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Quantum processes, such as quantum circuits, quantum memories, and quantum channels, are essential ingredients in almost all quantum information processing tasks. However, the characterization of these processes remains a daunting task due…
Verification is the process of checking whether a product has been implemented according to its prescribed specifications. We study the case of a designer (the developer) that needs to verify its design by a third party (the verifier), by…
Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked…
Due to several issues arising in the rapidly-expanding Halal industry, among them the production of non-genuine or contaminated products and meats, there is a need to develop effective solutions for ensuring authenticity and quality. This…
Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process…
Automated, data-driven quality management systems, which facilitate the transformation of data into useable information, are desired to enhance decision-making processes. Integration of accurate, reliable, and straightforward approaches…
Specifying legal requirements for software systems to ensure their compliance with the applicable regulations is a major concern to requirements engineering (RE). Personal data which is collected by an organization is often shared with…
To combine and query ordered data from multiple sources, one needs to handle uncertainty about the possible orderings. Examples of such "order-incomplete" data include integrated event sequences such as log entries, lists of properties…
High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for…
Efficient consistency maintenance of incomplete and dynamic real-life databases is a quality label for further data analysis. In prior work, we tackled the generic problem of database updating in the presence of tuple generating constraints…
Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm. Users lacking in expertise or computational resources can outsource their data and database management…
A problem of optimal information acquisition for its use in general decision making problems is considered. This motivates the need for developing quantitative measures of information sources' capabilities for supplying accurate information…
Data exploration and quality analysis is an important yet tedious process in the AI pipeline. Current practices of data cleaning and data readiness assessment for machine learning tasks are mostly conducted in an arbitrary manner which…
We consider here the problem of obtaining reliable, consistent information from inconsistent databases -- databases that do not have to satisfy given integrity constraints. We use the notion of consistent query answer -- a query answer…
The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in…
Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process…
The quality of the data in spreadsheets is less discussed than the structural integrity of the formulas. Yet it is an area of great interest to the owners and users of the spreadsheet. This paper provides an overview of Information Quality…
Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available…
This study examines the impact of Total Quality Management (TQM) practices on organizational outcomes. Results show a significant relationship between TQM practices such as top executive commitment, education and teaching, process control,…
In the distributed and dynamic framework of the Web, data quality is a big challenge. The Linked Open Data (LOD) provides an enormous amount of data, the quality of which is difficult to control. Quality is intrinsically a matter of usage,…