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Supporting Your Basic Needs -A Base Support Approach for Static Stability Assessments in Air Cargo

2023 | HICSS | Citations: 0
Authors: Longhitano, D.; Mazur, P.; Wolf, S.; Schoder, D.

Abstract: Static stability is one of the most important constraints in the design and eff ... Expand

Abstract: Static stability is one of the most important constraints in the design and efficient calculation of safe air cargo pallets. To calculate the static stability of a cargo layout, base-focused methods such as full or partial base support are often used. Compared to mechanical or simulation-based methods, they offer high performance and simplicity. However, these methods currently reach their limits when dealing with the practical complexity of air cargo, making them difficult to apply in practice. In this research, we extend and generalize these support point methods by modeling irregular and multilevel cargo shapes, which enables improved practical applications. We follow a designoriented approach to capture air cargo requirements, design an artifact, and evaluate its performance. Our results show a generalized approach that covers a greater practical complexity while maintaining its efficiency. Collapse

Semantic filters: function-behaviour-structure model computational algorithm

Topics: requirements analysis

Methods: design theory computational algorithm design requirement design science design artifact

Theories: function-behaviour-structure model
Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis
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2014 | Decision Support Systems | Citations: 0
Authors: Lau, Raymond Y. K.; Li, Chunping; Liao, Stephen S. Y.

Abstract: In the era of Web 2.0, there has been an explosive growth of consumer-contribute ... Expand

Abstract: In the era of Web 2.0, there has been an explosive growth of consumer-contributed comments at social media and electronic commerce Web sites. Applying state-of-the-art social analytics methodology to analyze the sentiments embedded in these consumer comments facilitates both firms' product design strategies and individual consumers' comparison shopping. However, existing social analytics methods often adopt coarse-grained and context-free sentiment analysis approaches. Consequently, these methods may not be effective enough to support firms and consumers' demands of fine-grained extraction of market intelligence from social media. Guided by the design science research methodology, the main contribution of our research is the design of a novel social analytics methodology that can leverage the sheer volume of consumer reviews archived at social media sites to perform a fine-grained extraction of market intelligence. More specifically, the proposed social analytics methodology is underpinned by a novel semi-supervised fuzzy product ontology mining algorithm. Evaluated based on real-world social media data, our prototype system shows remarkable performance improvement over a baseline ontology learning system and a context-free sentiment analysis system. The managerial implication of our research is that firms can apply the proposed social analytics methodology to tap into the collective social intelligence on the Web, and hence improve their product design and marketing strategies. Collapse

Semantic filters: function-behaviour-structure model computational algorithm

Topics: knowledge representation social media site online learning system computer hardware laptop computer

Methods: ontological modelling sentiment analysis computational algorithm experiment design artifact

Theories: function-behaviour-structure model
Automatic Domain Ontology Extraction for Context-Sensitive Opinion Mining

2009 | International Conference on Information Systems | Citations: 0
Authors: Lau, Raymond Y K; Lai, Chapmann C L; Ma, Jian; Li, Yuefeng

Abstract: Automated analysis of the sentiments presented in online consumer feedbacks can ... Expand

Abstract: Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform contextsensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline. Collapse

Semantic filters: function-behaviour-structure model computational algorithm

Topics: knowledge representation electronic commerce laptop computer application programming interface information retrieval

Methods: sentiment analysis domain ontology machine learning design artifact ontological modelling

Theories: fuzzy set theory function-behaviour-structure model

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Found 3 articles

Supporting Your Basic Needs -A Base Support Approach for Static Stability Assessments in Air Cargo

Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis

Automatic Domain Ontology Extraction for Context-Sensitive Opinion Mining