Knowledge graphs (KGs) play a central role in enabling structured reasoning for AI applications. Recent research has advanced two prominent extensions of KGs: multimodal KGs, which integrate diverse data sources such as text, images, audio, and video; and temporal KGs, which capture the dynamic evolution of knowledge over time. However, these two paradigms remain largely disjoint—multimodal KGs typically ignore temporal dynamics, while temporal KGs overlook rich multimodal context. To bridge this gap, we introduceFocusKG, the first discrete-time multimodal knowledge graph that unifies four modalities (text, image, audio, and video) with temporal annotations. Built from the Focus news program, FocusKG captures real-world events as temporally grounded multimodal facts, offering new expressiveness for time-sensitive tasks. We further propose aDiscrete-TimeMultimodal Knowledge GraphEmbedding (DTME) method, a novel framework tailored for learning representations over temporal multimodal KGs. DTME is composed of three key modules: (1) Multimodal Preprocessing, which encodes raw inputs from each modality into vector representations using modality-specific encoders; (2) Cross-Modal Graph Enhancer, which captures semantic interplay between entity and relation modalities through role-aware subgraph construction and a modality interaction network; and (3) Multi-Branch Time Aggregation, which injects temporal signals by modeling interactions between time and modality-aware features. Extensive experiments demonstrate FocusKG's value and DTME's efficacy. On link prediction, DTME outperforms state-of-the-art multimodal KG models and temporal KG models. We release FocusKG as a benchmark to foster research in multimodal temporal reasoning.

The quantitative analysis of the left ventricle (LV) contractile function is one of the key steps in the assessment of cardiovascular disease. Such analysis greatly depends on the accurate delineation of LV boundary from cardiac sequences. However, segmentation of the LV still remains a challenging problem due to its subtle boundary, occlusion, and image inhomogeneity. To overcome such difficulties, the authors propose a novel segmentation method by incorporating a dynamic shape constraint into the weighting function of the random walks segmentation algorithm. This approach involves iterative updates on the intermediate result to achieve the desired solution. The inclusion of a shape constraint restricts the solution space of the segmentation result to handle misleading information that may come from noise, weak boundaries and clutter, leading to increased robustness of the algorithm. The authors describe the details of the proposed method and demonstrate its effectiveness in segmenting the LV from real cardiac magnetic resonance (CMR) image sets. The experimental results demonstrate that the proposed method obtains better segmentation performance than the standard method.

Personalized location-based service recommendation is an important trend in the development of online ecommerce applications. In this work, we integrate the application of location-based service with recommendation technologies to present a hybrid recommendation model and a prototype system (HiPerData) to evaluate and measure the validity based on the Yelp dataset. In order to solve the four recommendation problems, we improve a predictive feature-based regression model, and combine the results of a set of collaborative filtering algorithms, which includes: SVD (Singular value decomposition), SVR (Support vector regression), SGD (Stochastic gradient descent), etc. Unlike previous approaches, we apply multiple methods to pre- and post-process the dataset and predict ratings, for example, a weighted pairwise preference regression for cold start problems, etc. We enhance the neighborhood-based approach leading to a substantial improvement of prediction accuracy. Our method gave the best overall results with a root mean square error of 1.22724.

As scientific applications like Computational Fluid Dynamics (CFD) simulations generate more and more data, co-processing becomes the most cost effective way to process the vast amount of data generated by these simulation. In a co-processing environment, analysis and/or visualization of intermediate results occur concurrently to the simulation itself. Improved efficiency and early insight into the simulation process and results are potential advantages in comparison to postprocessing, where analysis and/or visualization are performed after the completion of the simulation. To enable co-processing, however, intermediate data needs to be shared between simulation and data analysis, and some degree of coordination may be required to maintain the correctness of both simulation and data analysis. The overhead incurred to facilitate data sharing and coordination may well offset benefits gained, particularly where distributed, large-scale systems are involved as workload sharing, processor affinity and data locality introduce significant effects to the overall performance. In this paper, we propose a co-processing framework to address these issues. The empirical benchmarking results suggest that co-processing overhead tasks scale well with the system size, the overall gain of about 20% in turnaround time compared to post-processing and that the coprocessing framework allows simulation and data analysis task to scale up to their individual limits.

We address the problem of scheduling a class of large-scale applications inspired from real-world on hybrid Clouds, characterized by a large number of homogeneous and concurrent tasks that are the main sources of bottlenecks but open great potential for optimization. We formulate the scheduling problem as a new sequential cooperative game and propose a communication- and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, make span, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.

Graph processing has become popular for various big data analytic applications. Google's Pregel framework enables vertex-centric graph processing in distributed environment based on Bulk Synchronous Parallel (BSP) model. However, the BSP model is inefficient for many complex graph algorithms requiring graph traversals, as only a small number of vertices really update states in each super step. In this paper, we propose an hierarchical parallelization mechanism, taking the advantages of both synchronous (warp-level) and asynchronous (task-level) parallelization approaches. In addition, a runtime task scheduling mechanism is proposed, relying on real-time monitoring or prediction of resource utilization. Experiments have verified that the hierarchical parallelization mechanism can expose greater parallelism, and thus, increase resource utilization significantly. Moreover, the runtime scheduling mechanism can avoid aggressive resource competition, and thus, further enhance the performance of the parallelized graph processing.

Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a huge number of homogeneous and concurrent bags-of-tasks that are the main sources of bottlenecks but open great potential for optimization. The scheduling problem is formulated as a new sequential cooperative game and propose a communication and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, makespan, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.

While hybrid computing environments provide good potential for achieving high performance and low economic cost, it also introduces a broad set of unpredictable overheads especially for running data-intensive applications. This paper describes a novel approach which refines workflow structures and optimizes intermediate data transfers for large-scale scientific workflows containing thousands (or even millions) of tasks. The proposed method includes pre- and post-partitioning of workflows and data-flow optimization. Firstly, it partitions a workflow by identifying the critical path of the task graph. Secondly, it controls the granularity of partitions to reduce the complexity of task graph in order to process large-scale workflows. Thirdly, it optimizes the data-flow based on the scheduling to minimize its communication overheads. Our proposed approach is able to handle complex data flows and significantly reduce data transfer by replacing individual tasks according to data dependencies. We conducted experiments using real applications such as Montage and Broadband, and the results demonstrated the effectiveness of our methods in achieving low execution time with low communication overhead in a hybrid computing environments.

Grid schedulers require individual activity performance predictions to map workflow activities on different Grid sites. The effectiveness of the scheduling systems is hampered by inaccurate predictions due to the inability of existing predictors to effectively model the dynamic and heterogeneous nature of Grid resources, or the wide range of problem sizes and runtime arguments. To address this deficiency, we propose a hybrid Bayesian-neural network approach to dynamically model and predict the execution time of activities in real workflow applications. Bayesian network is used for a high-level representation of activities performance probability distribution against different factors affecting the performance. The important attributes are dynamically selected by the Bayesian network and fed into a radial basis function neural network to make further predictions. Our approach is generic to any type of scientific applications, and flexible to import expert knowledge to further improve accuracies. Experimental results for activities from three realworld workflow applications are presented to show effectiveness of our approach.

Scheduling large-scale applications on the Grid is a fundamental challenge and is critical to application performance and cost. Large-scale applications typically contain a large number of homogeneous and concurrent activities which are main bottlenecks, but open great potentials for optimization. This paper presents a new formulation of the well-known NP-complete problems and two novel algorithms that addresses the problems. The optimization problems are formulated as sequential cooperative games among workflow managers. Experimental results indicate that we have successfully devised and implemented one group of effective, efficient, and feasible approaches. They can produce soultuins of significantly better performance and cost than traditional algorithms. Our algorithms have considerably low time complexity and can assign 1,000,000 activities to 10,000 processors within 0.4 second on one Opteron processor. Moreover, the solutions can be practically performed by workflow managers, and the violation of QoS can be easily detected, which are critical to fault tolerance.

This paper describes a novel approach to fault detection and prediction on the grid based on data mining techniques. Data mining techniques are here applied as a mean to effectively process the significant amount of captured data from grid sites, services, workflows and activities. The paper provides a first approach of proposed techniques in terms of its ability of utilizing relevant information and the fault tolerance requirements. Such approach is one intelligent, distributed framework of fault detection and prediction for anomaly and failed activity by using resource- and workflow-based information. We use fault predictions to improve the performance of the workflow execution by avoiding potential faults of activities