The Science of Information Processing Structures and the Design of a New Class of Distributed Computing Structures

Mikkilineni, Rao

doi:10.3390/proceedings2022081053

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This paper generates two theoretical contributions to the literature. First, this paper addressed how knowledge risk was interpreted in terms of comes: knowledge risk framework (Massingham, 2010), knowledge risks in organizational networks (Trkman and Desouza, 2012), knowledge risk management and organizational performance (Durst et al , 2019), mapping knowledge risks (Durst and Zieba, 2019), knowledge risk management in the public sector (Durst et al , 2020), knowledge risks in business sustainability (Durst and Zieba, 2020), predict knowledge risk in real time (Mikkilineni, 2022) and knowledge risk management on sustainability (Zieba et al , 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Second, the literature review reports several studies interconnected with the dark side of knowledge management (Chua, 2009), dark side of knowledge transfer (Frishammar et al , 2015), mapping knowledge risks (Durst and Zieba, 2019), knowledge risk management in the public sector (Durst et al , 2020) and predicting knowledge risk in real-time (Mikkilineni, 2022). We focus our research on understanding how to develop a model for predicting innovation in SMEs, the likelihood of future-based decisions and implementing organizational solutions and anticipating inherent weaknesses in the knowledge infrastructure (van der Spek et al , 2009).…”

Section: Discussionmentioning

confidence: 99%

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Applying deep learning to predict innovations in small and medium enterprises (SMEs): the dark side of knowledge management risk

Figueiredo

Ferreira

Emilia

et al. 2023

VJIKMS

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Purpose This study aims to predict the dark side of knowledge management risk to innovation in Portuguese small and medium enterprises (SMEs). It examines the spinner innovation model factors of knowledge creation, knowledge transfer, private knowledge, public knowledge and innovation in uncertain environments. Design/methodology/approach The authors developed a conceptual model to support the analysis. The survey data stemmed from a sample of 208 Portuguese SMEs in Portugal. The authors analyzed the primary data from the ad hoc survey using the data mining (deep learning) technique. Findings The research sets out and tests factors relevant to understanding how to predict innovation in uncertain business environments. This study identifies four factors fostering innovation in SMEs: knowledge creation, knowledge transfer, public knowledge management and private knowledge management. Knowledge creation showed the best return and presented the closest relationship with innovation. Originality/value Innovation models generally measure the relationships between variables and their impacts on the economy (economic and regional development). Predictive models are considered in the literature as a gap to be filled, especially in an uncertain environment in the SME context.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Applying deep learning to predict innovations in small and medium enterprises (SMEs): the dark side of knowledge management risk

Figueiredo

Ferreira

Emilia

et al. 2023

VJIKMS

View full text Add to dashboard Cite

show abstract

General Theory of Information, Digital Genome, Large Language Models, and Medical Knowledge-Driven Digital Assistant

Kelly,

Coccaro,

Mikkilineni

2023

IS4SI Summit 2023

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Improving QoS Management Using Associative Memory and Event-Driven Transaction History

Di Stefano,

Gollo,

Morana

2024

Information

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Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm.

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The Science of Information Processing Structures and the Design of a New Class of Distributed Computing Structures

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 3 publications

References 14 publications

Applying deep learning to predict innovations in small and medium enterprises (SMEs): the dark side of knowledge management risk

Applying deep learning to predict innovations in small and medium enterprises (SMEs): the dark side of knowledge management risk

General Theory of Information, Digital Genome, Large Language Models, and Medical Knowledge-Driven Digital Assistant

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

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