Introduction to special section on intelligent mobile knowledge discovery and management systems
TL;DR: This special section on Intelligent Mobile Knowledge Discovery and Management Systems is to bring together top-quality articles on the art and practice of mobile knowledge discovery and management systems that exhibit a level of intelligence.
Abstract: Advances in wireless communication mobile-information infrastructures such as GPS, WiFi, and mobile phone technologies have enabled us to collect, process, and manage massive amounts of mobile data from diverse information sources. These mobile data are fine-grained, information-rich, and provide unparalleled opportunities for us to understand mobile user behaviours and generate useful knowledge, which in turn allows the delivery of intelligence for real-time decision making in various real-world applications. In this context, knowledge discovery is the process of automatic extraction of interesting and useful knowledge from large amounts of mobile data, whereas knowledge management consists of a range of strategies and practices to identify, create, represent, distribute, and enable the adoption of novel insights and experiences for decision making. There is a critical emerging need to investigate knowledge discovery and management issues in the mobile context. The objective of this special section on Intelligent Mobile Knowledge Discovery and Management Systems is to bring together top-quality articles on the art and practice of mobile knowledge discovery and management systems that exhibit a level of intelligence. We received a total of 12 submissions from which 3 articles have been selected for publication after an extensive peer-review process. The first article, entitled \" Mining Geographic-Temporal-Semantic Patterns in Trajectories for Location Prediction \" by Ying et al., has a focus on location prediction by mining human location traces. A unique perspective of this article is to exploit a user's geographic, temporal, and semantic information simultaneously for estimating the probability of a traveler in visiting a location. The key idea underlying this study is the discovery of user trajectory patterns, which are used to capture frequent movements triggered by the user's geographic, temporal, and semantic intentions. The article \" A Framework of Traveling Companion Discovery on Trajectory Data Streams \" by Tang et al. studies the problem of discovering object groups which travel together (i.e., traveling companions) from trajectory data streams. Since the solution of this problem requires a large computational cost because of expensive spatial operations , the authors propose a smart data structure to facilitate scalable and flexible companion discovery from location traces. The article \" Mondrian Tree: A Fast Index for Spatial Alarm Processing \" authored by M. Doo and L. Liu promotes the efficient process of spatial alarms, which remind us of the arrival of a future spatial event. A key research challenge in scaling spatial alarm processing is how to efficiently …
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08 Oct 2016
TL;DR: This paper proposes a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”, which treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near- to-distantsearch in the real world surveillance environment.
Abstract: While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.
450 citations
TL;DR: In this article, the authors proposed a novel analytical framework (Twitter Analytics) for analyzing supply chain tweets, highlighting the current use of Twitter in supply chain contexts, and further developing insights into the potential role of Twitter for supply chain practice and research.
372 citations
TL;DR: This paper proposes social context mobile (SoCoMo) marketing as a new framework that enables marketers to increase value for all stakeholders at the destination to connect the different concepts of context-based marketing, social media and personalisation, as well as mobile devices.
Abstract: Advanced technology enables users to amalgamate information from various sources on their mobile devices, personalise their profile through applications and social networks, as well as interact dynamically with their context. Context-based marketing uses information and communication technologies (ICTs) that recognise the physical environment of their users. Tourism marketers are increasingly becoming aware of those cutting-edge ICTs that provide tools to respond more accurately to the context within and around their users. This paper connects the different concepts of context-based marketing, social media and personalisation, as well as mobile devices. It proposes social context mobile (SoCoMo) marketing as a new framework that enables marketers to increase value for all stakeholders at the destination. Contextual information is increasingly relevant, as big data collected by a wide range of sensors in a smart destination provide real-time information that can influence the tourist experience. SoCoMo marketing introduces a new paradigm for travel and tourism. It enables tourism organisations and destinations to revolutionise their offering and to co-create products and services dynamically with their consumers. The proposed SoCoMo conceptual model explores the emerging opportunities and challenges for all stakeholders.
331 citations
TL;DR: In this article, a multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure.
Abstract: This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers’ spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.
244 citations
TL;DR: The most important approaches to serendipity in recommender systems are summarized, different definitions and formalizations of the concept are compared, state-of-the-art serendIPity-oriented recommendation algorithms and evaluation strategies to assess the algorithms are discussed, and future research directions are provided based on the reviewed literature.
Abstract: We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not only relevant and novel to the target user, but also significantly different from the items that the user has rated. However, the concept of serendipity is very subjective and serendipitous encounters are very rare in real-world scenarios, which makes serendipitous recommendations extremely difficult to study. To date, various definitions and evaluation metrics to measure serendipity have been proposed, and there is no wide consensus on which definition and evaluation metric to use. In this paper, we summarize most important approaches to serendipity in recommender systems, compare different definitions and formalizations of the concept, discuss serendipity-oriented recommendation algorithms and evaluation strategies to assess the algorithms, and provide future research directions based on the reviewed literature.
210 citations