In the existing traffic prediction scenarios, the lack of accompanying event data, noise interference and insufficient supervised signals seriously restrict the effect of actual traffic prediction. Meanwhile, currently prevalent graph neural networks often struggle to capture effective semantic structures when dealing with learning tasks involving diverse specific events, consequently exhibiting limited generalization and transfer capabilities. This study focuses on crowd gathering events in transportation scenarios and conducts quantitative analysis of their potential risks to traffic network. Relying on the massive online crowd query data produced in Location Based Services (LBS), we propose a generative strategy for node and edge augmentation based on event-traffic interactions, which seeks to generate richer supervised signals. Furthermore, in response to the generative graph structure derived from event chains that fail to match the contextual semantic information, we utilize comparative learning for self-supervised training as the auxiliary proxy task of time series prediction. Experiments on the benchmark datasets of real road networks show that the proposed method is effective in identifying the traffic risk of road segments, especially when the breakdown probability is greater than 50%.

Cross-database knob tuning has long been recognized as a critical but complex task. Modern database systems expose hundreds of configuration knobs that play key roles in memory management, concurrency control, and query optimization. Proper tuning can significantly improve performance, while improper settings can cause severe degradation. Despite progress with black-box methods like reinforcement learning and bayesian optimization, as well as LLM-based tuning guides, challenges remain, such as modeling knob dependencies, high cold-start trial costs, and weak handling of dynamic workloads. We propose KnobTuneX, a structure-aware, LLM-enhanced framework for automatic database tuning that integrates domain knowledge, historical behaviors, and reasoning capabilities to adapt to diverse workloads. The approach features an offline learning stage to capture knob-performance relationships and build a historical RAG store, and an online inference stage that dynamically recommends knobs based on structured reasoning and historical insights. By explicitly modeling dependencies among knobs and leveraging LLMs for informed decision-making, the framework achieves both interpretability and adaptability. Finally, we evaluate KnobTuneX on PostgreSQL and show that the method outperforms mainstream approaches in efficiency, scalability, and overall tuning quality across OLTP and OLAP workloads. The implementation of our work can be found at https://github.com/vjwww/KnobTuneX.

In contemporary urban management, the rapid advancement of technology and widespread use of location-based services provide critical insights, particularly in traffic planning. Analyzing urban spatiotemporal trajectory data reveals diverse perspectives that inform effective city management. This paper focuses on extracting insights from spatiotemporal trajectories and introduces a suite of urban data mining techniques applicable across scenarios, including points of interest diversification. The framework aids in understanding city dynamics, including the distribution and activities of individuals. The method begins by constructing spatiotemporal stay information through two processes: stay point and stay area extraction. Spatiotemporal semantic trajectory data is then integrated with Points of Interest (POI) semantic details. A novel clustering method based on network community detection is proposed. Our experiments show that the method not only achieves comparable or superior results but also ensures time efficiency, aligning with the demands of the big data era.

Differentiable architecture search (DARTS) is widely considered to be easy to overfit the validation set which leads to performance degradation. We first employ a series of exploratory experiments to verify that neither high-strength architecture parameters regularization nor warmup training scheme can effectively solve this problem. Based on the insights from the experiments, we conjecture that the performance of DARTS does not depend on the well-trained supernet weights and argue that the architecture parameters should be trained by the gradients which are obtained in the early stage rather than the final stage of training. This argument is then verified by exchanging the learning rate schemes of weights and parameters. Experimental results show that the simple swap of the learning rates can effectively solve the degradation and achieve competitive performance. Further empirical evidence suggests that the degradation is not a simple problem of the validation set overfitting but exhibit some links between the degradation and the operation selection bias within bilevel optimization dynamics. We demonstrate the generalization of this bias and propose to utilize this bias to achieve an operation-magnitude-based selective stop.

Differentiable architecture search (DARTS) is a promising end to end NAS method which directly optimizes the architecture parameters through general gradient descent. However, DARTS is brittle to the catastrophic failure incurred by the skip connection in the search space. Recent studies also cast doubt on the basic underlying hypotheses of DARTS which are argued to be inherently prone to the performance discrepancy between the continuous-relaxed supernet in the training phase and the discretized finalnet in the evaluation phase. We Figure out that the robustness problem and the skepticism can both be explained by the information bypass leakage during the training of the supernet. This naturally highlights the vital role of the sparsity of architecture parameters in the training phase which has not been well developed in the past. We thus propose a novel sparse-regularized approximation and an efficient mixed-sparsity training scheme to robustify DARTS by eliminating the information bypass leakage. We subsequently conduct extensive experiments on multiple search spaces to demonstrate the effectiveness of our method.

When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple travel costs and personalization-they usually deliver the same routes that minimize a single travel cost (e.g., the shortest routes or the fastest routes) to all drivers. We study the problem of how to recommend personalized routes to individual drivers using big trajectory data. First, we provide techniques capable of modeling and updating different drivers' driving preferences from the drivers' trajectories while considering multiple travel costs. To recommend personalized routes, we provide techniques that enable efficient selection of a subset of trajectories from all trajectories according to a driver's preference and the source, destination, and departure time specified by the driver. Next, we provide techniques that enable the construction of a small graph with appropriate edge weights reflecting how the driver would like to use the edges based on the selected trajectories. Finally, we recommend the shortest route in the small graph as the personalized route to the driver. Empirical studies with a large, real trajectory data set from 52,211 taxis in Beijing offer insight into the design properties of the proposed techniques and suggest that they are efficient and effective.

Map Generalization is one of the most fundamental technologies for modern digital maps. It can effectively reduce the storage space and fit to different applications according to their scale requirement. This paper presents an efficient solution for this problem that won the ACM SIGSPATTAL CUP 2014. Given the original geometries which are represented by sampling points sequence, this method divides the boundaries into many small segments based on their topological characteristics and constriants. It attempts to minimize the number of sampling points by simplifying the given map and constraining points. In addition, the method also employs many optimization techniques to reduce the total latency, like memory pool, parallel computing and string parsing. Experimental results on real datasets demonstrate the effectiveness and efficiency of the proposed method.

Trajectory search plays an important role in various applications such as trip planning and recommendation. However, most existing studies only focus on spatial proximity but ignore individual users' preferences. For example, it is inappropriate to recommend a route containing gravel roads to travelers without off-road vehicles. To accommodate various user preferences, we develop MOIR/UOTS, a trip recommendation system that supports User Oriented Trajectory Search (UOTS) [5], [7] based on our previous MOIR platform [2], [4]. Meanwhile, MOIR/UOTS also functions as a test-bed system for exploring and evaluating novel trajectory searching algorithms. In particular, we demonstrate how ordinary users can interact with MOIR/UOTS to search for trajectories with their preferences, and how MOIR/UOTS helps researchers to evaluate the performance of various algorithms.

Efficient retrieval of dynamically generated, spatial-temporal, and heterogeneous sampling data in the Internet of Things is a key challenge in recent years. However, current search engine techniques are not suitable for such kind of data. To solve this problem, we propose a Hybrid Search Engine Technique for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions ("IoT-SVK Search Engine"), in this paper. The IoT-SVK search engine has satisfactory performances in supporting multi-modal retrieval conditions, and thus provides a good solution for real-time searching of massive sampling data in the Internet of Things.

The effectiveness of map-matching algorithms highly depends on the accuracy and correctness of underlying road networks. In practice, the storage capacity of certain hardware, e.g. mobile devices and embedded systems, is sometimes insufficient to maintain a large digital map for map-matching. Unfortunately, most existing map-matching approaches consider little about this problem. They only apply to environments with information-rich maps, but turn out to be unacceptable for map-matching on simplified road networks. In this paper, we propose a novel map-matching algorithm called Passby to work on most simplified road networks. The storage size of a digital map in disk or memory can be greatly reduced after the simplification. Even under the most simplified situation, i.e., each road segment only consists of a couple of intersection points and omits any other information of it, the experimental results on real dataset show that our Passby algorithm significantly maintains high matching accuracy. Benefiting from the small size of map, simple index structure and heuristic foresight strategy, Passby improves matching accuracy as well as efficiency.

No abstract available.

Location update strategy is one of the most important factors that affect the performance of moving objects databases. However, current motion vector based location tracking methods are designed for regular movements and are thus not suitable for transportation networks with changeful traffic conditions. To solve this problem, we propose a new location update mechanism, Adaptive Network-constrained moving object Location Update Mechanism (ANLUM), in this paper. In ANLUM, the moving object can switch between different location tracking policies according to difference traffic conditions, so that the overall performance can be improved. To evaluate the performance of the proposed method, an experimental system is implemented and the results show that ANLUM can effectively reduce the communication costs with location tracking accuracy guaranteed in traffic jammed transportation networks.

Floating Car Data (FCD) provides an economic complement to infrastructure-based traffic monitoring systems. Based on our previous MOIR platform [5], we use FCD as the data source for large-scale real-time traffic monitoring. This new function brings a challenge of efficiently handling of streaming data from a very large number of moving objects. Server overload problems can occur when a system fails to process data and queries in real-tme, which can lead to critical issues such as unbounded delay accumulation, lost monitoring accuracy or lack of spontaneity. These problems can be addressed by adopting suitable load dropping decisions. In this work, we demonstrate several load shedding techniques, focusing on decision-making based on data attributes. With the end results being quantified and visualized using real data for a large city, this proof-of-concept system provides a convincing way of validating our ideas.

The uncertainty management problem for moving objects databases has been well studied recently, with many models and algorithms proposed. However, very limited work has dealt with the index of uncertain trajectories for a running moving objects database. In this paper, we propose an index framework, the UTR- Tree, for indexing the full uncertain trajectories of network constrained moving objects. Through a dynamic index maintenance technique which is associated with location updates, the UTR-Tree can deal with the full uncertain trajectories, which include not only the historical locations of moving objects, but also their current and near future location information with uncertainty considered, so that the queries on the whole life span of the moving objects can be efficiently supported. The experimental results show that the UTR-Tree outperforms previously proposed network- based moving object index methods in dealing with full uncertain trajectories.

MOIR is a Web-based prototype to support a number of novel applications with network-constrained moving object management. Technical aspects of MOIR range from data acquisition and trajectory smoothing, trajectory data management and query processing, movement predications, and location based Web page recommendation. The demo is in the context of detailed digital road maps with 38,0000 road segments and 55,000 road intersection points, and real spatiotemporal data of over ten thousand taxis in Beijing.

Over the past decade, community structure, a statistical property of networked systems such as social network and World Wide Web, has attracted considerable attention in data mining field because it enables description and prediction of complex networks. Many highly sensitive graph clustering algorithms were developed for identification of communities having dense connections internally and loose connections with others. In this context, Newman and Girvan proposed modularity Q score for quantifying the strength of community structure and measuring the fitness of a division. The Q function has become an important standard recently. In this paper, combining the strengths of the Q score and multilevel paradigm first developed for graph partitioning, we introduced a scalable algorithm MOME (i.e. modularity-based multilevel graph clustering) to efficiently discover communities from a network. The experimental results indicated that MOME ran extremely faster and finally achieved a division with a slightly higher Q score against the latest modularity-based method and its variants, particularly when the network was of a large-scale.

The goal of SECONDO is to provide a "generic" database system frame that can be filled with implementations of various DBMS data models. SECONDO was intended originally as a platform for implementing and experimenting with new kinds of data models, especially to support spatial, spatio-temporal, and graph database models. We now feel, SECONDO has a clean architecture, and it strike a reasonable balance between simplicity and sophistication. Since all the source code is accessible and to a large extent comprehensible for students, we believe it is also an excellent tool for teaching database architecture and implementation concepts. SECONDO runs on Windows, Linux, and Solaris platforms, and consists of three major components SECONDO kernel, optimizer, and graphical user interface.

Replication is one of the key technologies in promoting the performance of mobile database systems. In this paper, a novel mobile database replication scheme, the transaction-level result-set propagation (TLRSP) model, is put forward. A conflict detection and resolution strategy based on TLRSP is discussed in detail and its implementation algorithm is proposed. In the TLRSP model, mobile users are allowed to access local replicas of the database and to submit local transactions when the system is disconnected. The locally committed transactions are sent to a fixed database server for conflict reconciliation and result-set incorporation when the system is reconnected. The TLRSP model uses the incremental refreshing method to synchronize database replicas and to maintain the consistency of the replicated mobile database system.