In this paper, we study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing work in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost. (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per incoming rider requests should be incorporated, while it is excluded by most existing solutions. We propose an index structure together with a set of pruning rules and an efficient algorithm to include new riders into drivers' existing trip schedule. To answer new rider requests effectively, we propose two algorithms that match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the proposed methods.

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems.

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. In this paper, we for the rst time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the regret. Specically, two types of regret are dened: (i) the served rate regret, which refers to the rate of unserved requests, catering for the reputation and prot of the platform and workers; (ii) the revenue regret, which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we rst propose a dynamic programming insertion algorithm to improve the efciency of inserting a rider request into a trip schedule of a worker. Furthermore, two heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efciency and scalability of our solutions in dealing with different supply-demand relationships.

Table integration aims to create a comprehensive table by consolidating tuples containing relevant information. In this work, we investigate the challenge of integrating multiple tables from a data lake, focusing on three core tasks: (1) pairwise integrability judgment, which determines whether a tuple pair is integrable, accounting for any occurrences of semantic equivalence or typographical errors; (2) integrable set discovery, which identifies all integrable sets in a table based on pairwise integrability judgments established in the first task; (3) multi-tuple conflict resolution, which resolves conflicts between multiple tuples during integration. To this end, we train a binary classifier to address the task of pairwise integrability judgment. Given the scarcity of labeled data in data lakes, we propose a self-supervised adversarial contrastive learning algorithm to perform classification, which incorporates data augmentation methods and adversarial examples to autonomously generate new training data. Upon the output of pairwise integrability judgment, each integrable set can be considered as a community—a densely connected sub-graph where nodes and edges correspond to tuples in the table and their pairwise integrability, respectively—we proceed to investigate various community detection algorithms to address the integrable set discovery objective. Moving forward to tackle multi-tuple conflict resolution, we introduce an innovative in-context learning methodology. This approach capitalizes on the knowledge embedded within large language models to effectively resolve conflicts that arise when integrating multiple tuples. Notably, our method minimizes the need for annotated data, making it particularly suited for scenarios where labeled datasets are scarce. Since no suitable test collections are available for our tasks, we develop our own benchmarks using two real-world dataset repositories: Real and Join. We conduct extensive experiments on these benchmarks to validate the robustness and applicability of our methodologies in the context of integrating tables within data lakes.