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Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users’ efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

Flight delay is a significant problem that negatively impacts the aviation industry and costs billion of dollars each year. Most existing studies investigated this issue using various methods based on historical data. However, due to the highly dynamic environments of the aviation industry, relying only on historical datasets of flight delays may not be sufficient and applicable to forecast the future of flights. The purpose of this research is to study the flight delays from a new angle by utilising data generated from the emerging Internet of Things (IoT) paradigm. Our primary goal is to improve the understanding of the roots and signs of flight delays as well as discovering related factors. In this paper, we present a framework that aims at improving the flight delay problem. We consider the IoT data generated from distributed sensors that have not been considered in existing works in the analysis of flight delays, and for that purpose, an automatic tool is developed to collect IoT data from various data sources including flight, weather, and air quality index. Based on the heterogeneous data, an algorithm is developed to merge different features from diverse data sources. We adopt predictive modelling to study the factors that contribute to flight delays and to predict the flight delays in the future. The results of our work show a high correlation among the developed features. In particular, the results clearly demonstrate the association between the flight delays and the air quality index factor. In particular, our current prediction model achieves 85.74% in accuracy.

Over the past few years, activity recognition techniques have attracted unprecedented attentions. Along with the recent prevalence of pervasive e-Health in various applications such as smart homes, automatic activity recognition is being implemented increasingly for rehabilitation systems, chronic disease management, and monitoring the elderly for their personal well-being. In this paper, we present WITS, an end-to-end web-based in-home monitoring system for convenient and efficient care delivery. The system unifies the data- and knowledge-driven techniques to enable a real-time multi-level activity monitoring in a personalized smart home. The core components consist of a novel shared-structure dictionary learning approach combined with rule-based reasoning for continuous daily activity tracking and abnormal activities detection. WITS also exploits an Internet of Things middleware for the scalable and seamless management and learning of the information produced by ambient sensors. We further develop a user-friendly interface, which runs on both iOS and Andriod, as well as in Chrome, for the efficient customization of WITS monitoring services without programming efforts. This paper presents the architectural design of WITS, the core algorithms, along with our solutions to the technical challenges in the system implementation.

The Internet of Things (IoT), in general, is a compelling paradigm that aims to connect everyday objects to the Internet. Nowadays, IoT is considered as one of the main technologies which contribute towards reshaping our daily lives in the next decade. IoT unlocks many exciting new opportunities in a variety of applications in research and industry domains. However, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is “Does the IoT already exist?”. So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data, and user interests. To answer this question, in this work, we conduct an in-depth analytical investigation on real IoT data. More specifically, we identify IoT data sources over the Web and develop a crawler engine to collect large-scale real-world IoT data for the first time. We make the results of our work available to the public in order to assist the community in the future research. In particular, we collect the data of nearly two million Internet connected objects and study trends in IoT using a real-world query set from an IoT search engine. Based on the collected data and our analysis, we identify the typical characteristics of IoT data. The most intriguing finding of our study is that IoT data is mainly disseminated using Web Mapping while the emerging IoT solutions such as the Web of Things are currently not well adopted. On top of our findings, we further discuss future challenges and open research problems in the IoT area.

The value of customers for any business cannot be over-emphasised, and it is crucial for companies to develop a good understanding of their customer base. One of the most important pieces of information is to estimate the share of wallet for each individual customer. In the literature a related concept is often referred to as customer equity that provides aggregated measures such as the business market share. The current trend in personalising marketing campaigns have led to more granular estimation of wallet share, than the entire customer base or aggregated segments of customers. The current trend in personalising marketing and business strategies have lead to more granular estimation of wallet shares than the entire customer base or aggregated segments of customers. Existing research in this area requires access to additional information about customers, often collected via various surveys. However, in many real-world scenarios, there are circumstances where survey data are unavailable or unreliable. In this paper, we present a new customer wallet share estimation approach. In the proposed approach, a predictive model based on decision trees facilitates an accurate estimation of wallet shares for customers relying only on transaction data. We have evaluated our approach using real-world datasets from two businesses from different industries.

Graph matching is important for a wide variety of applications in different domains such as social network analysis and knowledge discovery. Despite extensive research over the last few decades, graph matching is still challenging particularly when it comes with new conditions and constraints. In this paper, we focus on a new class of graph matching, in which each node can accept multiple labels instead of one. In particular, we address the problem of finding the top-k nodes of a data graph which best match a labeled query node from a given pattern graph. We firstly prove this to be an NP-Complete problem. Then, to address this issue and improve the scalability of our approach, we introduce a more flexible graph simulation, namely surjective simulation. This new graph simulation reduces the unnecessary complexity that is due to the unnecessary constraints imposed by the existing definitions while achieving high-quality matching results. In addition, our approach is associated with an early stop strategy to further boost the performance. To approximate the maximum size of a simulation, our approach utilizes Metropolis Hastings algorithm and ranks the top-k matches after computing the set of surjective simulations. The experimental results over social network graphs demonstrate the efficiency of the proposed approach and superiority over existing approaches.

Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications as they provide facts and relationships that can be automatically understood by machines. Curated knowledge bases usually use Resource Description Framework (RDF) as the data representation model. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has problems like network instability and latency, which affect the query efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL queries to vector representation that are fed into machine-learning algorithms. A time-aware smoothing-based method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive experiments performed on real-world queries showcase the effectiveness of our approach, which outperforms the state-of-the-art work in terms of the overall querying speed.

Web of Things (WoT) is becoming increasingly interesting for researchers and professionals over the past few years. It provides numerous opportunities by disseminating the data that is generated by physical things and fills the gap between the physical and the virtual world. Despite its importance, WoT search has not been studied enough in the past. Given the dynamic challenge of the WoT, collecting data from WoT resources is not well developed. Furthermore, the effectiveness of WoT search can be significantly improved if the users' intention of the search is also considered. This can be facilitated by knowing the existing status of the WoT in real-world. In this chapter, we address multiple challenges in this area. Firstly, we depict the analytical structure of the future WoT which facilitate crawling, indexing and searching the data from physical things. Secondly, we show how we can identify WoT and extract the data from it. Thirdly, we use our crawler to crawl and analyse WoT data on the Internet. Furthermore, we provide a showcase in the analysis of the flights delay data. Finally, we provide a discussion on future research in this area.

The rapidly growing paradigm of the Internet of Things (IoT) requires new search engines, which can crawl heterogeneous data sources and search in highly dynamic contexts. Existing search engines cannot meet these requirements as they are designed for traditional Web and human users only. This is contrary to the fact that things are emerging as major producers and consumers of information. Currently, there is very little work on searching IoT and a number of works claim the unavailability of public IoT data. However, it is dismissed that a majority of real-time web-based maps are sharing data that is generated by things, directly. To shed light on this line of research, in this paper, we firstly create a set of tools to capture IoT data from a set of given data sources. We then create two types of interfaces to provide real-time searching services on dynamic IoT data for both human and machine users.

Recently, resolving the problem of evaluation and ranking the potential suppliers has become as a key strategic factor for business firms. With the development of intelligent and automated information systems in the information era, the need for more efficient decision making methods is growing. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable criteria assuming that compromising is acceptable to resolve conflicts. On the other side objective weights based on Shannon entropy concept could be used to regulate subjective weights assigned by decision makers or even taking into account the end-users' opinions. In this paper, we treat supplier selection as a group multiple criteria decision making (GMCDM) problem and obtain decision makers' opinions in the form of linguistic terms. Then, these linguistic terms are converted to trapezoidal fuzzy numbers. We extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon entropy concept. The final result is obtained through next steps based on factors R, S and Q. A numerical example is proposed to illustrate an application of the proposed method.

Typically, supplier selection constitutes one of the most important stages of supply chain management and a variety of basic and hybrid MCDM approaches have been deployed to provide this problem with well-suited solutions. This paper investigates a new novel approach for this problem based on ANP and fuzzy TOPSIS methods while it takes into account the risk factor solely regarding the decision maker’s venture strategy. In addition to an ANP model that determines the effects of decision criteria, in the proposed approach, a set of 5 risk categories has been deployed to affect the decision maker’s choice by normalizing the weights of risk criteria.

Recently, with the environmental crisis, Green supply chain management (or GSCM), and in particular closed loop supply chain model, has received considerable attention by researchers. Closed loop supply chain model aims at reduction of waste and generating profit for enterprises through integrating forward and reverse logistics. Unfortunately, there is limited research on general models for closed loop supply chains in literature. In this paper, extending and enhancing previous models, a general model is proposed for closed loop supply chains using linear programming. The goal of this study is to minimize the leakage of a closed loop supply chain to avoid waste and reduce SCM costs.

PurposeThis research aims to focus on supply chain coordination and its three determinants, i.e. responsibility interdependence, uncertainty, and inter‐functional conflict, developing research propositions to conceptualize how supply chain coordination is driven by its determinants.Design/methodology/approachThe research propositions are tested by a case study in a shoe factory to examine the differences between the theory and the practice of coordination.FindingsThe findings show that the firm carried out only piecemeal coordination in defining and fulfilling customer needs.Originality/valueA supply chain is known as a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers. Supply chain coordination plays a critical role in integrating different actors along the supply chain to enhance performance.

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Non-negative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.

The Internet of Things (IoT) envisions smart objects collecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic technologies, such as Linked Data, which can facilitate machine-to-machine (M2M) communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-automata, to meet the high performance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-automata, the proposed system can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.

Things search engines play a key role in increasing the visibility of the emerging Internet of Things IoT paradigm. Developing an innovative search approach is a fundamental step to lay the foundations of future IoT search engines. Currently, the most adopted approach for searching things is based on keyword search. Unfortunately, keyword search does not provide enough functionality for an IoT search engine. Correlating things based on their attributes is an emerging approach which can potentially improve the IoT search process. Since in reality there might exist a number of different correlations between a pair of everyday objects, integrating and applying them in IoT search is challenging. In this paper, we propose the ECS Extract, Cluster, Select framework. Our framework contains a novel approach to extract and integrate different types of correlation graphs with a spectral clustering method and a selection method to improve the coherence and the diversity of top-k results for a given search query. We evaluate our framework through extensive experiments using real-world datasets from different domains of IoT applications. The results show that the quality of search results improves greatly after we diversify the results of IoT data queries.

The Web of Things (WoT) paradigm introduces novel applications to improve the quality of human lives. Dynamic ridesharing is one of these applications, which holds the potential to gain significant economical, environmental, and social benefits particularly in metropolitan areas. Despite the recent advances in this area, many challenges still remain. In particular, handling large-scale incomplete data has not been adequately addressed by previous works. Optimizing the taxi/passengers schedules to gain the maximum benefits is another challenging issue. In this paper, we propose a novel system, MARS (Multi-Agent Ridesharing System), which addresses these challenges by formulating travel time estimation and enhancing the efficiency of taxi searching through a decremental search approach. Our proposed approach has been validated using a real-world dataset that consists of the trajectories of 10,357 taxis in Beijing, China.

With the emergence of high speed wireless networks and parallel advancements in Internet-based technologies such as the semantic Web, Web services, agent-based technologies, and context awareness, the realization of the vision of the intelligent wireless Web (IWW) has become a possibility. Under a project named SWESS (Smart WEb-based Supply chain coordination Software) in Shiraz University of Technologypsilas IT Laboratory, we simulated software that implements previous proposed framework. In this paper, after taking a brief look at a practical framework for mobile real-time supply chain coordination via IWW, we are going to investigate how we simulated such system and its performance.

With the rapid developments in e-learning and educational systems, updating and redesigning a learning activity is too costly and time-consuming. Web-based educational systems must be designed in order to enable reusing and repurposing of learning activities. As the most adopted framework in e-learning systems offered by ADL, SCORM can help us to reuse previous Content Objects again for developing a new learning activity. On the other hand, Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This promise is particularly attractive for creating software that operates in environments that are distributed and open, such as the Internet and we believe this could be the same for e-learning systems. The components and technologies discussed in the first and second sections constitute the building blocks for the realization of the futuristic vision of the new generation of SCORM conformant e-learning systems, which would allow intelligent access to highly specific and customized learning content by users.

Rapidly growing data in the loT is a crucial issue that must be handled effectively. Storing and retrieving data is the first step towards solving this issue. Besides, the diversity of operating systems that run on Internet nodes can affect data storage framework. Various approaches for handling data storage over networks can be found in literature. OBS is one of the recent approaches in this area and due to its flexibility, performance and scalability; it can be a good candidate for building a framework to handle network data storage. Discussing different options for data storage framework for the loT, this paper tailors OBS and proposes a cross platform framework for this problem.

Querying on SPARQL endpoints may be unsatisfactory due to high latency of connections to the endpoints. Caching is an important way to accelerate the query response speed. In this paper, we propose SPARQL Endpoint Caching Framework (SECF), a client-side caching framework for this purpose.\ud In particular, we prefetch and cache the results of similar queries to recently cached query aiming to improve the overall querying performance. The similarity between queries are calculated via an improved Graph Edit Distance (GED) function. We also adapt a smoothing method to implement the cache replacement. The empirical evaluations on real world queries show that our approach has great potential to enhance the cache hit rate and accelerate the querying speed on SPARQL endpoints.

In the emerging Internet of Things (IoT) environment, things are interconnected but not interlinked. Interlinking relevant things offers great opportunities to discover implicit relationships and enable potential interactions among things. To achieve this goal, implicit correlations between things need to be discovered. However, little work has been done on this important direction and the lack of correlation discovery has inevitably limited the power of interlinking things in IoT. With the rapidly growing number of things that are connected to the Internet, there are increasing needs for correlations formation and discovery so as to support interlinking relevant things together effectively. In this paper, we propose a novel approach based on Multi-Agent Systems (MAS) architecture to extract correlations between smart things. Our MAS system is able to identify correlations on demand due to the autonomous behaviors of object agents. Specifically, we introduce a novel open-sourced framework, namely CEIoT, to extract correlations in the context of IoT. Based on the attributes of things our IoT dataset, we identify three types of correlations in our system and propose a new approach to extract and represent the correlations between things. We implement our architecture using Java Agent Development Framework (JADE) and conduct experimental studies on both synthetic and real-world datasets. The results demonstrate that our approach can extract the correlations at a much higher speed than the naive pairwise computation method.