RFID-based Recommender Systems in Stationary Trade

Recommender Systems have been successfully deployed in a variety of e-Commerce application scenarios. Customer selections of services or standard goods are supported as well as product configuration tasks. Little research has however been done on the application of Recommender Systems outside the virtual domain in real-world stationary trade. This surprises as on a business side, brick-and-mortar stores remain the primary distribution channel for products of daily usage. On a technical side, the growing popularity of RFID-transponders for product identification has laid the foundation for generating both context-aware and user-adaptive product recommendations. This contribution describes approaches and challenges of utilizing concepts from the realm of Recommender Systems in RFID-enabled stationary trade

Using Sensors in Organizational Research-Clarifying Rationales and Validation Challenges for Mixed Methods

Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on 'reality,' sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors

Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

The Museum Explorer: User Experience Enhancement In A Museum

A learner in an informal learning environment, such as a museum, encounters various challenges. After initial assessment, a set of methods were proposed that may enhance a learner’s experience in a museum using computer aided technologies. The most important insight was the need to support the museum visitor in three phases of activity: prior to the visit, during the visit, and after the visit. We hypothesized that software tools that could help connect these three phases would be helpful and valuable supports for the visitor. To test and evaluate our hypothesis, a system called “The Museum Explorer” was built and instantiated using the collection in the Museum of Antiquities located at the University of Saskatchewan. An evaluation of the Museum Explorer was conducted. Results show that the Museum Explorer was largely successful in achieving our goals. The Museum Explorer is an integrated solution for visitors in museums across the pre-visit, visit, and post-visit phases. The Museum Explorer was designed to provide a means to connect and transfer user experience across the major phases of a museum visit. For each phase of a visitor’s experience, a set of tools was built that provides intelligent and interactive communication features. To assist visitors selecting artefacts to visit, a recommender system allows users to select a set of constraints. To better manage interactivity, features and functions were offered based on context. A study was conducted with volunteer museum visitors. Results from the study show that the Museum Explorer is a useful support. Analysis of the usage data captured by the Museum Explorer has revealed some interesting facts about users’ preferences in the domain that can be used by future researchers

Mastering Omni-Channel Retailing Challenges with Industry 4.0 Concepts

Omni-Channel Management is an important trend, which allows retailers to improve customer experiences. Notwithstanding, entirely seamless integration of all channels, for example, in terms of customer or pricing data or consistent product offerings, is still a challenging endeavor. Technological developments, such as Industry 4.0 (I4.0), lead to innovation opportunities in the production industry. As there are intersections between I4.0 and Omni-Channel retailing, we propose that prominent Omni-Channel retailing challenges can be overcome by integrating knowledge from both research domains. Therefore, the purpose of this article is to investigate, which I4.0 concepts are utilized in scientific literature to overcome challenges and how these concepts can be transferred to Omni-Channel Management. To make this knowledge tangible for retailers, this article deduces opportunities on the application of I4.0 concepts in Omni-Channel retailing. The results show that especially IoT networks offer numerous deployment options and even Cyber-Physical Systems and Smart Factories provide related potentials

The Smart Mobile Application Framework (SMAF) - Exploratory Evaluation in the Smart City Contex

What makes mobile apps "smart"? This paper challenges this question by seeking to identify the inherent characteristics of smartness. Starting with the etymological foundations of the term, elements of smart behavior in software applications are extracted from the literature, elaborated and contrasted. Based on these findings we propose a Smart Mobile Application Framework incorporating a set of activities and qualities associated with smart mobile software. The framework is applied to analyze a specific mobile application in the context of Smart Cities and proves its applicability for uncovering the implementation of smart concepts in real-world settings. Hence, this work contributes to research by conceptualizing a new type of application and provides useful insights to practitioners who want to design, implement or evaluate smart mobile applications