P. Erhan Eren

Road Hazard Detection and Sharing with Multimodal Sensor Analysis on Smartphones

The sensing, computing and communicating capabilities of smart phones bring new possibilities for creating smart applications, including in-car mobile applications for smart cities. However, due to the dynamic nature of vehicles, many requirements such as sensor management, signal and image processing or information sharing needs exist when developing a smart sensor-based in-car mobile application. On the other hand, most in-car applications generally employ single-modal sensor analysis, which also yields limited results. Using the advanced capabilities of smart phones, this study proposes a framework with built-in multimodal sensor analysis capability, and enables easy and rapid development of signal and image processing-based smart mobile applications. Within this framework, an abstraction for fast access to synchronized sensor readings, a plug in based multimodal analysis interface for signal and image processing applications, and a toolset to connect to other users or servers for sharing the results are provided built-in. As part of this study, a sample mobile application is also developed to demonstrate the applicability of the framework. This application is used for detecting defects on the road, such as potholes and speed bumps, and it automatically extracts the video section and the image of the corresponding road segment containing the defect. Upon such critical hazard detection, the application instantly informs nearby users about the incident. A good detection rate of speed bumps is obtained in the performed tests, while the advantage of automatic image extraction based on the multimodal approach is also demonstrated.

Development of an Assessment Model for Industry 4.0: Industry 4.0-MM

The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.

A Cloud Based Architecture for Distributed Real Time Processing of Continuous Queries

With the rapid pace of technological advancements in smart device, sensor and actuator technologies, the Internet of Things (IoT) domain has received significant attention. These advances have brought us closer to the ubiquitous computing vision. However, in order to fully realize this vision, devices and applications should rapidly adapt to the changes in the environment and other nearby devices. Most of the existing applications store collected data in a data store and allow users to query stored data to notice and react to such changes. Many of the applications utilize cloud and big data technologies for scalability. Nevertheless, the responsiveness of such IoT applications is largely limited due to polling based queries. Therefore, in this paper, we propose a centrally managed distributed infrastructure based on the state of the art big data technologies. We focus primarily on the problem of how to process multiple continuous queries in real time and notify users timely. Our main contribution is specifying a generic and scalable architecture to process a multitude of real time queries. We provide a prototype implementation to demonstrate the applicability of the approach. The scalability of the architecture is evaluated by conducting several experiments on the prototype implementation.

A Mobile Computing Framework Based on Adaptive Mobile Code Offloading

Smart phones are not capable of competing against their desktop counterparts or servers in terms of CPU speed, battery, memory and storage. However, a mobile device can transparently use cloud resources by employing an offloading mechanism. Offloading enables mobile devices to run computation intensive applications such as object recognition, Optical Character Recognition (OCR) and augmented reality. In this paper, an Inversion of Control (IoC) based offloading technique is proposed in order to overcome shortcomings and limitations of current approaches in the literature. A sample application has been implemented by using the proposed technique. The results show that the proposed offloading technique leads to energy savings of 66% to 81% and execution time savings by 76% to 81% with a small computational overhead.

What installed mobile applications tell about their owners and how they affect users download behavior

Number of user owned apps and their category differ with gender and personality.Having similar apps increases the probability of accepting recommended applications.Number of apps owned in some categories implies higher acceptance of recommended apps.Conscientiousness is positively related with accepting recommended applications.Being agreeable is related with editors choice application preference. The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. An experimental study was conducted through the use of a specifically designed mobile application, on users mobile phones. The goals were; first, to learn about the users personality and the applications they downloaded to their mobile phones, second to recommend applications to users via notifications through the use of experimental mobile application and learn about user behavior in mobile environment. The question of how the personality features of users affect their compliance to recommendations is explored in this study. It is found that conscientiousness is positively related with accepting recommended applications and being agreeable is related with the preference for the applications of editors choice. Furthermore, in this study, applications owned by the user and the composition of applications under categories and their relation with personality features are explored. It is shown that the number of user owned applications and their category differ according to gender and personality. Having similar applications and the number of applications owned under specific categories increase the probability of accepting recommended applications.

LRFMP model for customer segmentation in the grocery retail industry: a case study

Purpose The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model. Design/methodology/approach This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended. Findings The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.” Practical implications This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently. Originality/value This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

Text recognition and correction for automated data collection by mobile devices

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

A Mobile Participatory Framework for the Consumer Decision Process

With the recent advances in mobile technologies, the role of mobile information systems has been increasing within the consumer behavior domain. Accordingly, we propose a holistic framework which supports consumers on all stages of the consumer decision process by using the participatory sensing approach. The consumer decision process is the major part of the consumer behavior, and represents the set of stages a consumer follows when purchasing a product. On the other hand, participatory sensing is a recently developed approach in the mobile information systems domain, and enables users to be directly involved especially in the data collection, analysis and sharing processes through their mobile phones. The proposed framework is designed to incorporate this significant capability of participatory sensing into the consumer behavior domain. The aim of designing this framework is to build an effective, time and cost saving mobile information system for consumers by supporting their decision process. In addition to designing the framework, a working prototype is developed using mobile phones in order to show the applicability of the proposed framework. The obtained results are also presented with an overview of existing related studies. This study is also intended to serve as a base for further studies regarding the effective use of the participatory sensing approach and mobile information systems in the consumer behavior domain.

MobileCDP: A mobile framework for the consumer decision process

The consumer decision process is a widely accepted model covering consumer activities, and accordingly contains five interrelated stages: problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. In order to help consumers deal with challenges associated with all these stages, mobile information systems bring significant capabilities, as in other application domains. However, related prior research is mostly restricted to the individual stages of the process. Since the stages are interrelated, and the data collected in one are also valuable for another, we propose a mobile framework designed to provide assistance in all stages of the Consumer Decision Process, named MobileCDP. A prototype is also implemented and evaluated to show the applicability of the framework. Experiments show that the functions provided by the prototype are useful, well integrated, and easy to use. Moreover, statistical analysis of the results proves that the prototype reduces time, costs, and cognitive effort of the user.

Cloud-Based Enterprise Information Systems: Determinants of Adoption in the Context of Organizations

Cloud computing is growing at a very fast pace. Enterprise information systems (EISs) such as ERP, SCM, and CRM are used in organizations in order to increase customer satisfaction, operational excellence, and to decrease operational costs. Looking at the widespread literature available on both EIS and Cloud Computing, few researchers have examined the integration of both systems. While this area has not been fully investigated in the academia due to limited available literature, it has attracted significant interest from general practitioners. Accordingly, the Cloud-EIS can be considered as an important research problem. In this study, we attempt to investigate the factors influencing the usage and adoption of Cloud-EISs by considering Technology-Organization-Environment (TOE) framework as the basis to give directions to cloud service providers on how to design their products in order to increase adoption and usage. Analytic Hierarchy Process (AHP) is used in order to rank the determined factors. The results show that the most significant factors influencing the usage and adoption of Cloud-EISs are security & privacy, business activity- cloud EIS fitness, top management support, trust, and organization’s IT resource.