Automated taxonomy building by adopting discriminant and characteristic capabilities

Taxonomies are becoming essential in several fields, playing an important role in a large number of applications, particularly for specific domains. Taxonomies provide efficient tools to people by organizing a huge amount of information into a small hierarchical structure. Taxonomies were originally built by hand, but nowadays the technology permits to produce a vast amount of information. Consequently, recent research activities have been focused on automated taxonomy generation. In this paper, we propose a novel approach for automatically build a taxonomy, starting from a set of categories. We deem that, in a hierarchical structure, each node should intuitively be represented with proper meaningful and discriminant features, instead of considering a fixed feature space. Our proposal relies on two metrics able to identify the most meaningful features. Our conjecture is that a feature could significantly change its discriminant power (hence, its role) along the taxonomy levels. Hence, we devise a greedy algorithm able to build a taxonomy by identifying the meaningful terms for each level. We perform preliminary experiments that give rise to the usefulness of the proposed approach

Adoption patterns and performance implications of Smart Maintenance

To substantiate and extend emergent research on maintenance in digitalized manufacturing, we examine adoption patterns and performance implications of the four dimensions of Smart Maintenance: data-driven decision-making, human capital resource, internal integration, and external integration. Using data collected from 145 Swedish manufacturing plants, we apply a configurational approach to study how emergent patterns of Smart Maintenance are shaped and formed, as well as how the patterns are related to the operating environment and the performance of the manufacturing plant. Cluster analysis was used to develop an empirical taxonomy of Smart Maintenance, revealing four emergent patterns that reflect the strength and balance of the underlying dimensions. Canonical discriminant analysis indicated that the Smart Maintenance patterns are related to operating environments with a higher level of digitalization. The results from ANOVA and NCA showed the importance of a coordinated and joint Smart Maintenance implementation to the maintenance performance and productivity of the manufacturing plant. This study contributes to the literature on industrial maintenance by expanding and strengthening the theoretical and empirical foundation of Smart Maintenance, and it provides managerial advice for making strategic decisions about Smart Maintenance implementation

Trust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach

In this study, we examine the configurations of trust-enhancing factors that determine the intention to adopt conversational agents (CAs) for disease diagnosis. After identifying trust factors influencing the behavioral intent to adopt CAs based on the information systems acceptance research field, we assigned 201 participants to use the mobile Ada application and surveyed them about their experience. Ada is a medical diagnostic CA that combines patients’ symptoms with their medical history and provides diagnostic suggestions. The collected data was analyzed using a fuzzy set qualitative comparative analysis to capture the causal complexity of trust. We identified several configurations of trust-enhancing factors affecting the intention to adopt the CA. In particular, our results show that the adoption intentions are strongly determined by trust factors associated with the performance dimension. Furthermore, we derived two propositions for the development of CAs for healthcare purposes and elaborated implications for research and practice

Indexing and retrieval in digital libraries : developing taxonomies for a repository of decision technologies

DecisionNet is an online Internet-based repository of decision technologies. It links remote users with these technologies and provides a directory service to enable search and selection of suitable technologies. The ability to retrieve relevant objects through search mechanisms is basic to any repository's success and usability and depends on effective classification of the decision technologies. This thesis develops classification methods to enable indexing of the DecisionNet repository. Existing taxonomies for software and other online repositories are examined. Criteria and principles for a good taxonomy are established and systematically applied to develop DecisionNet taxonomies. A database design is developed to store the taxonomies and to classify the technologies in the repository. User interface issues for navigation of a hierarchical classification system are discussed. A user interface for remote World Wide Web users is developed. This user interface is designed for browsing the taxonomy structure and creating search parameters online. Recommendations for the implementation of a repository search mechanism are given.http://archive.org/details/indexingndretrie1094532199NAU.S. Navy (U.S.N.) authorApproved for public release; distribution is unlimited

Deep learning for time series classification: a review

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover

The Moderating Role of Absorptive Capacity in the Assimilation of Enterprise Information Systems

We attempt to understand how external institutional forces affecting ERP assimilation within organizations need not impact all organizations uniformly but instead can be moderated by the enterprises\u27knowledge-based capabilities. Building on an institutional model of ERP assimilation, we investigate the role of absorptive capacity (ACAP) in ERP assimilation. Specifically we examine how the ACAP of an organization can enhance or retard the effect of institutional forces on the degree of ERP assimilation. Following a recent framework we operationalize ACAP as potential ACAP (PACAP) and realized ACAP (RACAP) and find that both dimensions affect ERP assimilation in different ways. While both, PACAP and RACAP, have a direct positive impact on assimilation, PACAP moderates the impact of mimetic forces on assimilation whereas RACAP moderates the effect of normative pressures. While we find overall a strong support for our hypothesized model, interestingly, we also find that RACAP negatively moderates the effect of mimetic pressures on assimilation. We discuss the contributions of this study to a better understanding of IT assimilation processes

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

publishedVersio

Empirical study of attributes and perceived benefits of applications integration for enterprise systems

This research investigates the very essence of integration by focusing on the integration of applications for enterprise systems. Integration is a large and complex topic recognized as a key concept in a wide variety of IT domains that dates back to the dawn of the computer era. The evolution of IT integration has included integration of sub-routines of computer programs, integration of separate islands of data to create common databases, and integration of disparate applications to form enterprise systems. Perhaps the most touted characteristic and principal goal of enterprise systems is integration although virtually no research is available regarding this phenomenon. The value of integration is rarely defined either in abstract or practical terms. We generally assume that the value of integration is obvious although there is no evidence that supports this implicit view. To address the lack of evidence, this investigation began by examining the perceptions of three practitioner stakeholder groups about the characteristics and benefits of integration. These groups were senior managers, IT professionals, and end-users. In part I of the two-part study, interviews of 51 practitioners revealed 15 major themes related to practitioner perspectives of the characteristics, benefits, and downsides of applications integration. For part II, a new measure was created based on the literature and the analysis of the phase I interviews. 926 people in three organizations were surveyed. Contributions of the research included a new partially validated instrument to assess attributes and benefits of applications integration, taxonomies were created for integration attributes and perceived benefits, and a model was proposed to frame and study IT integration infrastructures. A foundation was established to evaluate the degree of applications integration for enterprise systems. Several downsides to applications integration were documented. Two new high order constructs (attributes and benefits) were established, along with four attribute dimensions and six benefit dimensions