ORCID Profile
0000-0002-1902-9087
Current Organisation
RMIT University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Information Retrieval and Web Search | Information Systems | Database Management | Data Structures | Library and Information Studies | Analysis of Algorithms and Complexity | Data Format | Coding and Information Theory
Electronic Information Storage and Retrieval Services | Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Expanding Knowledge in the Information and Computing Sciences |
Publisher: ACM
Date: 03-07-2014
Publisher: ACM
Date: 12-09-2016
Publisher: Springer Science and Business Media LLC
Date: 15-07-2021
Publisher: ACM
Date: 19-04-2021
Publisher: ACM
Date: 30-01-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 07-12-2017
Publisher: ACM
Date: 17-10-2015
Publisher: ACM
Date: 12-08-2012
Publisher: ACM
Date: 18-07-2019
Publisher: Association for Computing Machinery (ACM)
Date: 29-11-2022
DOI: 10.1145/3465058
Abstract: 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 s ling-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.
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICDE.2017.15
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 25-07-2020
Publisher: ACM
Date: 27-06-2018
Publisher: Springer Science and Business Media LLC
Date: 07-03-2022
DOI: 10.1007/S10791-022-09407-W
Abstract: Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP performance as a distribution instead of relying on point estimates. To obtain such distribution, we exploit the scaled Absolute Ranking Error (sARE) measure, and its mean the scaled Mean Absolute Ranking Error (sMARE). Our work demonstrates important statistical implications, and overcomes key limitations imposed by the currently used correlation-based point-estimate evaluation approaches. We also explore the potential benefits of using multiple query formulations and ANalysis Of VAriance (ANOVA) modeling in order to measure interactions between multiple factors. The resulting statistical analysis combined with a novel evaluation framework demonstrates the merits of modeling QPP performance as distributions, and enables detailed statistical ANOVA models for comparative analyses to be created.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 25-11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11575832_1
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 14-03-2021
Publisher: IEEE
Date: 04-2018
Publisher: Association for Computing Machinery (ACM)
Date: 05-03-2021
DOI: 10.1145/3440207
Abstract: 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 survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory data management system should possess to maximize flexibility.
Publisher: Springer Science and Business Media LLC
Date: 09-03-2016
Publisher: IEEE
Date: 06-2011
DOI: 10.1109/CCP.2011.18
Publisher: Association for Computing Machinery (ACM)
Date: 04-10-2019
DOI: 10.1145/3345001
Abstract: Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. Query variations covering the same information need represent one way in which different sources of information might arise. However, when implemented in the obvious manner, fusion over query variations is not cost-effective, at odds with the usual web-search requirement for strict per-query efficiency guarantees. In this work, we propose a novel solution to query fusion by splitting the computation into two parts: one phase that is carried out offline, to generate pre-computed centroid answers for queries addressing broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. To achieve this, we make use of score-based fusion algorithms whose costs can be amortized via the pre-processing step and that can then be efficiently combined during subsequent per-query re-ranking operations. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency and within reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.
Publisher: ACM
Date: 19-10-2020
Publisher: IEEE
Date: 04-2019
Publisher: ACM
Date: 06-07-2022
Publisher: Elsevier BV
Date: 02-2014
Publisher: ACM
Date: 08-12-2015
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11880561_28
Publisher: Wiley
Date: 24-08-2011
DOI: 10.1002/SPE.1112
Publisher: ACM
Date: 05-12-2016
Publisher: ACM
Date: 18-07-2019
Publisher: ACM
Date: 05-12-2019
Publisher: ACM
Date: 05-12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: ACM
Date: 07-08-2017
Publisher: ACM
Date: 19-07-2009
Publisher: Association for Computing Machinery (ACM)
Date: 11-2018
Abstract: With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data has become available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of smart transportation services and recent break-throughs in autonomous vehicles increase our reliance on trajectory data in a wide variety of applications. Supporting these services in emerging platforms requires more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Best Facility Trajectory Search ( k BFT) and (ii) k Best Coverage Facility Trajectory Search ( k BCovFT). We propose a novel index structure, the Trajectory Quadtree (TQ-tree) that utilizes a quadtree to hierarchically organize trajectories into different nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree, we develop a ide-and-conquer approach to efficiently process a k BFT query. To solve the k BCovFT, which is a non-submodular NP-hard problem, we propose a greedy approximation. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our algorithms outperform baselines by two to three orders of magnitude.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 08-12-2015
Publisher: ACM
Date: 26-09-2019
Publisher: ACM
Date: 26-09-2019
Publisher: Springer Science and Business Media LLC
Date: 21-06-2016
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2023
DOI: 10.1145/3589284
Abstract: 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, and compare them against the B+-tree under a wide range of settings. Through our evaluation, we make some key observations: 1) Overall, the B+-tree performs well across a range of workload types and datasets. 2) A learned index could outperform B+-tree or other learned indexes on disk for a specific workload. For ex le, PGM achieves the best performance in write-only workloads while LIPP significantly outperforms others in lookup-only workloads. We further conduct a detailed performance analysis to reveal the strengths and weaknesses of these learned indexes on disk. Moreover, we summarize the observed common shortcomings in five categories and propose four design principles to guide future design of on-disk, updatable learned indexes: (1) reducing the index's tree height, (2) better data structures to lower operation overheads, (3) improving the efficiency of scan operations, and (4) more efficient storage layout.
Publisher: ACM
Date: 03-07-2014
Publisher: ACM
Date: 19-10-2020
Publisher: ACM
Date: 25-07-2020
Publisher: ACM
Date: 07-08-2017
Publisher: IEEE
Date: 04-2020
Publisher: Association for Computing Machinery (ACM)
Date: 09-2019
Abstract: In this paper, we study the problem of large-scale trajectory data clustering, k -paths, which aims to efficiently identify k "representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters, k -paths can be used for visual exploration in applications such as traffic monitoring, public transit planning, and site selection. By combining map matching with an efficient intermediate representation of trajectories and a novel edge-based distance (EBD) measure, we present a scalable clustering method to solve k -paths. Experiments verify that we can cluster millions of taxi trajectories in less than one minute, achieving improvements of up to two orders of magnitude over state-of-the-art solutions that solve similar trajectory clustering problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: ACM
Date: 05-12-2016
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 27-06-2018
Publisher: ACM
Date: 29-10-2012
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 31-05-2015
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 08-05-2019
Publisher: ACM
Date: 26-11-2014
Publisher: Springer Science and Business Media LLC
Date: 25-04-2018
Publisher: ACM
Date: 07-08-2017
Publisher: ACM
Date: 05-12-2013
Publisher: ACM
Date: 07-07-2016
Publisher: Association for Computing Machinery (ACM)
Date: 12-2010
Abstract: Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a | q |-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.
Publisher: ACM
Date: 11-12-2018
Publisher: ACM
Date: 18-07-2019
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 07-12-2017
Publisher: Springer Science and Business Media LLC
Date: 26-02-2019
Publisher: Association for Computing Machinery (ACM)
Date: 2016
Abstract: The problem of maximizing bichromatic reverse k nearest neighbor queries (BR k NN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRST k NN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRST k NN query. We show that the problem is NP-hard and present both approximate and exact solutions.
Publisher: IEEE
Date: 04-2017
Publisher: Association for Computing Machinery (ACM)
Date: 21-12-2012
Abstract: On August 16, 2012 the SIGIR 2012 Workshop on Open Source Information Retrieval was held as part of the SIGIR 2012 conference in Portland, Oregon, USA. There were 2 invited talks, one from industry and one from academia. There were 6 full papers and 6 short papers presented as well as demonstrations of 4 open source tools. Finally there was a lively discussion on future directions for the open source Information Retrieval community. This contribution discusses the events of the workshop and outlines future directions for the community.
Publisher: Springer Berlin Heidelberg
Publisher: Association for Computing Machinery (ACM)
Date: 2014
DOI: 10.1145/2559168
Abstract: Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing word-sequence statistics using inverted indexes requires unreasonable processing time or substantial space overhead. Establishing a balance between these competing space and time trade-offs can dramatically improve system performance. In this article, we present and analyze a new index structure designed to improve query efficiency in dependency retrieval models. By adapting a class of ( ε, δ )-approximation algorithms originally proposed for sketch summarization in networking applications, we show how to accurately estimate statistics important in term-dependency models with low, probabilistically bounded error rates. The space requirements for the vocabulary of the index is only logarithmically linked to the size of the vocabulary. Empirically, we show that the sketch index can reduce the space requirements of the vocabulary component of an index of n -grams consisting of between 1 and 4 words extracted from the GOV2 collection to less than 0.01% of the space requirements of the vocabulary of a full index. We also show that larger n -gram queries can be processed considerably more efficiently than in current alternatives, such as positional and next-word indexes.
Publisher: IEEE
Date: 04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Association for Computing Machinery (ACM)
Date: 08-09-2021
DOI: 10.1145/3470563
Abstract: Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 30-04-2023
Publisher: ACM
Date: 11-07-2021
Publisher: ACM
Date: 26-11-2014
Publisher: ACM
Date: 11-12-2018
Publisher: ACM
Date: 11-12-2018
Publisher: ACM
Date: 05-12-2012
Publisher: ACM
Date: 03-07-2014
Publisher: IEEE
Date: 04-2018
Start Date: 2017
End Date: 2019
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 12-2018
Amount: $422,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 12-2016
Amount: $392,979.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 12-2019
Amount: $416,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2011
End Date: 12-2014
Amount: $360,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2019
End Date: 12-2024
Amount: $380,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2022
End Date: 12-2024
Amount: $364,295.00
Funder: Australian Research Council
View Funded Activity