its high demand and increasing diesel prices. Although the use of diesel-powered equipment in underground mines has increased over the years, effective management thereof has not received the same attention. With the advent of Industry 4.0, data can be utilised more effectively by modern businesses to identify and solve problems in a structured manner. In this study, an underground mine was used as a case study to determine whether a Data, Information, Knowledge, Wisdom (DIKW) method for diesel management could be coupled with the Six Sigma Define, Measure, Analyse, Improve, Control (DMAIC) tool to make more informed decisions and gain new insights to help reduce diesel wastage underground. The new integrated methodology identified diesel spillages and highlighted the biggest contributors to these underground spillages. The Six Sigma DMAIC domain utilised root cause analysis to determine the reason for recent systems failures, followed by the identification of practical solutions to eliminate up to 200 ML (megalitres) of diesel spillage. With this information, the case study mine stands to save over USD 175,000 per annum.

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.

From Data to Wisdom: A Review of Applications and Data Value in the context of Small Data

Small data and big data are distinct approaches to data analysis and utilization in various applications. While big data has been the focus of many research and business efforts for more than ten years, small data is increasingly being recognized as having potential value in certain settings. We systematically review literature and conclude that small data can be indeed valuable in certain scenarios. This paper incorporates the data value perspective of small data within various application areas. For this, we apply the data-information-knowledge-wisdom (DIKW) hierarchy to categorize papers and findings, and discuss the papers from the view point of “data value”. Our review identifies various contexts where small data can be used to create value, such as data pre-processing, classification tasks, anomaly detection, forecasting and decision support. We also highlight industries that may be particularly promising areas for practitioners and researchers focused on small data. In addition, we provide an overview of methods and tools for small data analysis, including statistical techniques, visualization, and machine learning algorithms. Finally, based on our results, we suggest, that further research should focus on small data analysis.

Metatheories for Information Literacy

This poster argues for a metatheoretical approach that investigates philosophical questions produced by the difficulties and disagreements within scientific disciplines in a reflexive manner. This approach provides support for a more consistent foundation of IL in conjunction with an in-depth discussion among researchers and more advanced theories and practices in the field. The poster will explore aspects of IL to which metatheory can contribute such as (1) principal concepts relevant to IL, (2) an understanding of how to achieve ‘literacy’, and (3) the integration of the internal and external elements of IL practices. To this end, it adopts a qualitative content analysis of principal frameworks, standards, and models of IL to compare the explicit and implicit metatheoretical—ontological, epistemological, and axiological—assumptions within existing IL theories and practices. This poster will use a ‘case study’ of the conventional data-information-knowledge-wisdom (DIKW) model with regard to disparate conceptualizations and their practical implications.

From Data to Wisdom: A Case Study of OPOP Model

The purpose of this research is to: (1) clarify the scope and connotation of the OPOP (One Product/Project/Performance, One Paper) model comprehensively; (2) show its application in design and creative teaching; (3) introduce this model to more people. First, the author reviews the design doctoral education system and its shortcomings and analyzes the similarities and differences between “Ph.D. of Design” and “Master of Design”; second, the researcher discusses the DIKW (Data, Information, Knowledge, Wisdom) pyramid, cognition and communication theory separately. The author also interviewed the two teachers who created the OPOP model to clarify its core meaning and goals. Finally, the author invited students who participated in an OPOP forum to fill out a questionnaire in order to analyze and understand their responses and suggestions. The OPOP model allows participants to realize that “creativity” and “interpretation” are equally important and to gradually learn to use academic papers to transform personal “tips” and “experience” into “knowledge” that can be imparted. The author hopes that the OPOP model and the general frameworks mentioned in this study can inspire all readers, enabling them to continuously explore the connotation and further possibilities of OPOP in light of the actual situation.

Swarm Differential Privacy for Purpose-Driven Data-Information-Knowledge-Wisdom Architecture

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-source IRIS dataset. This experiment demonstrates the efficiency of swarm intelligence in reducing computing complexity.

Three-way data analytics: Preparing and analyzing data in threes

Three-way decision and its underlying philosophy of thinking in threes have been investigated in a variety of topics. By observing that many three-way topics are related to data analytics, this work presents three-way data analytics, examine a number of its existing topics, and propose a few potential new topics. Based on the two stages of data analytics (i.e., data preparation and data analysis) and the Data-Information-Knowledge-Wisdom (DIKW) hierarchy, we organize the existing three-way topics into three bunches, namely, three-way data preparation concerning data, three-way data analysis concerning information, and three-way data analysis concerning knowledge and wisdom. This systematic organization may shed light on a new view of understanding current results. Moreover, it may promote the studies on the relationships between topics from different levels and inspire their synthesis to obtain three-way approaches to the complete picture of data analytics. Furthermore, we may discover topics that are commonly studied in conventional data analytics but not with three-way approaches. Since three-way approaches are theoretically and practically proved to be efficient and effective in many existing topics, many new topics may be worth investigating. This work presents a few potential new topics, including three-way data selection, three-way word analysis, and three-way data visualization.

Preventing Hospital-Acquired Pressure Injuries by Using a Tiered Protocol in Children Receiving ECMO in the Pediatric Intensive Care Unit.

Preventing Hospital-Acquired Pressure Injuries by Using a Tiered Protocol in Children Receiving ECMO in the Pediatric Intensive Care Unit.

Soil skills challenge: A problem-based field competition towards active learning for BSc. Geoscience students

In this study, we present an interactive problem-based learning environment, namely, the “Soil Skills” contest for final year students majoring in Soil Sciences, a BSc program at Sultan Qaboos University, Oman. This five-day component of a compulsory field-course exemplifies an active learning approach, which has been promoted by higher educators based on its efficacy and outcomes on the student’s learning processes. Our pedagogical strategy integrates the concepts and notions of: (i) Inquiry-Based Learning (IBL), (ii) scaffolding, (iii) teams competition and gaming, and (iv) Data–information–knowledge–wisdom. We evaluated outcome-based learning objectives of two cohorts (2018 and 2020) of Soil Sciences students during the contest via a field evaluation by juries, composed of Soil Science faculty, using an objective evaluation scheme. The evaluation of the contest was also done through a combination of an online questionnaire sent to registered students and peer-assessment among students conducted by the group leaders. The results of teaching evaluation of the course, in the semester when the contest was implemented, showed a higher rating score (3.93/4.0) than the college average (3.16/4.0) for all other courses (about 150/semester) and also than the average score (3.30/4.0) of the course in the previous year. The surveys of two cohorts showed that majority of the students indicated positive improvements in various transferable and interpersonal metacognitive skills (self-reported improvements in problem-solving abilities, thinking out of the box, observation, teamwork, capacity to perform under pressure, and communication). Further surveys of forthcoming cohorts are needed. The Soil Skills pedagogy approach is in line with the call towards enriching geoscience students with the skills and competencies necessary to address real-world problems of complex Earth systems.

The ‘last mile’ for climate data supporting local adaptation

Non-technical summary The ‘last mile’ is a transportation planning term that describes the movement of people and goods from a transportation hub to a final destination; a local place such as a home or a shop. This is the final step of the logistics process that unites the product with its new owner. We present and explain challenges of science-guided adaptation at the local level, and how this is an equivalent ‘last mile’ challenge for climate adaptation. Technical summary The ‘last mile’ issue, a term used in transportation planning, describes the movement of people and goods from a transportation hub to a final destination, a local place such as a home or a shop. This is the critical final step of the logistics process that unites the product with its new owner, and the point of the value chain. This analogy aptly describes the last steps between presenting scientific evidence of climate change to decision-makers for use in local adaptation and planning. Climate change data (observational and model simulation data e.g. climate change projections and predictions) remain under-utilised, especially by local institutions and actors for which adaptation is a priority. The assumptions and assertions of the classical data–information–knowledge–wisdom are challenged, and a derivative form of the information hierarchy is proposed. Elements of the classical information hierarchy are offset by four balancing elements of access (to data); usability (of information); governance (of knowledge) and politics (of wisdom). These balancing elements and their relatedness coincide with newer models of innovation relating to the interaction between different stakeholders across the different levels of governance, the inclusion of stakeholder expectations, transparency and accountability. Social media summary Climate data to wise decision-making in the ‘last mile’: a novel perspective on science-guided local adaptation.

Revising the DIKW Pyramid and the Real Relationship Between Data, Information, Knowledge and Wisdom

This paper offers a critique and reformulation of the data-information-knowledge-wisdom (DIKW) pyramid. Today, collection of personal, business, industrial, and other types of data has never been more pervasive and invasive. Data storage now is measured in yottabytes (56 septillion bits of data) and beyond. This collected data is interrogated, monetized, hacked, and otherwise handled and mishandled around the world at an increasingly rapid pace due to improvements in technology. The interrogated data becomes information, but whether this information is useful or valuable depends entirely on the manner of interrogation and the accuracy of the underlying data. In turn, information could become knowledge but not necessarily, and not necessarily useful knowledge either. Knowledge and wisdom are also closely related, but wisdom typically contains a volume and longevity of collected knowledge and a purpose. Are humans more knowledgeable or wiser for today’s massive amounts of collected data? This paper examines the traditional DIKW pyramid and proposes a revised DIKW relationship based on a Venn diagram to better reflect the relationship between data, information, knowledge, and wisdom.

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context.

The trend towards socialization, personalization and servitization in smart manufacturing has attracted the attention of researchers, practitioners and governments. Social manufacturing is a novel manufacturing paradigm responding to this trend. However, the current cyber–physical system (CPS) merges only cyber and physical space; social space is missing. A cyber–physical–social system (CPSS)-based smart manufacturing is in demand, which incorporates cyber space, physical space and social space. With the development of the Internet of Things and social networks, a large volume of data is generated. A data-driven view is necessary to link tri-space. However, there is a lack of systematical investigation on the integration of CPSS and the data-driven view in the context of social manufacturing. This article proposes a seven-layered framework for a data-driven CPSS (D-CPSS) along the data–information–knowledge–wisdom (DIKW) pyramid under a social manufacturing environment. The evolution, components, general model and framework of D-CPSS are illustrated. An illustrative example is provided to explain the proposed framework. Detailed discussion and future perspectives on implementation are also presented.

Ewolucja i rola pojęcia „treść” (ang. content) w społeczeństwie opartym o informację i wiedzę

In today’s information society and knowledge based economy one of the main development factors is the use of electronic forms of creating and conveying information. However, there is a terminological and management problem with the volume of the processed information as well as the increasing number and variety of forms of presenting and conveying the information. In the last few years the fields of socio-economic sciences as well as technical and engineering studies have encompassed a new keyword – “content”, which in the Polish terminology is usually translated as “treść”. The language of economy, management and IT have lastingly embraced such terms as Content Management, Enterprise Content Management, Content Marketing or the most recent ones – Social Content or Content Governance. Content is an abstract and multi-meaning term whose use, despite its huge popularity, leads to many ambiguities and causes many interpretation problems. The first research question posed in this paper is – for how long has the term content been functioning in our environment? The paper presents the evolution of electronic information forms processed in IT systems for several decades now. The paper offers a broad definition of the term content as well as its most recent form –intelligent content. The second research problem presented in the paper involve the relations between the term content and the classic hierarchical concept – i.e. the Data–Information–Knowledge–Wisdom model (DIKW). The first conclusion is the statement that content is a category that encompasses the following processed in electronic form: data, information and the part of knowledge that can be formalized and recorded, i.e. explicit knowledge. The second conclusion from analyzing the functions played by content in the process of the organizational creation of knowledge is that content is the most important strategic medium in knowledge management within an information and knowledge based society.

Tri-level thinking: models of three-way decision

The underlying philosophy of three-way decision is thinking in threes, namely, understanding and processing a whole through three distinct and related parts. One can formulate many concrete models of three-way decision to account for different interpretations of the three parts. By interpreting the three parts as three levels, this paper investigates tri-level thinking to build concrete models of three-way decision. We examine some fundamental issues and basic ingredients of tri-level thinking. In accordance with the data–information–knowledge–wisdom (DIKW) hierarchy, we present a perception–cognition–action (PCA) tri-level conceptual model that is applicable to studying intelligent data analytics, intelligent systems, and human understanding.

PRINCIPLES OF GOOD DATA

In recent years, there has been an exponential increase in the collection, aggregation and automated analysis of information by government and private actors that disproportionately disadvantages the underrepresented, marginalized and unheard. In response to this there has been significant critique regarding what could be termed ‘bad’ data practices in the globalised digital economy. Considerations of ‘bad data’ practices often only provide critiques rather than engaging constructively with a new vision of how digital technologies and data can be used productively and justly to promote social, economic, cultural and politically progressive goals. In this paper we consider the fundamentals of Good Data to increase trust. We begin by conceptual considerations of the nature of ‘data’ and ‘goodness’. We align our principles with the Data Information Knowledge Wisdom (DIKW) model and use the term ‘data’ as a proxy for the whole DIKW model. Given the limits of our knowledge of moral facts (should they exist) and in light of colonial and post-colonial data practices we assume a hybrid moral theory—where we allow that some moral facts may be objective (e.g. ‘tolerance’ or ‘openness’) and others relative. We advocate an ethic of active seeking, openness and tolerance to diverse views on ‘the good’ particularly consultation with the underrepresented, marginalised and unheard. We go on to defend fifteen principles of good data under four banners: Community, Rights, Usability & Politics in order to ultimately progress a more just digital economy and society.

Generating knowledge from complex data sets in human experimental pain research

Pain has acomplex pathophysiology that is expressed in multifaceted and heterogeneous clinical phenotypes. This makes research on pain and its treatment apotentially data-rich field as large amounts of complex data are generated. Typical sources of such data are investigations with functional magnetic resonance imaging, complex quantitative sensory testing, next-generation DNA sequencing and functional genomic research approaches, such as those aimed at analgesic drug discovery or repositioning of drugs known from other indications as new analgesics. Extracting information from these big data requires complex data scientific-based methods belonging more to computer science than to statistics. Aparticular interest is currently focused on machine learning, the methods of which are used for the detection of interesting and biologically meaningful structures in high-dimensional data. Subsequently, classifiers can be created that predict clinical phenotypes from, e.g. clinical or genetic features acquired from subjects. In addition, knowledge discovery in big data accessible in electronic knowledge bases, can be used to generate hypotheses and to exploit the accumulated knowledge about pain for the discovery of new analgesic drugs. This enables so-called data-information-knowledge-wisdom (DIKW) approaches to be followed in pain research. This article highlights current examples from pain research to provide an overview about contemporary data scientific methods used in this field of research.

Towards a knowledge base to support global change policy goals

ABSTRACTIn 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.

Data, Information, Evidence, and Knowledge:: A Proposal for Health Informatics and Data Science.

In this commentary, I revisit and modify Ackoff’s data-information-knowledge-wisdom (DIKW) hierarchy. I suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge (DIEK). This framework defines data as raw symbols, which become information when they are contextualized. Information achieves the status of evidence in comparison to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by success and consensus. As checkpoints for the transition from evidence to knowledge I suggest relevance, robustness, repeatability, and reproducibility

Investigating wisdom: Call for research

ABSTRACTThis study aims to address the gap in understanding “wisdom” as it relates to Data, Information, Knowledge, Wisdom (DIKW) Model/Hierarchy and the information science discipline. Disciplines like data science, information science, and knowledge management study wisdom without defining it. This study identifies the definition of the term wisdom within the information science discipline.