RFID-based Article-to-Fixture Predictions in Real-World Fashion Stores

In recent years, Radio Frequency Identification (RFID) technology has been applied to improve numerous processes, such as inventory management in retail stores. However, automatic localization of RFID-tagged goods in stores is still a challenging problem. To address this issue, we equip fixtures (e.g., shelves) with reference tags and use data we collect during RFID-based stocktakes to map articles to fixtures. Knowing the location of goods enables the implementation of several practical applications, such as automated Money Mapping (i.e., a heat map of sales across fixtures). Specifically, we conduct controlled lab experiments and a case-study in two fashion retail stores to evaluate our article-to-fixture prediction approaches. The approaches are based on calculating distances between read event time series using DTW, and clustering of read events using DBSCAN. We find that, read events collected during RFID-based stocktakes can be used to assign articles to fixtures with an accuracy of more than 90%. Additionally, we conduct a pilot to investigate the challenges related to the integration of such a localization system in the day-to-day business of retail stores. Hence, in this paper we present an exploratory venture into novel and practical RFID-based applications in fashion retails stores, beyond the scope of stock management.

How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs

Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal---an online repository designed to support users in exploring and finding more than 500 existing biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researchers and practitioners. While usage of this portal is high, we know very little about how exactly users search and explore ontologies and what kind of usage patterns or user groups exist in the first place. Deeper insights into user behavior on such portals can provide valuable information to devise strategies for a better support of users in exploring and finding existing ontologies, and thereby enable better ontology reuse. To that end, we study and group users according to their browsing behavior on BioPortal using data mining techniques. Additionally, we use the obtained groups to characterize and compare exploration strategies across ontologies. In particular, we were able to identify seven distinct browsing-behavior types, which all make use of different functionality provided by BioPortal. For example, Search Explorers make extensive use of the search functionality while Ontology Tree Explorers mainly rely on the class hierarchy to explore ontologies. Further, we show that specific characteristics of ontologies influence the way users explore and interact with the website. Our results may guide the development of more user-oriented systems for ontology exploration on the Web.

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.