A language modeling framework for expert finding

Statistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process of identifying experts given a particular topic. In this paper, we introduce and detail language modeling approaches that integrate the representation, association and search of experts using various textual data sources into a generative probabilistic framework. This provides a simple, intuitive, and extensible theoretical framework to underpin research into expertise search. To demonstrate the flexibility of the framework, two search strategies to find experts are modeled that incorporate different types of evidence extracted from the data, before being extended to also incorporate co-occurrence information. The models proposed are evaluated in the context of enterprise search systems within an intranet environment, where it is reasonable to assume that the list of experts is known, and that data to be mined is publicly accessible. Our experiments show that excellent performance can be achieved by using these models in such environments, and that this theoretical and empirical work paves the way for future principled extensions.     

A framework for the successful implementation of food traceability systems in China

Implementation of food traceability systems in China faces many challenges due to the scale, diversity, and complexity of China's food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a prestudy helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in 83 food companies. This study identifies six dimensions for critical success factors: laws, regulations, and standards; government support; consumer knowledge and support; effective management and communication; top management, company wide, and vendor support; and information and system quality.     

A framework for portal implementation: A case for Saudi organizations

Portals provide an aggregated and personalized view of information through a large application integrating information, people, and processes across organizational boundaries. As such, portals can significantly impact organizations, completely changing how they work and operate. In addition, many critical success factors can affect portals at any stage of implementation. Many organizations lack suitable frameworks for implementing portals to ensure success in the organization, customer satisfaction, and employee morale. To address this gap, we proposed a comprehensive framework to develop and operate portal projects by considering specific critical success factors. We have evaluated and compared this work with previous work in the same field, more accurate and significant results.     

A general end-to-end diagnosis framework for manufacturing systems

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.