Abstract

Abstract

In today’s smart environments, the serviceli-zation of various resources has produced a tremendous number of IoT- and cloud-based smart services. Thanks to the pivotal role of pillar paradigms, such as edge/cloud computing, Internet of Things, and business process management, it is now possible to combine and translate these service-like resources into configurable workflows, to cope with users’ complex needs. Examples include treatment workflows in smart healthcare, delivery plans in drone-based missions, transportation plans in smart urban networks, etc. Rather than composing atomic services to obtain these workflows, reusing existing process fragments has several advantages, mainly the fast, secure, and configurable compositions. However, reusing smart process fragments has not yet been addressed in the context of smart environments. In addition, existing solutions in smart environments suffer from the complexity (e.g., multi-modal transportation in smart mobility) and privacy issues caused by the heterogeneity (e.g., package delivery in smart economy) of aggregated services. Moreover, these services may be conflicting in specific domains (e.g. medication/treatment workflows in smart healthcare), and may affect user experience. To solve the above issues, the present paper aims to accelerate the process of generating configurable treatment workflows w.r.t. the users’ requirements and their smart environment specificity. We exploit the principles of software reuse to map each sub-request into smart process fragments, which we combine using Cocke-Kasami-Younger (CKY) method, to finally obtain the suitable workflow. This contribution is preceded by a knowledge graph modeling of smart environments in terms of available services, process fragments, as well as their dependencies. The built information network is, then, managed using a graph representation learning method, in order to facilitate its processing and composing high-quality smart services. Experimental results on a real-world dataset proved the effectiveness of our approach, compared to existing solutions.

Abstract

Nowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users’ lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services’ capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services’ QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services’ usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single-network settings (a single service repository), while smart services’ consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross-network recommender system for smart cities. We first model the multiplex network of smart services’ providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors’ cross-network relations, we compute the similarity between users and we define algorithms for exploring the smart services’ information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross-network recommendation and the quality of produced recommendations, compared to state-of-the-art single-network recommendation.

Abstract

Business Process as a Service (BPaaS) has recently emerged from the synergy between business process management and cloud computing, allowing companies to outsource and migrate their businesses to the cloud. BPaaS management refers to the set of operations (decomposition, customization, placement, etc.) that maintain a high-quality of the deployed cloud-based businesses. Like its ancestor SaaS, BPaaS placement consists on the dispersion of its composing fragments over multiple cloud availability zones (CAZ). These latter are characterized by their huge, diverse and dynamic data, which are exploited to select the high-performance servers holding BPaaS fragments, while preserving their constraints. These fragments’ relations and their placement schemes constitute a dynamic BPaaS information network. However, the few existing BPaaS solutions adopt a static placement strategy, while it is important to take the CAZ dynamic and uncertain nature into account. Also, current solutions do not properly model the BPaaS environment. To offer an efficient BPaaS placement scheme, we combine prediction and learning capabilities, which will help identify the migrating fragments and their new hosting servers. We first model the BPaaS context as a heterogeneous information network. Then, we apply an incremental representation learning approach to facilitate its processing. Using the principles of proximity-aware representation learning, we infer useful knowledge regarding BPaaS fragments and the available servers at different CAZ. Finally based on the degree of closeness between the BPaaS environment’s entities (e.g., fragments, servers), we select the optimum cloud availability zone on which the resource-consuming BPaaS fragments are migrated based on a proposed placement scheme. Obtained results were very promising compared to traditional BPaaS placement solutions.

Abstract

Multimodal data, which includes various data formats such as image, video, text, and sensor data, is essential for urban traffic management. The lack of proven multimodal transportation data has been a significant challenge for urban planners, leading to biased or incomplete estimates of travel demand, mode choice, and network performance. Multimodal data integration offers a valuable resource for understanding and optimizing traffic control and management. However, the heterogeneity of the data, various kinds of noise, alignment of modalities, and techniques to handle missing data are some of the challenges that arise. This paper presents a novel multimodal dataset which is the first of its kind, its scraped from England Highways, incorporating speed, flow, and camera images for the M60, M25, and M1 motorways. The dataset provides a comprehensive view of traffic behavior at specific junctions, enabling detailed analysis and real-world applications. By integrating previously disparate data sources, this dataset offers a valuable resource for understanding and optimizing traffic control and management. The paper outlines the dataset’s development, including the gathering of speed and flow data, and the use of image scraping techniques to capture CCTV images. The potential applications of the dataset for traffic control, planning, and optimization are also discussed. Overall, this multimodal dataset represents a significant contribution to the field, with implications for the development of advanced traffic management systems and the improvement of transportation infrastructure

Abstract

in recent years, traffic flow prediction has presented challenges in the management of transportation systems. It is a crucial part of Intelligent Transportation Systems (ITS). The complexities of various transportation data, spatial and temporal dependencies on road networks, and multimodalities, such as public transit, pedestrian flow, and bike sharing, make it a challenging task to forecast traffic flow accurately. Numerous works have been introduced to address these challenges, but few have simultaneously considered these factors, resulting in limited success. In this study, a model is proposed to integrate Graph Convolutional Networks (GCN) and Bidirectional Long Short-Term Memory (BiLSTM). This model utilizes the advantages of GCN in handling spatial data and capturing dependencies in road networks, combined with BiLSTM’s capability in learning temporal dynamics. The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method addresses the limitations of existing methods that fail to consider spatial-temporal dependencies and multimodalities, leading to improved prediction accuracy and efficiency

Abstract

The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset.