Improving Data Quality by Leveraging Statistical Relational Learning

Digitally collected data su ↵ ers from many data quality issues, such as duplicate, incorrect, or incomplete data. A common approach for counteracting these issues is to formulate a set of data cleaning rules to identify and repair incorrect, duplicate and missing data. Data cleaning systems must be able to treat data quality rules holistically, to incorporate heterogeneous constraints within a single routine, and to automate data curation. We propose an approach to data cleaning based on statistical relational learning (SRL). We argue that a formalism - Markov logic - is a natural fit for modeling data quality rules. Our approach allows for the usage of probabilistic joint inference over interleaved data cleaning rules to improve data quality. Furthermore, it obliterates the need to specify the order of rule execution. We describe how data quality rules expressed as formulas in first-order logic directly translate into the predictive model in our SRL framework

Improving Data Quality by Leveraging Statistical Relational\ud Learning

Digitally collected data su\ud ↵\ud ers from many data quality issues, such as duplicate, incorrect, or incomplete data. A common\ud approach for counteracting these issues is to formulate a set of data cleaning rules to identify and repair incorrect, duplicate and\ud missing data. Data cleaning systems must be able to treat data quality rules holistically, to incorporate heterogeneous constraints\ud within a single routine, and to automate data curation. We propose an approach to data cleaning based on statistical relational\ud learning (SRL). We argue that a formalism - Markov logic - is a natural fit for modeling data quality rules. Our approach\ud allows for the usage of probabilistic joint inference over interleaved data cleaning rules to improve data quality. Furthermore, it\ud obliterates the need to specify the order of rule execution. We describe how data quality rules expressed as formulas in first-order\ud logic directly translate into the predictive model in our SRL framework

Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework

The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry. Design/methodology/approach Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses. Findings The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance. Originality/value Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research