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The purpose of this paper is to develop a usable configuration management for Archistar, which utilizes secret sharing for redundantly storing data over multiple independent storage clouds in a secure and privacy-friendly manner. Selecting the optimal secret sharing parameters, cloud storage servers and other settings for securely storing the secret data shares, while meeting all of end user’s requirements and other restrictions, is a complex task. In particular, complex trade-offs between different protection goals and legal privacy requirements need to be made.

A human-centered design approach with structured interviews and cognitive walkthroughs of user interface mockups with system administrators and other technically skilled users was used.

Even technically skilled users have difficulties to adequately select secret sharing parameters and other configuration settings for adequately securing the data to be outsourced.

Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.

The authors present novel human computer interaction (HCI) guidelines for a usable configuration management, which propose to automatically set configuration parameters and to solve trade-offs based on the type of data to be stored in the cloud. Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.