Yang Guo

Spatio-temporal data are continuously generated and grow exponentially with the rapid proliferation of mobile devices. Efficient storage and indexing of spatio-temporal Big Data are crucial for supporting fast queries. Due to their high write throughput and scalability, distributed NoSQL data stores such as HBase are widely adopted as storage engines in spatio-temporal Big Data systems. These systems require a key-value-based storage model to convert one or a group of spatio-temporal data points into a single key-value pair. However, existing techniques cannot govern an inherent dilemma caused by adding a time dimension within spatial information, that is, we can either optimize for space-preferred or time-preferred queries but not both. In this paper, we propose Cymo, a flexible storage model with query-aware indexing that dynamically adapts to varying query patterns across different spatial regions and evolving query behaviors over time. Our key idea is to partition the spatio-temporal space into multiple subspaces, allowing each subspace to optimize its storage model based on its specific query characteristics. Cymo features a learning model that predicts query patterns for each subspace using historical workloads. Additionally, it introduces a virtual layer that abstracts the heterogeneity of different storage models, providing a unified query interface. This design ensures that storage model variations remain transparent to applications while enabling effective query optimization based on diverse query patterns. We have implemented Cymo on HBase and integrated it into GeoMesa, a representative spatio-temporal Big Data system. Experimental results on real taxi datasets demonstrate that Cymo significantly improves query performance, achieving speedups of 1.53× to 10× compared to GeoMesa. The open-source implementation of Cymo has been released for public access.

To manage massive trajectory data, we propose a novel cloud-based trajectory data management technique, Springbok, which leverages cloud storage to balance performance and monetary costs. Unlike existing key-value, relational, and time series databases, Springbok natively models trajectories as first-class data objects via a spatio-temporal series data model, enabling efficient insertion, query processing, and cost-aware storage management in cloud environments. It further employs an optimized indexing scheme that accounts for both the characteristics of trajectory data and the properties of cloud storage, enabling efficient query execution. In addition, Springbok adopts a tiered cloud storage architecture with carefully designed data layouts, flushing, access, and compression policies, guided by cloud storage performance characteristics and pricing models, to jointly optimize performance and cost. We implemented a fully functional prototype of Springbok supporting core trajectory queries, deletion, and crash recovery, and evaluated it using both real-world and synthetic datasets. The results show that Springbok achieves comparable or better performance than state-of-the-art systems in most cases while significantly reducing monetary costs, demonstrating its effectiveness in balancing performance and cost.

With the rapid growth of mobile devices and applications, a prodigious number of spatio-temporal data are generated constantly. To process these data for applications like traffic forecasting, existing spatio-temporal systems rely on the move-data-to-computation paradigm. However, this approach incurs significant data movement overhead between hosts and storage devices, particularly when a spatio-temporal query is executed on a non-preferred data layout or when the query has a small result size due to its inherent nature. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on real hardware and demonstrated how to apply fine-grained techniques to accelerate various spatio-temporal queries. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to 81% for widely used spatio-temporal queries compared to host computing solutions.

Mobile devices have gained immense popularity in recent years and have become an integral part of people’s daily lives. This surge in usage presents new challenges for energy conservation, particularly concerning the screens of mobile phones. Not only are screens being used for longer durations, but also their adjustment needs to be dynamically conducted during runtime. Existing approaches for display energy optimization mainly focus on the display content while disregarding user interactions. This limitation prevents the full exploitation of opportunities to reduce screen energy consumption during runtime. Detection-based display energy optimization approaches incorporate user interactions to some extent. However, these approaches require additional hardware and offer relatively coarse-grained control. To address these challenges, we propose QLEO, a Q-learning-based runtime display energy optimization scheme for Android systems. QLEO takes both the display and user interaction features into account. By dynamically classifying the user state into active and idle states based on these features, we enable adaptive screen brightness adjustments according to the user’s current state. We formulate display energy optimization as a constrained dynamic optimization problem and propose an integrated Q-learning model to effectively solve it. QLEO is designed as a module within the hardware abstraction layer, capable of obtaining display and user interaction features from the upper Android framework, and directly controlling the screen brightness through the underlying Linux kernel. We have implemented QLEO on real hardware and released the source code for public access. The experimental results demonstrate that QLEO achieves significant energy reduction without compromising the user experience.

With the development of building industrialization, the prefabricated buildings have received unprecedented attention. Most of the steel structures are prefabricated in the factory and installed in the construction site, which is a typical prefabricated building structure. For the commonly used steel frame structures, the research status of the seismic and collapse resistance of the beam-column joints were summarized respectively. Then, the research status of the joints with seismic and collapse resistance was presented. Existed research showed that: The engineering design is no longer satisfied with the good seismic performance. The building structures need to possess recoverable function at the same time. The steel frame joints with recoverable function mainly include: the bolted joints; the joints with energy dissipation damper; the hinged connection joints. On the other hand, to fully exert the catenary effect of the steel beam under collapse condition, it is necessary to improve or optimize the existed joints. This has become a hotspot in the anti-collapse research. At present, the earthquake and collapse condition were not synthetically considered for the research of the steel frame joints. Therefore, it is urgent to present a novel steel frame joint with seismic and collapse resistance.

When Fiber Reinforced Polymer/Plastic (FRPs) are used to repair cracked steel plates, the bonding materials will be inevitably trapped into cracks. This phenomenon, however, has not been considered in the current theoretical analyses of most scholars. In this paper, a bridging force model was introduced to simulate the bonding materials between upper and lower surfaces of cracks. A finite element model was established for single-surface repairing of cracked steel plates, and the results were compared with the finite element calculation results without considering the bonding effect of binders inside cracks. Besides, the impact of bonding materials inside cracks upon FRP repairing of cracked steel plates was quantified. Results revealed that, in the case of short cracks, the error between two finite element calculations was slight; when the cracks were longer than 40 mm, the difference between calculated stress intensity factors (SIF), crack expansion displacements, and maximum out-of-plane displacements of steel plates were 20.61%, 77.14%, and 35.73% respectively; and the impact of bonding materials in the crack surfaces upon cracked steel plates cannot be ignored.

To study the influence of the cutting depth of the beam flange on the seismic performance of the spatial joints between the duplex concrete-filled steel tubular column and H-shaped steel beam, six finite element models were established using ABAQUS finite element software, and low-cycle reciprocating loads were applied to the beam ends of the joint model. The results show that the cutting depth of the steel beam flange has a greater impact on the seismic resistance of the spatial joint between the duplex concrete-filled steel tubular column and the steel beam. With the increase of the cutting depth, the energy dissipation capacity of the joint first increases and then decreases, and the ductility of the joint is improved, but the stiffness and limit bearing capacity of the joint has decreased. Especially when the cutting depth is too large, the web in the weakened area of the H-shaped steel beam is prone to buckling instability.