Since the right decision is made from the correct data, assessing data quality is an important process in computational science when working in a data-driven environment. Appropriate data quality ensures the validity of decisions made by any decision-maker. A very promising area to overcome common data quality issues is computational intelligence. This paper examines from past to current intelligence techniques used for assessing data quality, reflecting the trend for the last two decades. Results of a bibliometric analysis are derived and summarized based on the embedded clustered themes in the data quality field. In addition, a network visualization map and strategic diagrams based on keyword co-occurrence are presented. These reports demonstrate that computational intelligence, such as machine and deep learning, fuzzy set theory, evolutionary computing is essential for uncovering and solving data quality issues.

Computational Intelligence Techniques for Assessing Data Quality: Towards Knowledge-Driven Processing

As customers’ consumption is increasingly dominated by technology-driven systems, online self-verification becomes an important aspect of customers’ online purchasing behavior and plays a significa...

Customer Online Feedback with an Identity Versus No Identity: The Influence on Review Comments:

For decades, different approaches have been designed and implemented for optimizing maintenance management systems in manufacturing. Especially advancing digitalization is leading to novel data-driven maintenance methods and strategies. However, the envisaged value-added implementation of novel solutions faces several challenges in the industrial context. Despite plausible advantages for manufacturing enterprises, maintenance is not yet considered as an enabler and driver of sustainable and resilient production management. Maintenance-Free Factory (M2F), thereby, is to investigate unexhausted potentials for transforming maintenance to an enabler rather than cost-driven system along manufacturing. Pursuing this line of research, this position paper reflects the perspective of the authors on maintenance management, based on experiences gained from research and practice over the past years. It aims to trigger thoughts on the vision of M2F considering i) profound change in maintenance organization and processes, ii) value-driven application of diagnostics, predictive and prescriptive methods, iii) significant change in the competence profile of maintenance engineers, and iv) a reorientation in strategic mind-set of managers and chief production planners. A key element is exemplary discussed in detail, i.e., the need for a flexible maintenance shift model, which may lead to a significant increase in performance of production systems and increase of overall equipment effectiveness. 

Maintenance-Free Factory: A Holistic Approach for Enabling Sustainable Production Management


 Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous, or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The Society of Petroleum Engineers (SPE) has formed a cross-disciplinary technical subcommittee to investigate how to describe and propagate drilling data quality and uncertainty. The subcommittee is a cooperation between the drilling system automation, wellbore positioning, and drilling uncertainty prediction technical sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated and ranked with the most compelling user story analyzed in detail. From this approach, a series of multidisciplinary workflows (drilling data generators) can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one user story focused on capturing the description of the quality and uncertainty of depth measurements. Multiple use cases have been extracted from this single user story exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic networks to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted use cases, showing that the semantic network method has the potential to describe a broad range of complex drilling-related processes. The study also demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagates between each of those concepts.

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.

A Method to Classify Data Quality for Decision Making Under Uncertainty

A General Framework for Data Uncertainty and Quality Classification

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A General Framework for Data Uncertainty and Quality Classification