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Management Support Systems: Towards Integrated Knowledge Management
Abstract
Humans do not apply formalistic scaffolds of fixed rules of ‘knowledge’ to integrate the a priori given objective world of data ‘out there’: they do not compute the world. Regardless of some ‘knowledge’-modeling assumptions, just the opposite is true: humans use their subjectively perceived world of turbulent circumstances to bring forth (create, recreate and adapt), again and again, knowledge as an autopoietic network of relations through which they coordinate their actions. Such knowledge brings (through language) coherence and coordination to the otherwise turbulent and chaotic world of human action. Knowledge is not ‘processing of information’ but a coordination of action. As a consequence, any management support system (DSS, AI, ES, etc.) claiming knowledge as its purpose or its base, cannot be of the symbolic computation type à la Simon.
... The progression from know-nothing, know-how and know-what to know-why in the concept by Zeleny (1987) is the progression from "muddling through", through efficiency and effectiveness to explicability. Reverse sequence of know-why, know-what and know-how by Garud (1997) symbolises the shift from "understanding of the principles underlying phenomena" through "appreciation of the kinds of phenomena worth pursuing" to "understanding of the generative processes that constitute phenomena", respectively. ...
... As mentioned earlier, Bloom's taxonomy for curriculum delivery at higher education institutions is feasible in two modes: the bottom-up traditional approach and the top-down flipped approach. By the same token, the progression from know-nothing, know-how and knowwhat to know-why in the concept by Zeleny (1987) mirrors the reverse sequence of know-why, know-what and know-how by Garud (1997). Such a perspective inspired us to consider the 5Ws from a "traditional" and a "flipped" point of view (Tables 2b-2c). ...
... Thus, in the context of the taxonomy of knowledge by Zeleny (1987), bread as a "cultural object" (Griswold, 2004) is a "construct" built on "data" (elements: H 2 O, yeast bacteria, starch molecules), "information" (ingredients: flour, sugar, water, spices, fixed recipe for bread only), "knowledge" (choose among different recipes for bread), and "wisdom" (Why bread and not croissant?). In doing so, Zeleny (1987) outlines a trajectory from "muddling through" (i.e. ...
With the objectives of the Grünwald Declaration (1983), the Alexandria Proclamation (2006), the UNESCO Paris Agenda (2007) and the concept of media literacy (alias understanding and using mass media in either an assertive or non-assertive way, including an informed and critical understanding of media, the techniques they employ and their effects) on mind, it goes without saying that any communication takes place in a certain context (set of facts and circumstances surrounding a media text for the purpose of its interpretation as defined by Wilson et al., 2011, p. 182). Having media literacy on mind, “know-where” to search for information and “knowwhether” such researched information identifies facts imply in our understanding media and literacy, respectively. Then, know-why corresponds with our perception of the context (Wilson, 2011), and know better conveys “to know or understand the truth about something” (Merriam-Webster, n.d.). The perspective of Haider & Sundin (2022) is the one that the purpose of information literacy is to support people’s knowledge, competencies and resources for their proficient engagement with information (incl. finding, evaluating, producing, and communicating situated information in contextappropriate ways). On the one hand, literacy is a conceptual entity in the context of educational sciences; on the other hand, information literacy (just like media literacy) merely specifies media or information, data, digital, or artificial intelligence (AI) as an entity for literacy to latch onto. In the global survey – addressed to UNESCO networks of Associated Schools and university Chairs in May 2023 slightly over one-tenth of 450 institutions (of which 44% were from Europe) confirmed that they have developed institutional policies and/or formal guidance concerning the use of generative AI applications. Curriculum delivery at higher education institutions adheres to Bloom’s taxonomy (Bloom, 1956), which can be applied in two alternative modes: the traditional approach or the flipped approach, in a variety of cultural backgrounds. The aim of our paper is to map the awareness of media (and information) literacy among higher education students at the University of Economics in Bratislava with instruction either in the Slovak language or in the English language. Findings reveal gaps in recognition of sponsored content just like relatively low awareness of generally respected fact-checking online sites with remarkable discrepancies between the cohort studying in the Slovak language and the cohort studying in the English language.
... • Subsection 3.2 -Presentation of the Marr five levels of system understanding (Baraldi, 2017;, 2018bMarr, 1982;Quinlan, 2012;Sonka, Hlavac, & Boyle, 1994). • Subsection 3.3 -Augmented Data-Information-Knowledge-Wisdom (DIKW) hierarchical conceptualization (Rowley, 2007;Rowley & Hartley, 2008;Wikipedia, 2020a;Zeleny, 1987Zeleny, , 2005Zins, 2007), eligible for use in 'AGI ⊃ CV ⊃ EO-IU ⊃ ARD' tasks. • Subsection 3.4 -Research and technological development (RTD) of big data cube management systems and AGI as closely related problems that cannot be separated. ...
... In colloquial terms, a meta-science is an "applied" science of "basic" sciences (Wikipedia, 2020b) (refer to this Section above). The goal of a meta-science is to transform observations (sensory data, true-facts) of the physical 4D geospace-time real-world, together with knowledge about the world provided by other scientific disciplines, into useful user-and contextdependent information about the world and/or solutions in the world (Couclelis, 2012;Dreyfus, 1965Dreyfus, , 1991Dreyfus, , 1992Fjelland, 2020), in agreement with the increasingly popular Data-Information-Knowledge-Wisdom (DIKW) conceptual hierarchy where, typically, information is defined in terms of data, knowledge in terms of information and wisdom in terms of knowledge (Rowley, 2007;Rowley & Hartley, 2008;Wikipedia, 2020a;Zeleny, 1987Zeleny, , 2005Zins, 2007), see Figure 12. ...
... In the popular DIKW literature where, typically, information is defined in terms of data, knowledge in terms of information and wisdom in terms of knowledge (Rowley, 2007;Rowley & Hartley, 2008;Wikipedia, 2020a;Zeleny, 1987Zeleny, , 2005Zins, 2007) (see Figure 12), Jennifer Rowley defines information as "organized or structured data, which has been processed in such a way that the information now has relevance for a specific purpose or context, and is therefore meaningful, valuable, useful and relevant" (Rowley, 2007;Wikipedia, 2020a). ...
Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.
... To understand the essence of this emerging field, we can utilize DIKW (Data-Information-Knowledge-Wisdom) pyramid theory (Zeleny, 1987;Ackoff, 1989) as a conceptual framework and explore how cognition engineering enables the leap from knowledge to wisdom. Figure 3: The DIKW Pyramid and its relationship to cognition engineering paradigm. ...
... Figure 4 shows the development of artificial intelligence can also be understood as an evolution through distinct engineering paradigms, each representing a fundamental shift in capabilities and applications. This progression perfectly embodies the natural journey from "Data to Wisdom," (Zeleny, 1987;Ackoff, 1989) with each stage expanding boundaries built upon previous paradigms. Cognition engineering represents a critical stage on the path toward AGI, as it addresses the fundamental requirement for deep thinking capabilities that cannot naturally emerge through knowledge accumulation alone. ...
The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling, yet exhibited fundamental limitations in knowledge latency, shallow reasoning, and constrained cognitive processes. During this era, prompt engineering emerged as our primary interface with AI, enabling dialogue-level communication through natural language. We now witness the emergence of "Act II" (2024-present), where models are transitioning from knowledge-retrieval systems (in latent space) to thought-construction engines through test-time scaling techniques. This new paradigm establishes a mind-level connection with AI through language-based thoughts. In this paper, we clarify the conceptual foundations of cognition engineering and explain why this moment is critical for its development. We systematically break down these advanced approaches through comprehensive tutorials and optimized implementations, democratizing access to cognition engineering and enabling every practitioner to participate in AI's second act. We provide a regularly updated collection of papers on test-time scaling in the GitHub Repository: https://github.com/GAIR-NLP/cognition-engineering
... It is widely accepted that both commercial and public organizations that are consciously aware of the significance of being an innovative organization largely invest in their people through both formal and informal learning. Zeleny (1987) diagramed the relative definitions of data, information, knowledge, and wisdom (DIKW) in the following way: ...
... Davenport and Prusak (1998) as well as Mcinerney (2002) agreed that knowledge is closer to action, or actionable information, and increased through interaction with information from other people. Whilest Zeleny (1987) cited that knowledge is the purposeful coordination of action, it implies the capacity of coordinated actions toward some goal or objectives and that the coordinated action is the test of possessing knowledge. Nonaka and Takeuchi (1995) mentioned that knowledge, unlike information, is about beliefs and commitment. ...
The twenty-first century is the century of prosperity and continuous learning. Learning is acquiring, modifying, or reinforcing existing or new knowledge, and involves synthesizing experience, spirit, and passion which leads to individual wisdom. This article studied the evolution of information, knowledge, and wisdom. Although wisdom is considered to be the highest form of knowledge, it is still a sophisticated concept with no consensus definition. The article describes what individual wisdom is, how to develop individual wisdom for active learning, and how to diffuse individual wisdom into organizational wisdom. In addition, this article lists the ways for individual wisdom cultivation, including how to manage individual wisdom for best practices in an organization, and last, but not least, how to contribute organizational values, or return profit to the community for reimbursement which entrepreneurs can utilize as natural resources and public utilities for his own businesses.
... V rámci rozdielnych prístupov v literatúre z oblasti vedomostného manažmentu sa stretávame aj s komplexnejšími prístupmi, rozlišujúcimi informačný proces a vzdelávací proces, pričom v informačnom procese figurujú informácie a znalosti (poznatky) a vo vzdelávacom procese skúsenosti a vedomosti (Vymětal -Diačiková -Váchová 2005, s. 12 -16). Základný koncept práce s týmito pojmami v kontexte vedomostného manažmentu je známy ako vedomostná pyramída 17 alebo tiež pod skratkou DIKW (Data -Information -Knowledge -Wisdom, čiže údaje -informácie -vedomosti -múdrosť) 18 a spája sa s menom českého profesora Milana Zeleného, ktorý svoj pôvodný koncept publikoval ešte takmer pred polstoročím (Zeleny 1987), aj keď už o dva roky neskôr publikoval veľmi podobný koncept (doplnený o fázu porozumenia medzi fázami znalosti a múdrosti) Russell L. Ackoff (1989): ...
... Tab. 3 Porovnanie vedomostnej pyramídy M. Zeleného a R. L. Ackoffa Zeleny 1987 Ackoff 1989 údaje (Data) nevedomosť (Know nothing) symboly informácie (Information) poznatky (Know what) údaje, ktoré sú spracované tak, aby boli užitočné, poskytujú odpoveď na otázky "kto?", "kde?", "kedy?" vedomosti (Knowledge) vedomosti (Know how) použitie údajov a informácií, poskytuje odpoveď na otázku "ako?" porozumenie (Understanding) vyhodnotenie príčin, poskytuje odpoveď na otázku "prečo?" múdrosť (Wisdom) vedomosti (Know why) vyhodnotenie porozumenia osvietenie (Enlightement) dosiahnutie pocitu pravdivosti, uplatnenie zmyslu pre to, čo je správne a čo zlé, čo je spoločensky prijímané, rešpektované či sankcionované Prameň: vlastné spracovanie 15 Paralelne sa používajú tiež názvy znalostný manažment, riadenie znalostí, riadenie vedomostí a pod. 16 Aj keď vedomostný manažment nebýva štandardne zaraďovaný medzi tzv. ...
Monografia je syntetickou interdisciplinárnou autorskou prácou, podávajúcou pohľad na vzdelávaciu politiku zo zorného uhla všetkých relevantných vedných odborov. Nekladie si za cieľ byť koncepčným materiálom pre reformy vzdelávania, aj keď nevylučujeme možnosť, že môže byť inšpiráciou aj pre inštitúcie, zodpovedné za vzdelávaciu politiku. Monografia napriek tomuto svojmu často polemickému zameraniu (a vlastne práve vďaka nemu) je však určená širokej odbornej verejnosti, ktorá sa zaoberá vzdelávaním v rôznych súvislostiach svojho teoretického i praktického záujmu.
... Among the reviewed approaches in cognitive science, we find conceptualizations of cognition as "embodied" (e.g., [74]), "embedded" (e.g., [70]), "enacted" (e.g., [79]) "extended" (e.g., [22]), as well as "mediated" [23] and "material" [51]. Similarly, recent studies in information science and knowledge management do challenge the DIKW pyramid, for instance investigating the role of "implicit knowledge" [27], mapping the coming into being of "information literacy" [57], addressing the This paper has three goals: 1) to outline a critique of the data-information-knowledge-wisdom (DIKW) pyramid ( Fig. 1), which is adopted as a foundational paradigm in information science [2,39] and knowledge management [24,83], as well as-percolating through these disciplinesin applied fields such as geomatics [44,73], computer science [62], computer engineering [36], and data science [52], 2) to propose an information-theory based revisitation of the concepts of data, information, and knowledge, building on the "4Es" view on cognition (cf. [64]), alongside a critical review of recent studies in information science, knowledge management, and semiotics, whose systematization can inform a novel basis for the DIKW paradigm, 3) to advance an ecosystemic reframing of such a paradigm, which can be particularly fruitful for investigating emerging Artificial Intelligences (AI) from an agential perspective. ...
This article takes the lead from the data-information-knowledge-wisdom (DIKW) pyramid which is still a foundational paradigm in information science and applied fields. The goal is three-folded: (1) to outline a critique of such paradigm, with specific regard to its logico-objectivist standpoint; (2) to propose an information theory-based revisitation of the concepts of data, information, knowledge, and wisdom able to move beyond logico-objectivism; (3) to advance an ecosystemic rearrangement among data, information, and knowledge, whose key feature is the balanced entanglement between known and unknown. This will lead to conceptualizing agency as an open-ended information-processing disposition. Such conceptualization can prove useful to reframe discussions around Artificial Intelligence (AI) beyond sentience, emphasizing the epistemological effects that AI has in the world, as well as to advance an understanding of ethics as a relational from-within practice.
... Where is the knowledge we have lost in information?" These questions were taken up in the field of information science, and separately in the field of knowledge management, where the relationships are often expressed visually, with the addition of the concept of data, as the DIKW pyramid, a hierarchical display with data on the bottom, information above it, knowledge above information, and wisdom at the top [24][25][26][27][28]. While limited to specialized scholarly circles during the 1980s, the basic idea was popularized and widely circulated among the digerati around the time that the internet first became a popular phenomenon, in the early 1990s. ...
John Henry Newman defined the university as “a place of teaching universal knowledge”, which suggests that it is also an environment for the teaching and creation of knowledge, and therefore a medium for the teaching and creation of knowledge. Based on the field of media ecology, defined by Neil Postman as “the study of media as environments”, and following Marshall McLuhan’s famous maxim that, “the medium is the message”, we can understand knowledge to be the product of a particular type of medium or environment. Taking inspiration from the poetic questions posed by T.S. Eliot, “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”, this essay takes issue with the view expressed among internet boosters that information is the basis of knowledge, and knowledge is the basis of wisdom. Instead, an alternative understanding presented in which information as a contemporary phenomenon is a product of the electronic media environment, knowledge is a product of the literacy associated with the chirographic and typographic media environments, and wisdom is a product of the oral media environment.
... This conveys essential information to viewers, and information visualization is an outcome of interdisciplinary integration. Zeleny (1987) proposed the Data Information Knowledge and Wisdom Hierarchy (DIKW) model, which categorizes information into four hierarchical levels: data, information, knowledge, and wisdom. Lai (2001) discussed the concept of information regarding information communication in which emphasis is placed on the essence, characteristics, and classification of information for upstream information dissemination. ...
This study focused on bird species in Taiwan and used content analysis to explore the presentation of ecological information across three media: two-dimensional catalogs, infographics, and smart phone apps. The results indicated that (1) illustrations and icons can be used to improve the usability of media and users' ability of recognize bird species. (2) The use of graphic symbology helps users search for information, and graphic rendering can be used to enhance the visual characteristic of birds. (3) Designers can introduce adequate functions and interface designs to improve user convenience when browsing and recording bird characteristics. From the study results, an ecological app for birds in Taiwan will be developed in subsequent research to promote ecological awareness and disseminate information.
... This conveys essential information to viewers, and information visualization is an outcome of interdisciplinary integration. Zeleny (1987) proposed the Data Information Knowledge and Wisdom Hierarchy (DIKW) model, which categorizes information into four hierarchical levels: data, information, knowledge, and wisdom. Lai (2001) discussed the concept of information regarding information communication in which emphasis is placed on the essence, characteristics, and classification of information for upstream information dissemination. ...
This study focused on bird species in Taiwan and used content analysis to explore the presentation of ecological information across three design forms: two-dimensional catalogs, infographics, and smart phone apps. The reliability test conducted by two coders yielded a reliability score of 0.99, 0.97, and 0.98 for two-dimensional catalogs, infographics, and smart phone apps, respectively. These results imply that (1) 2D catalogs present the greatest amount of information with the highest information complexity, followed by smart phone apps and infographics. (2) The use of pictorial illustration comprehensively displays the visual elements; the use of graphic symbology helps users search for information, and graphic rendering can be used to enhance the visual characteristic of birds. (3) Designers can introduce adequate functions and interface designs during app development to improve user convenience when browsing and recording bird characteristics.
... To better clarify the notion of knowledge within the DIKW hierarchy, we begin by reporting the interpretation of value associated with each stage in Table 1 (Zeleny 1987;Lamba and Dubey 2015). ...
The diffusion of AI and big data is reshaping decision-making processes by increasing the amount of information that supports decisions, while reducing direct interaction with data and empirical evidence. This paradigm shift introduces new sources of uncertainty, as limited data observability results in ambiguity and a lack of interpretability. The need for the proper analysis of data-driven strategies motivates the search for new models that can describe this type of bounded access to knowledge.This contribution presents a novel theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We provide a dynamical description of knowledge states by endowing our model with a structure to compare and combine them. Specifically, an update is represented through combinations, and its explainability is based on its consistency in different dimensional representations. We look at inequivalent knowledge representations in terms of multiplicity of inferences, preference relations, and information measures. Furthermore, we define a formal analogy with two scenarios that illustrate non-classical uncertainty in terms of ambiguity (Ellsberg’s model) and reasoning about knowledge mediated by other agents observing data (Wigner’s Friend). Finally, we discuss some implications of the proposed model for data-driven strategies, with special attention to reasoning under uncertainty about business value dimensions and the design of measurement tools for their assessment.
... Practical wisdom can be understood as a know-why resource that guides knowledge processes and strengthens organisational effectiveness (Ackoff, 1989;Nonaka et al., 2014;Zeleny, 1987). It is action-orientated and helps choose both the means and the goals in a way that serves the greater good (Ames et al., 2020;Queiroz, 2012). ...
To tackle grand societal challenges and make decisions that serve the common good, we need well educated business successors who will become the practically wise entrepreneurs of tomorrow. This paper presents a pioneering educational framework, i.e., the WiseUp Cube framework, to enhance the decision-making skills of business successors through the cultivation of practical wisdom (phronesis). Emphasising practical wisdom as central to sustainable and responsible decision-making, the study provides a novel perspective for contemporary entrepreneurial education. It addresses a critical gap in management and entrepreneurship education literature by focusing on business successors, an often-neglected target group, whose training and education are pivotal for sustainable business practices. The educational framework targets six practical wisdom skills for critical decision-making. It integrates pedagogical, psychological and philosophical aspects and offers six learning perspectives to enhance these skills. In addition, pedagogical tools for implementing the learning perspectives are named. The study contributes to the advancement of entrepreneurial education. It extends the Responsible Management Education agenda to Vocational Education and Training (VET), an area that has received too little attention in this respect, but which offers immense potential for tackling grand challenges. Moreover, it pioneers the translation of the conceptual understanding of the Aristotelian notion of phronesis into a practical framework for education. It serves as a valuable template for educators involved in the training of business successors and offers guidance for the design of curricula and educational practices targeting this distinct cohort.
... To better clarify the notion of knowledge within the DIKW hierarchy, we begin by reporting the interpretation of value associated with each stage in Table 1 [30,31]. With reference to this chart, the patterns supporting decision-making should be interpreted or explained in line with a dimensional measurement of the value generated through actions. ...
The diffusion of AI and big data is reshaping decision-making processes by increasing the amount of information that supports decisions while reducing direct interaction with data and empirical evidence. This paradigm shift introduces new sources of uncertainty, as limited data observability results in ambiguity and a lack of interpretability. The need for the proper analysis of data-driven strategies motivates the search for new models that can describe this type of bounded access to knowledge. This contribution presents a novel theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We provide a dynamical description of knowledge states by endowing our model with a structure to compare and combine them. Specifically, an update is represented through combinations, and its explainability is based on its consistency in different dimensional representations. We look at inequivalent knowledge representations in terms of multiplicity of inferences, preference relations, and information measures. Furthermore, we define a formal analogy with two scenarios that illustrate non-classical uncertainty in terms of ambiguity (Ellsberg's model) and reasoning about knowledge mediated by other agents observing data (Wigner's friend). Finally, we discuss some implications of the proposed model for data-driven strategies, with special attention to reasoning under uncertainty about business value dimensions and the design of measurement tools for their assessment.
... This pyramidal hierarchy was introduced by Sharma (2004), who provided the framework for additional discussions in the domain of Knowledge and Information Management sciences. It ought to be underscored that Sharma (2004) just consolidated and structured the already available information, referring to the works of Zeleny (1987) and Ackoff (1989) in this subject. Figure 2.1 represents the so-called "Knowledge Pyramid", which demonstrates the links between the discussed ideas. ...
Explore the transformative role of web scraping in mitigating information asymmetry within e-commerce through this scholarly volume. With a focus on the automotive sector, particularly the prominent OTOMOTO portal in Poland, this book provides a nuanced examination of automated tools designed to empower market participants. Tailored for researchers, academics, and data scientists, as well as legal professionals and policymakers who may find valuable perspectives on the regulatory landscape surrounding web scraping and its implications for information transparency. Emphasizing the imperative of informed decision-making, this work serves as a comprehensive resource for understanding and harnessing the potential of web scraping in the digital marketplace.
... Wallace (2007) outlines that it is not certain to whom should be attributed the first presentation of the 'data-to-information-to-knowledge-to-wisdom transformation' pyramid, as this 'hierarchical arrangement has been a part of the language of information science for many years ' (13). Notwithstanding, the author credits the work of Adler (1970) for an early implied hierarchy, Zeleny (1987) for solidifying its structural cogency, Ackoff (1989) for augmenting the pyramid through the addition of 'understanding', and Debons et al. (1988) for added complexity within its first graphical representation (Wallace 2007). ...
This brief commentary addresses the limitations of the DIKW data-to-wisdom transformation model as relates to its utility in the age of AI. Calling for both old and new theories of knowledge and wisdom to bring about a human-centered concern for the inextricable progression toward human-computational wisdom.
... Determination of the best treatment for roasting robusta coffee beans is determined by the Multiple Attribute methods (Zeleny, 1987). The parameters used in determining the best treatment are the lowest water content (%), highest ash content (%), lowest caffeine content (%), highest total phenol (%), and lowest pH. ...
Roasting is a process that contributes to the formation of compounds and flavors in coffee beans. Temperature and length of time are the main factors in the roasting process. Generally, the range of temperatures and roasting times varies significantly for different varieties of coffee beans. This study aims to determine the effect of roasting on changes in the chemical characteristics of robusta coffee beans (Coffea canephora) from Sidomulyo Village. This study used different brewing methods to brew roasted robusta coffee beans with the best chemical characteristics used a factorial randomized block design with two factors: roasting temperature, which consisted of three levels (185, 190, and 195 ⁰C), and roasting time, which consisted of three levels (10, 13, and 16 minutes). The roasted coffee beans were analyzed for water content, ash content, caffeine content, total phenol, and pH. The results of this study obtaineda water content value of 3.523 ± 0.129% to 1.939 ± 0.025%, ash content of 8.119 ± 0.115% to 4.315 ± 0.260%, a caffeine content of 2.494 ± 0.015% to 2.176 ± 0.021%; total phenol of 6.251 ± 0.101% to 4.334 ± 0.117%; and a pH value of 6.675±0.126 to 4.075±0.171. At this stage, the best treatment (Zeleny method) is produced by robusta coffee beans roasted at 185⁰C for 10 minutes. Robusta coffee beans with the best treatment have a moisture content of 3.523 ± 0.129%; ash content of 4.315 ± 0.260%, the caffeine content of 2.494±0.015%; total phenol of 6.251±0.101%; and a pH value of 4.075±0.171. Key words: Coffea canephora; roasting; caffeine; phenol.
... It includes the issue of oversampling and under-assessing that ought to be tended to appropriately. Class inconsistency circumstance is a test in using machine learning computations as a gadget for expecting [26][27]. ...
The employability of students is an important concern for colleges nowadays, and forecasting it can assist institutions take prompt action to improve the institutional placement ratio. The most effective data mining methods for forecasting students' employability are categorization methods. Students can concentrate on areas where they need to improve to better match the company's skill set by being aware of their deficiencies prior to an interview. Also, forecasting student employability might assist academic staff in developing curricular plans. In order for kids, parents, guardians, organisations, and businesses to profit to some extent, this report offered a fresh, futuristic roadmap. Out of seven trials, the first five were carried out using in-depth statistical calculations, while the latter two were carried out using supervised machine learning techniques. On just one extreme, the Support Vector Machine (SVM) had the highest accuracy rate of 93% when it related to forecasting employment status. The Random Forest (RF), on the other hand, was able to identify the gender of placed kids with a maximum accuracy of 89%. It is also advised to determine the placement of gender and placement status using a number of key criteria. A statistical t-test with a significance threshold of 0.05 demonstrated that the student’s gender had no impact on the pay that was provided during job placement.
... Niektórzy charakteryzują tę nieużyteczną cechę danych jako nieznaczącą (ang. know-nothing) [9]. W niektórych przypadkach dane są rozumiane jako odnoszące się nie tylko do symboli, ale także do sygnałów lub bodźców, do których odnoszą się wspomniane symbole. ...
Proces inwestycyjno-budowlany w Polsce przechodzi głęboką
cyfryzację. W wielu fazach i na różnych etapach tego procesu
wykorzystuje się BIM. W przestrzeni naukowej i biznesowej często
mówi się o „modelu informacyjnym budynku”. Słyszy się frazy
typu „potoki danych”, „faszerowanie informacją”, „strukturyzowane
dane”, „otwarte standardy wymiany danych” itd. W wielu
przypadkach pojęcia „dane” i „informacje” stosuje się zamiennie,
nie zastanawiając się nad ich znaczeniem w kontekście BIM.
Przez to są mylone i czasami prowadzą do błędów poznawczych
czy problemów w komunikacji. Niezależnie od roli (projektant, inwestor,
producent) znajomość obu pojęć i związanych z nimi standardów
jest niezbędna tam, gdzie w procesach pojawia się BIM.
W artykule dokonano głębokiego przeglądu literatury pod kątem
stosowania i znaczenia obu pojęć. Przedstawiono je w konkretnym
studium przypadku w celu lepszego zrozumienia ich definicji.
W artykule podkreślono też znaczenie norm i standardów, które
wyraźnie wskazują jak pracować z danymi i informacjami w BIM.
... The world henceforth will be run by synthesisers, people able to put together the right information at the right time, think critically about it, and make important choices wisely'. Organisations must act wisely on intelligence rather than data, information or knowledge [12][13][14][15][16][17][18][19]. These latter have increasingly lost prominence to intelligence in sustaining organisational performance over the last few decades [20]. ...
The Competitive Intelligence (CI) construct must be scientifically
defined, characterised, empirically validated, and accurately measured to
grow in science and business. This study aims at elevating the accuracy
of the empirical validation of the CI construct suggested and confirmed
by Madureira, Popovic, & Castelli1,2 to serve as the scientific foundation
for CI praxis. This construct is selected due to its unmatched recency,
thoroughness, and universality and identified limitations of its empirical
validation. We relied on a multistrand design of fully sequential with
equivalent status qualitative and quantitative mix-methods followed by
the triangulation of the findings and the development of the metainferences.
Validity, reliability, and applicability were tested using
computer-aided text analysis and artificial intelligence methods based on
61 in-depth interviews with CI subject matter experts. Contributions to
knowledge advancement and relevance to practice derive from the
scientific grade empirical construct validation, providing undisputed
levels of content, discriminant, external accuracy, reliability and
triangulation of results. This study highlights three critical implications.
First, the delimitations of the body of knowledge and recognition of the
CI domain serve as the baseline for theory development. Second, the
validated construct guarantees reproducibility, replicability and
generalisability, laying the foundations for establishing a CI science,
practice and education. Third, creating a common language and shared
understanding will drive the much-claimed definitional consensus. This
study thus stands as a foundational pillar in supporting CI praxis in
improving decision-making quality and the performance of organisations.
... The new business models are considering the best way to achieve sustainability and a long-term run. CRMCs support different emphasizing processes so as to spread new information and knowledge in enterprises to make new products and adopt technologies [23]. CRMCs can uphold businesses to maintain and organize information to send to the correct people for a quick understanding and to share it with pertinent users [16]. ...
This research aims to investigate how customer relationship management capabilities (CRMCs) improve customer engagement and innovation performance. Customer engagement is also tested as a mediator between CRM capabilities and innovation performance. Furthermore, the distrust moderating role was studied towards the relationship between CRMCs and customer engagement. Data were collected from 491 owner/managers of SMEs working in Saudi Arabia. The findings revealed that CRMCs positively predict customer engagement and innovation performance. Customer engagement directly affects innovation performance and acts as a mediator as well. In addition, the findings proved that distrust moderates on the link between CRMCs and customer engagement. This research adds to the marketing management theory and practices and highlighted the role of CRMCs in achieving innovation performance. It explored, considering the data base collected from SME owners and managers, the mechanism of innovation performance achievement through CRMCs and customer engagement. CRMC improvements allow businesses to have sustainable development and a better integration in the market.
... Ackoff (1989) defined a hierarchical model with data (symbols) as observations, information as processed data, knowledge as the application of information, and wisdom as values and the exercise of judgement. Zeleny (1987) added "enlightenment," creating the DIKWE chain. This includes "know-nothing," "know-what," "know-how," "know-why," and "know yourself." ...
STEM education has become vital to equip the next-generation knowledge workers for Industry 4.0 world. STEM education refers to a curriculum based on the teaching of science, technology, engineering and mathematics, aiming to prepare students with critical thinking, collaboration, communication, and creative thinking (4C) abilities. STEM education and its associated industry market have tremendously grown in the past decade. Developing knowledge management skills and understandings are equally critical in equipping lifelong knowledge workers. The knowledge, skills, attitudes, and abilities to identify, search, analyse, apply and disseminate information and media products are essential. However, knowledge management-related education programs, curricula or frameworks for K-12 education still need to be made available. This article examines the common characteristics of STEM and KM education, investigates the current practice of KM in STEM education in Hong Kong, and proposes a STEM curriculum with KM learning elements for K-12 education.
... Confucius (Tsai,214), Plato (Copleston, 1993; Ch. XIX), Dewey and Bentley (1949), Polanyi (1966), Drucker (1959), Zeleny (1987), Ackoff (1989), Nonaka (1991), Wiig (1993), Davenport and Prusack (1998), Zins (2007), Wallace (2007) and Rowley (2007) are just some among the ones presenting significant points of view about this term. Although there are many aspects of knowledge, and numerous commonly linked terms, such as understanding, intelligence, experience, information and wisdom could have been discussed, this study does not intend to introduce deeper the map of connected terminology, which is many times overlapping. ...
Almost all the scientific fields transfer heritage knowledge on their own way because science itself is a mean of heritage. Knowledge management gives some models and methods for understanding how intangible elements of our culture and heritage can be transferred, including that knowledge which let us understand, respect, and appreciate the tangible and intangible heritage elements. The framework this chapter offers the reader the attributes of knowledge communities, as well as the explanation of the challenges heritage communication have to face: how to keep alive the communication channels and processes. Disappearance of languages, forgotten code and symbol systems, low capacity, and motivation for acceptance of new knowledge, the natural process of forgetting, as well as the authentication of the knowledge owner and the knowledge to be shared are just a few to be discussed to make the readers understood how heritage communication can be effective and efficient.
... The information systems field has been facing similar challenges of extracting knowledge from data for several decades. The common four level hierarchy of knowledge extraction is, from bottom to top: Data, Information, Knowledge and Wisdom (DIKW), known as the DIKW hierarchy, the "Knowledge Pyramid" and the "Information Hierarchy" (Ackoff, 1989;Rowley, 2007;Zeleny, 1987). At the lower levels of this pyramid (data and information), mainly automatic quantitative tools are used to extract the relevant content, which is required for decision-making. ...
This study examines the potential of big data discourse analysis (i.e., culturomics) to produce valuable knowledge, and suggests a mixed methods model for improving the effectiveness of culturomics. We argue that the importance and magnitude of using qualitative methods as complementing quantitative ones, depends on the scope of the analyzed data (i.e., the volume of data and the period it spans over). We demonstrate the merit of a mixed methods approach for culturomics analyses in the context of identifying research trends, by analyzing changes over a period of 15 years (2000-2014) in the terms used in the research literature related to learning technologies. The dataset was based on Google Scholar search query results. Three perspectives of analysis are presented: (1) Curves describing five main types of relative frequency trends (i.e., rising; stable; fall; rise and fall; rise and stable); (2) The top key-terms identified for each year; (3) A comparison of data from three datasets, which demonstrates the scope dimension of the mixed methods model for big data discourse analysis. This paper contributes to both theory and practice by providing a methodological approach that enables gaining insightful patterns and trends out of culturomics, by integrating quantitative and qualitative research methods.
... Bases de datos, reportes, manuales, patentes y conocimiento formalmente documentado en los sistemas de información. Fuente: Zeleny (1987) Una vez que el conocimiento es interiorizado, la organización requiere hacerlo accesible a las personas. Los miembros de la organización necesitan saber qué conocimiento existe en la organización y cuáles de sus propias experiencias deben ser transferidas para ayudar a otros. ...
El cambio constante, las crisis en diversas esferas y la incertidumbre económica motivan el desarrollo de estudios para promover el trabajo interdisciplinario para acelerar las soluciones en la atención a la salud. En este contexto el objetivo de esta investigación es comprobar empíricamente la validez de una estructura teórica de gestión del conocimiento para la investigación traslacional, para ayudar a entender mejor la innovación en salud, sin sacrificar las divergencias de pensamiento o el aporte a la investigación básica. Por medio de la metodología del estudio de caso, se integraron los resultados de la revisión sistemática de la literatura para realizar entrevistas semiestructuradas a investigadores de un Instituto Nacional de Salud en México. Aún con las limitantes del uso de un único caso, el caso posee como atributos el estudio en profundidad de un fenómeno complejo y poco conocido. Como resultado se identificaron las áreas de oportunidad para la gestión del conocimiento para cada una de las etapas de la investigación traslacional, identificando como futuras líneas de investigación el diseño de prácticas para cada etapa.
... Bilgelik kavramının işletme yönetimi konusu içerisinde incelenmesi ise oldukça yenidir ve hala çok sıklıkla araştırılan ve tartışılan bir konu değildir. Bilgelik kavramı, ilk olarak 1987 yılında Milan Zeleny tarafından bilgi yönetiminin bir sonraki aşaması olarak ele alınmıştır (Zeleny, 1987). Yönetim alanında bilgelik ile ilgili ilk kapsamlı kitap, Robert J. Sternberg Ardelt'e (2011) göre henüz genel kabul görmüş bir bilgelik tanımı bulunmamakla birlikte, bilgeliğin "çok yönlü ve çok boyutlu" olduğu ve bu çeşitli boyut ve boyutların karşılıklı olarak birbirini güçlendirdiği ve desteklediği konusunda araştırmacılar arasında bir tür toplu anlaşma vardır. ...
The subject of wisdom has been an important topic in philosophy throughout the ages, and many scholars have written books and articles on the subject of wisdom. Although its origins are in philosophy, other disciplines such as psychology and management have also studied wisdom. Decision making is a major management task that is very important because the success and failure of an organization depends largely on management decisions. Despite the vital role of decision-making and the growing interest in wisdom in management, the management literature is still lacking in research that provides a clear understanding of how decision-making in management by utilizing wisdom. In this direction, in the light of technological developments, it is seen that there are many obstacles to the implementation of decision support systems and the use of tools. The main purpose of the study is to use the dimensions of wisdom management in addressing unstructured decisions and obstacles in the implementation of decision support systems. In the first part of the study, the concepts of wisdom and organizational wisdom, in the second part, the concepts of decision support systems, and in the third part, the concepts of business intelligence are discussed in detail. In the fourth section, the findings of the survey conducted for Turkish and Syrian companies operating in the information technologies sector in Gaziantep are shown. The fifth part of the research covers the evaluations, discussions and suggestions about the research part.
Preventing data leakage and monitoring information assets are crucial to safeguarding sensitive information within organizations. However, these practices must be approached with careful consideration for employees’ fundamental right to privacy. This article delves into the balance between data protection measures and the rights of employees to privacy in the workplace. This study navigates the complex terrain of data security by examining current best practices in security monitoring and ethical data privacy considerations. Effective data leakage prevention strategies should align with the principles of transparency and accountability, ensuring that security measures do not infringe upon individual privacy rights. By exploring the ethical dilemmas and practical challenges inherent in data protection practices, this article aims to contribute to understanding how organizations can maintain robust security measures while respecting employees’ right to privacy in the modern workplace.
The significance of information in companies, businesses, and organizations is now widely recognized. Information serves as the foundation from which knowledge and corporate experience are derived. Managers are entrusted with guiding organizations toward their objectives by making informed decisions. As a result, organizations increasingly prioritize access to data and information to gain a competitive edge. While the importance of technology is acknowledged, so too is its complexity. In this chapter, we will explore how a well-informed decision-making process supported by effective information management can serve as a crucial tool for controlling corporate risk.
Purpose
This study aims to present a qualitative analysis of how higher education institutions (HEIs) faculties manage knowledge to facilitate the learning and engagement of individuals with intellectual and developmental disabilities.
Design/methodology/approach
This study uses a qualitative research design to collect responses from 39 HEI faculties to understand how they manage knowledge to facilitate learning in individuals with intellectual and developmental disabilities. Data collection tools comprised a set of predetermined questions, soliciting written responses.
Findings
Coding of the collected data confirmed that there was a knowledge management process in HEIs for enabling higher education of individuals with intellectual and developmental disabilities. A total of six themes emerged, clarifying the knowledge management process. The four components of this process were creation, storage, sharing and use. In addition to the four process-related themes, two other themes that emerged were barriers to knowledge creation and supportive culture. This covers the relevant aspects of the set-up around the knowledge management process in HEIs trying to improve the higher education of individuals with intellectual and developmental disabilities.
Originality/value
There is a need to improve the education of individuals with intellectual and developmental disabilities, which requires effective knowledge management. This paper reveals details of the steps in the knowledge management process relevant to this aim. This is a unique contribution, providing a basis for future research and the introduction of required knowledge management practices by HEIs dedicated to providing high-quality education to students with intellectual and developmental disabilities.
This article is to provide the information about the python that who it is going to be used in Advanced system and making day by day new machines. As python was just a programing language in first and used to short the android and other languages and later on now its taking control all over the world by hand shaking with other elements like Machine Learning(ML) Systems and Artificial Intelligent(AI) systems, with the help of many Algorithms and tools technology they are overcoming to every phase of life but the strange part is that now the simple data is going to the changed in knowledge by using this python. As It is well known that history is well predictor of future and by taking data from past we are predicting future using this Advanced language in the mention below section have some knowledge about this technique.
Digital culture in an organization is the relationship between employees and the use of technology. Embracing digital has now become a mandate for any organization to be successful and it is imperative especially for lean organizations to develop a digital & data mindset to remain competitive in the marketplace. Digital tools help organizations save time and money while improving communication, driving productivity, recruiting top talent, giving employees the ability to work from anywhere, providing information that can influence business decisions, and more. The paper identifies four major components that can help make digital transformation a success in lean & agile Organizations. A top down approach, strategy over technology mindset, a culture that encourages innovation, risk taking and optimizing operational processes are the most important elements that can drive digital transformation. Additionally, upskilling employees & encouraging them to take ownership of their growth lies at the core of making a digital culture sustainable. A case study of initiatives by several companies toward digital transformation has been discussed to design the best possible strategy that can drive digital culture in lean & agile organizations. Executive support, removing the concept of silos, breaking the stereotype around failure & risk aversion are a few of the factors that have been identified.
Jakość informacji we współczesnej gospodarce nabiera coraz większego znaczenia. Jednymi z istotniejszych jej źródeł są systemy rachunkowości dostarczające informacji finansowej, której jakość zależy od wielu czynników. Badania naukowe wskazują na istnienie wielu determinant zarówno po stronie wielkości sprawozdawczych przedsiębiorstw, jak i po stronie cech charakterystycznych czy uwarunkowań makroekonomicznych, kulturowych oraz instytucjonalnych i prawnych. W pracy autor skupił się na wynikach badań wpływu stosowania MSR/MSFF oraz wykazaniu potencjalnych innych czynników, determinujących jakość informacji finansowej prezentowanej przez przedsiębiorstwa.
The still ongoing development of the knowledge and information society resulted in a substantial increase in the importance of knowledge work. While advancing digitalisation in many areas has led to the automation of business processes, faster communication and improvements in personal working conditions, the technological, cultural and organisational support of knowledge workers dragged behind this progressive development. In particular, there is a lack of support for structured, concise and precise communication in knowledge-intensive, data-driven processes and their integration into administrative business processes.
This thesis takes up the concept of mental models for the analysis of observable communication data and, using the example of industrial property protection, in particular patent prosecution, proposes a modelling approach that supports the externalisation of mental models, contributes to the formation of shared mental models and collective knowledge and enables the identification of highly dynamic, variable sequences in a bottom-up approach. As a result, a domain model, comparable to a map, is created that can be used for planning and forecasting and especially for supporting users in knowledge-intensive, team-oriented activities.
Based on this modelling approach, solutions were implemented, concepts for user interfaces were developed, and several case studies were conducted. Especially the feedback collected during the case studies suggests that the approach developed in this thesis has a lot of potential regarding its application in different knowledge-intensive work areas.
Background : Digital twins (DT) are the coupling of a real-world physical asset to a virtual representation to provide insight and actionable knowledge. The benefits of DT are considered to include improvements in reproducibility, reliability of interventions, increased productivity, as well as increased time for innovation. For instance, a DT could be used to boost agricultural productivity whilst also meeting various targets (e.g., biodiversity, sustainability). Or a DT could be used to monitor a cell culture, predict interactions, and make subtle adjustments to maintain the environment allowing researchers to conduct other work. Yet in developing DT two fundamental questions emerge: ‘What will the DT capabilities be?’ (i.e., the range of features and possible actions) and ‘What will the DT do?’ (i.e., which capabilities will it utilise).
Methods : Here we discuss a theoretical framework for DTs developed during Wageningen University & Research’s Investment Programme on DTs that aims to answer these questions. Focusing on the Life and Environmental Sciences to help developers and stakeholders to agree on the capabilities, purpose, and goal of a DT. As well as identifying iterative design stages that may help set interim development goals such as a minimum viable product.
Results : This framework defines a DT as sitting at one of five maturity, or capability, levels associated with specific types of DT: a status, an informative, a predictive, an optimisation, and an autonomous twin.
Conclusions : The aim of DTs is to make better, data-driven, decisions yet there can be a gulf between expectations of what a Digital Twin will do and the reality. The five maturity levels outlined here can be used to first identify and communicate about the type of Digital Twin required for a particular project prior to DT development. Bridging the gap between what project leads, developers, and stakeholders envision the end-product will be.
Nowadays, artificial intelligence and related technologies have been widely applied in many areas of people's daily lives, while also being mutually integrated and adapted with laws, morality, and ethics. In the real-world scenario of filling out forms, there are various types of stakeholders involved, which involve legal ethics, equity exchange, and transfer situations, resulting in a game between form makers and form fillers. From the perspective of the form fillers, common problems may arise such as repetitive or incorrect entries, inconvenience, information leakage, or conflicting purpose. The form makers may have their own purpose, but due to their lack of understanding or disregard for legal and ethical content, discrimination and bias may exist in the data, information, and knowledge aimed at different form fillers. Furthermore, existing automatic form-filling technologies are limited by inconsistent, incomplete, inaccurate, and insufficient content resources, and different stakeholders have cognitive differences, which leads to the AI form-filling decision process being limited to simple data or information transfer and facing issues of reliability and interpretability.
This article discusses the reliability of AI automated decision-making in the above situations. Based on the DIKWP theory and technology, the purpose system is used to drive the fusion analysis of the content-cognition system of all parties in the intelligent form-filling interaction process. This is done to address the specific issues of inconsistency, incompleteness, inaccuracy, and insufficiency of the obtained content resources (F-N problem). At the same time, a form-filling interaction governance evaluation model is constructed, aiming to responsibly evaluate the compliance utility, privacy security, and fairness of the form, and to improve the reliability and credibility of automatic form-filling decision-making. The main work and innovation points of this article are as follows:
First, in response to the F-N problem of content resources and the cognitive differences of the research object, a homogenization analysis is conducted on the object's DIKWP system in the form-filling interaction process. A cross-domain linkage and transformation mechanism is established to reduce the uncertainty of the content, while also constructing essential content alignment between different systems, making essential existence judgments, analyzing content consistency, and establishing semantic structure correlations to mitigate the differences issue.
Second, the construction of the purpose model of the form-filling interaction object is described in detail. An analysis is conducted on the relationships within a single purpose system and between multiple purpose systems, and a competitive handling mechanism is designed to establish priorities. Additionally, a form-filling interaction value model is constructed, and DIKWP logic is used to redefine and analyze possible biases in the form-filling scenario. At the same time, a governance evaluation system for the form is constructed and supplemented, and specific evaluations are carried out based on compliance utility, privacy security, and fairness and impartiality.
Third, A DIKWP-based combination search and ranking strategy is designed using the DIKWP framework, which is based on purpose sequences. The form's multi-purpose search process is modeled as a continuous topological structure on the user's content-cognition map, and search efficiency is improved through purpose association priority search and partial order constraint subgraph pruning search. In the personal information form-filling scenario, content sorting and filling are carried out based on user purpose constraints and subjective wisdom value judgment, achieving the reliability of automatic form-filling.
At first glance, data science and entrepreneurship are two seemingly unrelated fields of scientific research. In this introductory chapter of the book, we nonetheless try to bring together both disciplines, for which we introduce the concept of data entrepreneurship. We first discuss a number of well-known definitions of both data science and entrepreneurship, we disentangle them, and then we delineate striking differences and important similarities. We move on by discussing a number of prominent process models of both data science and entrepreneurship and again point at key differences and similarities in these typical processes. Both endeavors ultimately result in a conceptual framework that also forms the basis for the remainder of this book, which consists of sections on data engineering, data analytics, data entrepreneurship, and data and society, respectively.KeywordsData scienceEntrepreneurshipData entrepreneurshipProcess modelsData entrepreneurship frameworkData engineeringData analyticsData and society
Ontology-based mappings in knowledge graphs are a widely discussed topic in biomedical research. Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Classical approaches use RDF triple stores, which have serious limitations. Here, we introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept based on biomedical literature and text mining as a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain specific language data using BEL. Here, context is a more general concept than annotations. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.
Data and Knowledge Management, sometimes also called Information Management, is a core topic of Data Engineering and Data Mining. It is also an interdisciplinary field, touching economics (how efficient and expensive is the solution?), psychology (does one use this solution in a way that was intended?) and, of course, informatics. This chapter offers a theoretical overview on Data and Knowledge Management and thus provides a theoretic foundation for the following parts of this book. Moreover, if you implement or plan a solution in the field of data mining or data engineering, carefully consider the information given here. In other words: Besides the theory, this chapter provides a technical blueprint.
Abstract
Nowadays, artificial intelligence systems have been widely used in various fields and have made many achievements, however, problems such as algorithm bias, discriminatory decision results, and leakage of privacy resources also arise, leading to damage to the fairness, security, and autonomy rights of Internet users. At the same time, laws related to the use and governance of artificial intelligence systems is still in the period of adaptation after its introduction, and the degree of technicalization in the process of practical application is at a low level. The frequent occurrence of the phenomenon of "technology overstepping" by artificial intelligence systems in the face of a large number of digital resources on the Internet, which has led to controversies over the moral and ethical issues of artificial intelligence systems. How to implement effective network governance under AI technology is a major challenge in today's Internet world.
In this paper, based on DIKW graph technology, the resources generated by the interaction in the Internet are fused and modeled, and the data graph, information graph, and knowledge graph obtained from the modeling together form the DIKW graph. The artificial intelligence system makes detailed provisions for the rights and responsibilities of the participants in the decision-making process and the allocation of decision-making resources based on the content obtained from modeling. The main elements are as follows:
(1) Establishing an purpose model and a value model, the purpose is used to divide the purpose system trees of different participants in the resource circulation process, and making accurate decision processing based on the refined purpose The value model includes the four basic principles of fairness, the two types of personal safety and property safety of security, and the guarantee of the participants' autonomous choice and control in the decision-making process of artificial intelligence system.
(2) Modeling the circulation of resources between different participants in the automated decision-making process of artificial intelligence system, the circulation of resources includes four stages of resource collection, resource modeling, purpose identification and resource transmission. Based on part of Personal Information Protection Law of the People's Republic of China, in this paper, the behavior of artificial intelligence system in the process of resource collection, storage and modeling is regulated. In the process of purpose identification, the basic structural system of intention model and value model is established. In the process of resource transmission, the artificial intelligence system processes the transmitted resources according to the divided purpose trees, and anonymizes the transmitted resources if necessary from the three values of security, fairness and autonomy under the requirements of the established value model. The methods of anonymization include different type of data, information, knowledge and group.
(3) Simulation of a technologized framework for the governance of artificial intelligence system, using the epidemiological investigation of close contacts during a new coronary pneumonia and the subsequent resource allocation during isolation as an example.
Keywords: DIKW mapping technology, artificial intelligence governance, purpose model, value model, resource circulation
High technology is fundamentally different from any other technology: it affects directly the nature and organization of tasks to be performed. Therefore, high technology, more than any other technology, has to be managed. We propose operational definition of high technology in terms of its effects on the support net of requisite relationships. This allows us to bring the distinctions between high technology, technology and appropriate technology into sharper operational focus. The symbiosis of men and machines is discussed in the framework of human systems management and symbionics. Principles of high technology management are outlined and their use in organization design demonstrated on real-life example of high-technology effects on systemic operations structures of an international company.