14,440 research outputs found
The Application of Fuzzy Logic Controller to Compute a Trust Level for Mobile Agents in a Smart Home
Agents that travel through many hosts may cause a threat on the security of the visited hosts. Assets,
system resources, and the reputation of the host are few possible targets for such an attack. The
possibility for multi-hop agents to be malicious is higher compared to the one-hop or two-hop
boomerang agents. The travel history is one of the factors that may allow a server to evaluate the
trustworthiness of an agent. This paper proposes a technique to define levels of trust for multi-hop
agents that are roaming in a smart home environment. These levels of trust are used later to
determine actions taken by a host at the arrival of an agent. This technique uses fuzzy logic as a
method to calculate levels of trust and to define protective actions in regard to those levels
How does big data affect GDP? Theory and evidence for the UK
We present an economic approach to measuring the impact of Big Data on GDP and GDP growth. We define data, information, ideas and knowledge. We present a conceptual framework to understand and measure the production of “Big Data”, which we classify as transformed data and data-based knowledge. We use this framework to understand how current official datasets and concepts used by Statistics Offices might already measure Big Data in GDP, or might miss it. We also set out how unofficial data sources might be used to measure the contribution of data to GDP and present estimates on its contributions to growth. Using new estimates of employment and investment in Big Data as set out in Chebli, Goodridge et al. (2015) and Goodridge and Haskel (2015a) and treating transformed data and data-based knowledge as capital assets, we estimate that for the UK: (a) in 2012, “Big Data” assets add £1.6bn to market sector GVA; (b) in 2005-2012, account for 0.02% of growth in market sector value-added; (c) much Big Data activity is already captured in the official data on software – 76% of investment in Big Data is already included in official software investment, and 76% of the contribution of Big Data to GDP growth is also already in the software contribution; and (d) in the coming decade, data-based assets may contribute around 0.07% to 0.23% pa of annual growth on average
Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics
Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.
Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.
Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.
Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
Talking about routines in the field: the emergence of organizational capabilities in a new cellular phone network company
No abstract availabl
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