Auflistung nach Autor:in "Sadiq, Shazia"
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- ZeitschriftenartikelDiscovering Data Quality Problems(Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Zhang, Ruojing; Indulska, Marta; Sadiq, ShaziaExisting methodologies for identifying data quality problems are typically user-centric, where data quality requirements are first determined in a top-down manner following well-established design guidelines, organizational structures and data governance frameworks. In the current data landscape, however, users are often confronted with new, unexplored datasets that they may not have any ownership of, but that are perceived to have relevance and potential to create value for them. Such repurposed datasets can be found in government open data portals, data markets and several publicly available data repositories. In such scenarios, applying top-down data quality checking approaches is not feasible, as the consumers of the data have no control over its creation and governance. Hence, data consumers - data scientists and analysts - need to be empowered with data exploration capabilities that allow them to investigate and understand the quality of such datasets to facilitate well-informed decisions on their use. This research aims to develop such an approach for discovering data quality problems using generic exploratory methods that can be effectively applied in settings where data creation and use is separated. The approach, named LANG, is developed through a Design Science approach on the basis of semiotics theory and data quality dimensions. LANG is empirically validated in terms of soundness of the approach, its repeatability and generalizability.
- KonferenzbeitragUtilizing successful work practice for business process evolution(Business Information Systems – 9th International Conference on Business Information Systems (BIS 2006), 2006) Lu, Ruopeng; Sadiq, Shazia; Governatori, GuidoBusiness process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, business processes are always evolving in current dynamic business environments where requirements and goals are constantly changing. Whereas literature reports on the importance of domain experts in process modelling and adaptations, current solutions have not addressed this issue effectively. In this paper, we present a framework that utilizes successful work practice to support business process evolution. The framework on one hand provides the ability to use domain expert knowledge and experience to tailor individual process instances according to case specific requirements; and on the other, provides a means of using this knowledge through learning techniques to guide subsequent process changes.