Big data as the new enabler in business and other intelligence

Vossen, Gottfried

doi:10.1007/s40595-013-0001-6

Cited by 42 publications

(27 citation statements)

References 18 publications

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“…Embedding BDA in BPMS may give "process participants better real-time situational awareness and the ability to tailor their responses appropriately" (Gao, 2013, p.4). According to Vossen (2013) and Vera-Baquero et al (2015), BDA capable BPMS need to be analytical, automatic, adaptive, and agile to leverage big data (Hill and Schulte, 2011;Gao, 2013) and this makes the system capable of advanced analysis while automatically processing data from several different business processes (Wamba and Mishra, 2017). Additionally, when applied to two or more related organizations, these systems may foster inter-organizational information exchange (Vera-Baquero et al, 2015).…”

Section: Big Data Business Process Management Systems and Ambidextrmentioning

confidence: 99%

Ambidextrous organization and agility in big data era

Rialti

Marzi

Silic

et al. 2018

BPMJ

105

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The aim of this research is to explore the effect of Big Data Analytics capable Business Process Management Systems (BDA capable BPMS) on ambidextrous organizations' agility. The ways in which the functionalities of BDA capable BPMS may improve organizational dynamism and reactiveness will be explored. Design: A theoretical analysis of the potential of BDA capable BPMS in increasing organizational agility, with particular attention to the ambidextrous organizations, has been performed. A conceptual framework was subsequently developed. Findings: The research proposes a framework highlighting the importance of BDA capable BPMS in increasing ambidextrous organizations' agility. Moreover, the authors apply the framework to the cases of consumer-goods companies that have included BDA in their processes management. Research limitations: The principal limitations are linked to the need to validate quantitatively the proposed framework. Practical implications: The value of the proposed framework can be found in its ability to help managers to fully understand and exploit the potentiality of BDA capable BPMS. Moreover, the implications show some guidelines to ease the implementation of such systems within ambidextrous organizations. Originality: The research offers a model to interpret the effects of BDA capable BPMS on ambidextrous organizations' agility. In this way, the research addresses a significant gap by exploring the importance of information systems for ambidextrous organizations' agility.

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Section: Big Data Business Process Management Systems and Ambidextrmentioning

confidence: 99%

Ambidextrous organization and agility in big data era

Rialti

Marzi

Silic

et al. 2018

BPMJ

105

View full text Add to dashboard Cite

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“…On the basis of the information received, the actuators make the necessary decisions about how to respond to the appropriate actions. It creates, however, a new type of data, big data, James [9], Vossen G [19] -a large number of datasets in the streaming and dynamic forms. Thus, we need a new approach that seeks to discover new information, identify and categorize data, focusing on exploring natural phenomena, acquiring new knowledge, and understanding real-time laws of nature.…”

Section: The Real World and Virtual Worldmentioning

confidence: 99%

Artificial Brain and "Human-Machine" Integration

Tran¹

2017

JCSAIT

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We present, in this short paper, a model of artificial brain based on the Software-Hardware integration in the "1 + 1 = 1" philosophy framework using machine learning and multiprocessor system on chip, SoC. Its virtual experiences are generated by a deep learning process with random changing of the structure of a net of artificial neural network, NoNN, using Monte Carlo method. It ensures creative property of the human cognitive processing and possibility of the "humanmachine" integration/"Human brain-Artificial Brain" integration, which should be applied in various areas of online control. KeywordsPhilosophy; Meta-concept; Machine learning; Artificial brain; "Human-Machine" integration; Online control; 3 Introduction "1 + 1 = 1" ! According to Georg Wilhelm Friedrich Hegel (1770-1831), we cannot have the "pure being" unless we also take into consideration "pure nothing" i.e., being and nothing are oneness (1 + 1 = 1) which is neither being nor nothing but a third option presented rather by meta-language, "being-nothing", that transcends both. It we call the "one" philosophy. In fact, the "one" philosophy is find in Indian four-cornered argumentation logic (about 500 BC): affirmation X, negation of X, ¬X, both X ∧ ¬X and neither ¬(X ∨ ¬X), in which, X ∧ ¬X is integration of X and ¬X which represents a third option that transcends both. It describers not knowledge originated from logic (logical knowledge) but rather knowledge originated from a cognitive process in the human brain; in the Chinese "one" philosophy presented by Zhuang Zhou -(369-286 B.C.), in which he claimed that Discrimination, and the opposition of it, Nondiscrimination, are seen as oneness Discrimination-Nondiscrimination. It is not the common sum, Discrimination + Nondiscrimination, but rather an integration described perhaps firstly in the world by meta-language Discrimination-Nondiscrimination referred as a third option that transcends both Discrimination and Nondiscrimination. It represents his feeling comes from cognitive processing; in Levis's modal logic and Lukasiewicz's multivalued logic (1930), in which, the value of truth like the third argumentation of Indian logic, is the integration of true and false -"false-false" integration. It is finding also in the "membership-nonmembership" integration presented in the Lotfi Zadeh's fuzzy set theory (1965). It is clear that various meta-concepts, "being-nothing", "DiscriminationNondiscrimination", "true-false" and "membership-nonmembership" have not meaning associated with common word/words, but another meaning related to the denotative concept described in the "sense and denotation" theory written by Frege FLG (1848-1925, which represents a meaning in the semantic and pragmatic aspects of both conscious and non-conscious thoughts of the human brain [7].Likewise, according to the "one" philosophy, it is impossible to understand the real world (reality) without the virtual world (virtual reality) contained in mind (in the human brain). That is, when we receive what we consider to be...

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“…They concluded that big data analytics can be applied to leverage business changes and enhance decision-making by applying advanced analytics techniques on big data and revealing hidden insights and valuable knowledge. Vossen (2014) studied and reviewed the issues, techniques and applications of big data, with an emphasis on business intelligence architectures. Saecker and Markl (2013) then presented an overview of existing processing approaches to address the problems arising from big data analytics when using modern hardware architectures.…”

Section: Big Data and Big Data Analyticsmentioning

confidence: 99%

AHP Model for the Big Data Analytics Platform Selection

Lněnička

2015

AIP

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Big data analytics refers to a set of advanced technologies, which are designed to efficiently operate and maintain data that are not only big, but also high in variety and velocity. This paper analyses these emerging big data technologies and presents a comparison of the selected big data analytics platforms through the whole data life. The main aim is then to propose and demonstrate the use of an AHP model for the big data analytics platform selection, which may be used by businesses, public sector institutions as well as citizens to solve multiple criteria decision-making problems. It would help them to discover patterns, relationships and useful information in their big data, make sense of them and to take responsive action.

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Big data as the new enabler in business and other intelligence

Cited by 42 publications

References 18 publications

Ambidextrous organization and agility in big data era

Ambidextrous organization and agility in big data era

Artificial Brain and "Human-Machine" Integration

AHP Model for the Big Data Analytics Platform Selection

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