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In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

This study aims to conduct a thorough evaluation to offer a modern overview of mindfulness’s performance and conceptual framework in leadership.

This study reviews the literature on mindfulness in leadership using bibliometric analysis and systematic review techniques. This study delves into the most significant writings, leading journals, authors, organizations and nations contributing to the field and the selected methodologies and research contexts for mindfulness in leadership.

This study unveils three areas of mindfulness in leadership: leadership mindfulness interventions and practices, essential outcomes of mindfulness practice and emergent styles and mindfulness.

This study significantly expands the Baer et al. (2006) review of mindfulness to offer new views over their manual qualitative analysis based on a smaller collection of literature while adding the leadership perspective. Using bibliometric analysis, this study especially carries out performance analysis and scientific mapping of the collection of research publications on mindfulness in leadership. Additionally, more current studies are included to update the field.