Zobrazeno 1 - 10
of 362
pro vyhledávání: '"Culpepper, J"'
Line charts are a valuable tool for data analysis and exploration, distilling essential insights from a dataset. However, access to the underlying dataset behind a line chart is rarely readily available. In this paper, we explore a novel dataset disc
Externí odkaz:
http://arxiv.org/abs/2408.09506
Autor:
Wang, Tingting, Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane, Dedeoglu, Volkan, Arablouei, Reza
In this paper, given a user's query set and a budget limit, we aim to help the user assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). We prove this probl
Externí odkaz:
http://arxiv.org/abs/2401.00659
While in-memory learned indexes have shown promising performance as compared to B+-tree, most widely used databases in real applications still rely on disk-based operations. Based on our experiments, we observe that directly applying the existing lea
Externí odkaz:
http://arxiv.org/abs/2306.02604
Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit and implement four state-of-the-art updatable learned indexes on disk, a
Externí odkaz:
http://arxiv.org/abs/2305.01237
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 real-time traffic conditions in road networks
Externí odkaz:
http://arxiv.org/abs/2107.12948
Autor:
Luo, Hui, Zhou, Jingbo, Bao, Zhifeng, Li, Shuangli, Culpepper, J. Shane, Ying, Haochao, Liu, Hao, Xiong, Hui
Publikováno v:
SIGIR Conference (2020) 781-790
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 b
Externí odkaz:
http://arxiv.org/abs/2101.02969
Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations have been exploited in past TREC CORE tracks to contribute diverse, high
Externí odkaz:
http://arxiv.org/abs/2011.04830
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
Externí odkaz:
http://arxiv.org/abs/2004.02570
Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node classification, and link prediction. Although significant progress has been
Externí odkaz:
http://arxiv.org/abs/2003.13212
Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this
Externí odkaz:
http://arxiv.org/abs/2003.11547