Pedestrian trajectory prediction in high-density and dynamic environments remains a significant challenge due to the limitations in modeling complex group interactions and evolving pedestrian-environment dependencies. This article presents KG-PTP, a knowledge graph-driven trajectory prediction framework that incorporates a dynamic trajectory knowledge graph (DTKG), a direction-aware clustering algorithm (D-DBSCAN), and an internal–external behavior modeling component (IN-EX). The DTKG enables semantic integration of heterogeneous spatial–temporal information and supports real-time updates. D-DBSCAN dynamically groups pedestrians based on motion direction and density, while IN-EX captures both individual motivations and external environmental influences. Experimental evaluations on ETH pedestrian dataset (ETH) and UCY crowd dataset (UCY) datasets demonstrate that KG-PTP achieves 12.3% and 15.7% reductions in average displacement error (ADE) and final displacement error (FDE), respectively, compared with state-of-the-art baselines. Furthermore, the proposed framework is validated through crowd evacuation simulations, confirming its applicability to public safety scenarios.

Temporal knowledge graphs (TKGs) effectively capture the dynamic evolution of events over time, emerging as a critical driving force in the advancement of artificial intelligence. In recent years, temporal knowledge graph reasoning (TKGR) has garnered significant attention for its ability to address the intrinsic incompleteness of TKGs. Among various TKGR methods, reinforcement learning (RL)-based multi-hop reasoning stands out due to the decision-making capabilities and interpretability. However, existing multi-hop reasoning methods are predominantly designed for the transductive setting where test entities are observed during training, and they exhibit limited performance in the fully-inductive setting where training and test entities are entirely disjoint. Moreover, the sparse links of newly emerged unseen entities in TKGs hinder multi-hop reasoning methods from utilizing sufficient actions to construct multihop relational paths, ultimately impairing reasoning accuracy. To address these challenges, we propose ARLIE (Adaptive Reinforcement Learning with Inductive Embeddings), a novel method capable of conducting multi-hop reasoning in both fully-inductive and transductive settings over TKGs. Specifically, ARLIE consists of the following two key components. (1) A context-based inductive representation method generates finegrained embeddings for unseen entities by exploiting queryrelated contextual information. (2) After obtaining temporal evolution and semantic dependencies of unseen entities, an actionaugmented adaptive RL framework leverages diverse actions to infer missing elements step-by-step over TKGs. Finally, experimental results show that ARLIE surpasses state-of-the-art TKGR methods across both fully-inductive and transductive settings.

In crowd evacuation research, the knowledge contained in crowd evacuation is very complex and is multisource. Crowd evacuation scenarios restrict pedestrians’ movement decision-making, and the movement states of the crowd imply the movement characteristics. However, the existing studies on crowd evacuation navigation approach cannot make full use of the complex and multisource crowd evacuation knowledge, which reduces the effect of the evacuation navigation. To solve this problem, a new crowd evacuation navigation approach based on multisource knowledge is proposed. First, we collect relevant data on crowd evacuation using an image sensor network and establish a crowd evacuation knowledge graph to organize and store this data. Second, the explicit knowledge of scene structure and crowd movements is represented based on the crowd evacuation knowledge graph. Then, a deep-learning-based tacit knowledge model (DLTKM) is designed to extract the tacit knowledge of different groups and scene entities. Finally, a new crowd evacuation navigation approach based on wireless sensor network and related knowledge representations is designed to plan evacuation paths for evacuees. The experiment results show that this approach can plan reasonable evacuation paths for pedestrians, and improve the efficiency of crowd evacuations.

Summary A common issue among digital health applications is that they are based on project‐specific solutions and are developed from scratch, which results in redundant development and lack of data and technology reuse. This can be seen in, for example, building study specific websites and mobile frontends, deploying customized infrastructures, and collecting data that may have been collected in other studies and projects. However, existing data and technology are not easily shareable between projects due to large investment required to address data exchanging mechanism, data and technology sovereignty, data security and resource discovery, while the benefit for the resource owners is uncertain. In this article, we present a supporting framework, named DHLink, to address this issue on two fronts. First, the DHLink framework securely connects multiple digital health applications, facilitates real‐time data sharing, and supports rapid application development by reusing data and technology. Second, to aid in rapid development of new digital health applications, a set of highly generic and reusable microservices has been developed as the initial resources available in the DHLink ecosystem. Two proof‐of‐concept use cases outlined show the effectiveness of DHLink for both data sharing between existing applications, and rapid development of a new application.

Summary Small social groups based on kinship or friendships are ubiquitous in human crowds. Therefore, the effect of social groups on crowd evacuations and that of crowd evacuations on social groups must be investigated. To simulate the group phenomenon when an emergency occurs, we propose an improved social force model that takes into account the social group relationship among the population, and based on our proposed model, a novel grouping algorithm predicated on non‐uniform binary grid partitioning is put forward. The approach initially maps the individuals into the plane space, and then it adopts top‐down binary grid partitioning iteratively until the divided grid contains only the individuals with relations; then, the values of the relation and density of the non‐empty grid cells are calculated, and the grids are sorted according to these values. After sorting, selecting, merging, and forming the core grids, the other grids are merged to the core grids. We have compared the algorithm with the hierarchical classification algorithm and the grid‐based algorithm. The results show that the accuracy, speed, and scalability are all advantages. We also establish a simulation platform to illustrate the proposed grouping algorithm and the improved social force model for crowd evacuation simulation.

In the era of big data, new challenges occur in the field of data federation research. New types of data sources with new formats of data have emerged, and end users need to conduct complex search and data analytical tasks, which impose requirements such flexible data federation, customized security mechanism and high-performance processing (for example, near real time query). To address these challenges, this paper proposes a data federation platform named FEDSA and reports on an initial implementation. Distinctive features of the platform include process-driven data federation, Data Federation as a Service, a simple query language over a high-level common data model, data security protection over all federation services, query re-writing and full distribution. We demonstrate how these features address the challenges, discuss the performance of the current implementation, and outline future extensions.

The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, due to the pay-as-you-go model, the total cost of using cloud services depends on the consumption of storage, computation and bandwidth resources which are three key factors for the cost of IaaS-based cloud resources. In order to reduce the total cost for data, given cloud service providers with different pricing models on their resources, users can flexibly choose a cloud service to store a generated dataset, or delete it and choose a cloud service to regenerate it whenever reused. However, finding the minimum cost is a complicated yet unsolved problem. In this paper, we propose a novel algorithm that can calculate the minimum cost for storing and regenerating datasets in clouds, i.e., whether datasets should be stored or deleted, and furthermore where to store or to regenerate whenever they are reused. This minimum cost also achieves the best trade-off among computation, storage and bandwidth costs in multiple clouds. Comprehensive analysis and rigid theorems guarantee the theoretical soundness of the paper, and general (random) simulations conducted with popular cloud service providers' pricing models demonstrate the excellent performance of our approach.

Data reliability and storage costs are two primary concerns for current Cloud storage systems. To ensure data reliability, the widely used multi-replica (typically three) replication strategy in current Clouds incurs a huge extra storage consumption, resulting in a huge storage cost for data-intensive applications in the Cloud in particular. In order to reduce the Cloud storage consumption while meeting the data reliability requirement, in this paper we present a cost-effective data reliability management mechanism named PRCR based on a generalized data reliability model. By using a proactive replica checking approach, while the running overhead for PRCR is negligible, PRCR ensures reliability of the massive Cloud data with the minimum replication, which can also serve as a cost effectiveness benchmark for replication based approaches. Our simulation indicates that, compared with the conventional three-replica strategy, PRCR can reduce from one-third to two-thirds of the Cloud storage space consumption, hence significantly lowering the storage cost in a Cloud.

The proliferation of cloud computing allows scientists to deploy computation and data intensive applications without infrastructure investment, where large generated datasets can be flexibly stored with multiple cloud service providers. Due to the pay-as-you-go model, the total application cost largely depends on the usage of computation, storage and bandwidth resources, and cutting the cost of cloud-based data storage becomes a big concern for deploying scientific applications in the cloud. In this paper, we propose a novel algorithm that can automatically decide whether a generated dataset should be 1) stored in the current cloud, 2) deleted and re-generated whenever reused or 3) transferred to cheaper cloud service for storage. The algorithm finds the trade-off among computation, storage and bandwidth costs in the cloud, which are three key factors for the cost of storing generated application datasets with multiple cloud service providers. Simulations conducted with popular cloud service providers' pricing models show that the proposed algorithm is highly cost-effective to be utilised in the cloud.

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the Cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. To address this issue, in this paper we present a novel cost-effective data reliability management mechanism named PRCR, which proactively checks the availability of replicas for maintaining data reliability. Our simulation indicates that, comparing with the typical 3-replica replication strategy, PRCR can reduce the storage space consumption by one-third to two-thirds, hence reduce the storage cost significantly in the Cloud.

Nowadays, large-scale Cloud-based applications have put forward higher demand for storage ability of data centres. Data in the Cloud need to be stored with high efficiency and cost effectiveness while meeting the requirement of reliability. While in current Cloud systems, typical 3-replicas data replication strategies are applied for data reliability, in this paper we propose a novel cost-effective dynamic data replication strategy which facilitates an incremental replication method to reduce the storage cost and meet the data reliability requirement at the same time. This replication strategy works very well especially for data which are only used temporarily and/or have a relatively low reliability requirement. The simulation shows that our replication strategy for reliability can reduce the data storage cost in data centres substantially.

Due to the dynamic nature of cloud computing, how to achieve satisfactory QoS (Quality of Service) in cloud workflow systems becomes a challenge. Meanwhile, since QoS requirements have many dimensions, a unified system design for different QoS management components is required to reduce the system complexity and software development cost. Therefore, this paper proposes a generic QoS framework for cloud workflow systems. Covering the major stages of a workflow lifecycle, the framework consists of four components, viz. QoS requirement specification, QoS-aware service selection, QoS consistency monitoring and QoS violation handling. While there are many QoS dimensions, this paper illustrates a concrete performance framework as a case study and briefly touches others. We also demonstrate the system implementation and evaluate the effectiveness of the performance framework in our cloud workflow system.