Jixue Liu

Research Interests

Data management and data science Data mining and knowledge discovery Machine learning

APrf Jixue Liu

Associate Professor

School of Computer Science and Information Technology

College of Engineering and Information Technology

Dr Jixue Liu's research and teaching areas are mainly in database and data mining areas. His research topics are in text and sequence data analytics, data integration and entity resolution, dependency theory and discovery, privacy, XML, etc. He teaches database and data mining courses. He taught programming, Web development, and XML courses.   

Publications http://sites.google.com/site/jixueliu/publications 

Major Projects

* 2020-2022 ARC DP200101210 Fairness aware data mining for discrimination-free decision-making. Chief investigator.

* 2020-2023 ARC IC190100017 ARC Training Centre for Integrated Operations for Complex Resources. Chief investigator.

* 2016-2018 D2D CRC Project Lead: Entity Linking and Resolution.

* 2016-2018 D2D CRC Project Investigator: Beating the News.

* ARC Discovery Grant 2008-2010: XML views of relational databases: semantics and update problems

* 2005-2007 Australia Research Council (ARC) Discovery Project: Constraint in XML data Integration

* 2006 Australia Research Council (ARC) Discovery Project: Normalizing XML documents.

Areas and topics

* Fairness/discrimination detection in algorithms and fair algorithms

* Privacy preserving data publication in relational databases and text data.

* Analytics in time series and text data.

* dependency discovery from relational, XML and web data

* XML based research: XML functional dependencies,mapping between XML and relational databases with regard to schemas and constraints; XML design theory in terms of normal forms; translation between XSLT and SQL; XML view update

* XML data transformation and integration. It studies how XML data can be transformed to a required format without information loss and how multiple XML documents can be integrated while keys are preserved and satisfied.

Date Position Institution name
2021 - ongoing Associate professor University of South Australia

Date Type Title Institution Name Country Amount
2014 Award Best paper awards Conference Board - -

Language Competency
Achinese Can read, write, speak, understand spoken and peer review
English Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
1997 - 2021 University of South Australia Australia PhD

Year Citation
2026 Lu, S., Liu, L., Yu, K., Le, T. D., Liu, J., & Li, J. (2026). Dependency-based anomaly detection: A general framework and comprehensive evaluation. Expert Systems with Applications, 297(129249), 129249.
DOI
2026 Cheng, D., Xu, Z., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2026). Linking model intervention to causal interpretation in model explanation. Pattern Recognition, 173, 112814.
DOI
2025 Asamoah, R., Tran, V., & Liu, J. (2025). Novel dynamic history-based algorithm for flotation process optimisation. Minerals Engineering, 232(109502), 1-11.
DOI
2025 Deho, O. B., Bewong, M., Kwashie, S., Li, J., Liu, J., Liu, L., & Joksimovic, S. (2025). Is it still fair? A comparative evaluation of fairness algorithms through the lens of covariate drift. Machine Learning, 114(1), 1-19.
DOI Scopus1
2025 Yang, F., Liu, J., Li, J., Liu, L., Wang, S., Li, W., & Ni, S. (2025). Causal reinforcement learning for train scheduling on single-track railway networks. Transportation Research Part C Emerging Technologies, 178(105215), 105215.
DOI
2025 Lartey, C., Asamoah, R. K., Greet, C., Zanin, M., & Liu, J. (2025). An interpretable and generalised machine learning model for predicting flotation performance. Minerals Engineering, 232(109492), 1-11.
DOI
2025 Cheng, D., Li, J., Liu, L., Xu, Z., Zhang, W., Liu, J., & Le, T. D. (2025). Disentangled Representation Learning for Causal Inference With Instruments. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14078-14091.
DOI Scopus4 WoS2
2024 Cheng, D., Li, J., Liu, L., Yu, K., Duy Le, T., & Liu, J. (2024). Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data. IEEE Transactions on Neural Networks and Learning Systems, 35(8), 11542-11552.
DOI Scopus9 WoS8 Europe PMC2
2024 Cheng, D., Jiuyong, L. I., Liu, L., Liu, J., & Thuc Duy, L. E. (2024). Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey. ACM Computing Surveys, 56(5), 37 pages.
DOI Scopus32 WoS22
2024 Lartey, C., Liu, J., Asamoah, R. K., Greet, C., Zanin, M., & Skinner, W. (2024). Effective Outlier Detection for Ensuring Data Quality in Flotation Data Modelling Using Machine Learning (ML) Algorithms. Minerals, 14(9), 1-28.
DOI Scopus10 WoS11
2024 Deho, O. B., Liu, L., Li, J., Liu, J., Zhan, C., & Joksimovic, S. (2024). When the Past != The Future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models. IEEE Transactions on Learning Technologies, 17, 1007-1020.
DOI Scopus5 WoS2
2024 Liu, J., Li, J., Liu, L., Le, T., Ye, F., & Li, G. (2024). Fairmod: making predictions fair in multiple protected attributes. Knowledge and Information Systems, 66(3), 1861-1884.
DOI Scopus3 WoS2
2024 Peters, S., Liu, J., Keppel, G., Wendleder, A., & Xu, P. (2024). Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery. Remote Sensing, 16(10), 1722.
DOI Scopus12
2024 Lu, S., Jones, E., Zhao, L., Sun, Y., Qin, K., Liu, J., . . . Peters, S. (2024). Onboard AI for Fire Smoke Detection Using Hyperspectral Imagery: An Emulation for the Upcoming Kanyini Hyperscout-2 Mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 9629-9640.
DOI Scopus8 WoS7
2024 Liu, J., Li, J., Peters, S., & Zhao, L. (2024). A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery. Remote Sensing Applications Society and Environment, 36(101283), 101283.
DOI Scopus3
2024 Zhao, L., Liu, J., Peters, S., Li, J., Mueller, N., & Oliver, S. (2024). Learning class-specific spectral patterns to improve deep learning-based scene-level fire smoke detection from multi-spectral satellite imagery. Remote Sensing Applications Society and Environment, 34(101152), 101152.
DOI Scopus11
2024 Zhao, L., Liu, J., Peters, S., Li, J., Mueller, N., & Oliver, S. (2024). Cross-sensor transfer learning for fire smoke scene detection using variable-bands multi-spectral satellite imagery aided by spectral patterns. International Journal of Remote Sensing, 45(10), 3332-3348.
DOI
2024 Shi, Z., Chow, C. W. K., Gao, J., Xing, K., Liu, J., & Li, J. (2024). Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations. Sensors, 24(6), 1857.
DOI Scopus4 WoS3 Europe PMC1
2023 Bewong, M., Wondoh, J., Kwashie, S., Liu, J., Liu, L., Li, J., . . . Kernot, D. (2023). DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text. IEEE Access, 11, 32826-32841.
DOI Scopus4 WoS3
2023 Cheng, D., Li, J., Liu, L., Zhang, J., Liu, J., & Le, T. D. (2023). Local Search for Efficient Causal Effect Estimation. IEEE Transactions on Knowledge and Data Engineering, 35(9), 8823-8837.
DOI Scopus16 WoS13
2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2023). Personalized Interventions to Increase the Employment Success of People With Disability. IEEE Transactions on Big Data, 9(6), 1561-1574.
DOI Scopus1 WoS1
2023 Li, J., Liu, L., Zhang, S., Ma, S., Le, T. D., & Liu, J. (2023). Causal heterogeneity discovery by bottom-up pattern search for personalised decision making. Applied Intelligence, 53(7), 8180-8194.
DOI
2023 Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023). Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why. IEEE Transactions on Learning Technologies, 16(4), 560-572.
DOI Scopus16 WoS11
2023 Cheng, D., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2023). Toward unique and unbiased causal effect estimation from data with hidden variables. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 1-13.
DOI Scopus23 WoS25 Europe PMC3
2022 Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Le, T. D. (2022). How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?. British Journal of Educational Technology, 53(4), 822-843.
DOI Scopus58 WoS40
2022 Shi, Z., Chow, C. W. K., Fabris, R., Liu, J., Sawade, E., & Jin, B. (2022). Determination of coagulant dosages for process control using online UV-vis spectra of raw water. Journal of Water Process Engineering, 45(102526), 1-8.
DOI Scopus34 WoS30
2022 Shi, Z., Chow, C. W. K., Fabris, R., Liu, J., & Jin, B. (2022). Applications of online UV‐Vis spectrophotometer for drinking water quality monitoring and process control: a review. Sensors, 22(8, article no. 2987), 1-21.
DOI Scopus86 WoS71 Europe PMC20
2022 Cheng, D., Li, J., Liu, L., Le, T. D., Liu, J., & Yu, K. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery, 36(3), 1174-1196.
DOI Scopus11 WoS10
2022 Zhao, L., Liu, J., Peters, S., Li, J., Oliver, S., & Mueller, N. (2022). Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model. Remote Sensing, 14(13), 24 pages.
DOI Scopus38 WoS28
2022 Liu, J., Li, J., & Liu, L. (2022). FastOPM—A practical method for partial match of time series. Pattern Recognition, 130(article no. 108808), 12 pages.
DOI
2022 Chow, C. W. K., Liu, J., Li, J., Swain, N., & Saint, C. P. (2022). A Data Visualisation Tool for Treatment Process Monitoring in Web Browsers. Water Conservation Science and Engineering, 7(4), 363-373.
DOI Scopus2
2021 Peters, S., Liu, J., Bruce, D., Li, J., Finn, A., & O’Hehir, J. (2021). Research note: cost-efficient estimates of Pinus radiata wood volumes using multitemporal LiDAR data. Australian Forestry, 84(4), 206-214.
DOI Scopus4 WoS4
2021 Li, J., Zhang, W., Liu, L., Yu, K., Le, T. D., & Liu, J. (2021). A general framework for causal classification. International Journal of Data Science and Analytics, 11(2), 127-139.
DOI Scopus6 WoS6
2021 Shi, Z., Chow, C. W. K., Fabris, R., Zheng, T., Liu, J., & Jin, B. (2021). Evaluation of the impact of suspended particles on the UV absorbance at 254 nm (UV₂₅₄) measurements using a submersible UV-Vis spectrophotometer. Environmental Science and Pollution Research, 28(10), 12576-12586.
DOI Scopus12 WoS5 Europe PMC3
2020 Shi, Z., Chow, C. W. K., Fabris, R., Liu, J., & Jin, B. (2020). Alternative particle compensation techniques for online water quality monitoring using UV–Vis spectrophotometer. Chemometrics and Intelligent Laboratory Systems, 204(104074), 9 pages.
DOI Scopus36 WoS31
2020 Li, J., Liu, L., Le, T. D., & Liu, J. (2020). Accurate data-driven prediction does not mean high reproducibility. Nature Machine Intelligence, 2(1), 13-15.
DOI Scopus38 WoS38
2020 Ansah, J., Liu, L., Kang, W., Liu, J., & Li, J. (2020). Leveraging burst in twitter network communities for event detection. World Wide Web, 23(5), 2851-2876.
DOI Scopus20 WoS15
2020 Nirola, R., Saint, C., Hehir, J. O., & Liu, J. (2020). Merchantable wood volume response of P. radiata D. Don post thinned plots on coated and uncoated urea fertilizers. Banko Janakari, 30(2), 36-47.
DOI
2019 Bewong, M., Liu, J., Liu, L., & Li, J. (2019). Privacy preserving serial publication of transactional data. Information Systems, 82, 53-70.
DOI Scopus12
2019 Ding, X., Ou, Y., Jia, J., Jin, H., & Liu, J. (2019). Efficient subgraph search on large anonymized graphs. Concurrency and Computation Practice and Experience, 31(23), 1-14.
DOI Scopus1
2019 Yang, Q., Beecham, S., Liu, J., & Pezzaniti, D. (2019). The influence of rainfall intensity and duration on sediment pathways and subsequent clogging in permeable pavements. Journal of Environmental Management, 246, 730-736.
DOI Scopus35 WoS31 Europe PMC4
2019 Bewong, M., Liu, J., Liu, L., Li, J., & Choo, K. K. R. (2019). A relative privacy model for effective privacy preservation in transactional data. Concurrency and computation: practice and experience, 31(23, article no. e4923), 1-13.
DOI Scopus8
2019 Liu, H. C., & Liu, J. (2019). On the expressive power of logics on constraint databases with complex objects. Journal of computer science and technology, 34(4), 795-817.
DOI
2018 Hoang, T., Liu, J., Roughead, E., Pratt, N., & Li, J. (2018). Supervised signal detection for adverse drug reactions in medication dispensing data. Computer Methods and Programs in Biomedicine, 161, 25-38.
DOI Scopus29 WoS23 Europe PMC18
2018 Chow, C. W. K., Liu, J., Li, J., Swlkain, N., Reid, K., & Saint, C. P. (2018). Development of smart data analytics tools to support wastewater treatment plant operation. Chemometrics and intelligent laboratory systems, 177, 140-150.
DOI Scopus31
2018 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2018). Authenticity and credibility aware detection of adverse drug events from social media. International journal of medical informatics, 120, 157-171.
DOI Scopus5 WoS4 Europe PMC6
2018 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2018). Authenticity and credibility aware detection of adverse drug events from social media. International Journal of Medical Informatics, 120, 157-171.
DOI Scopus10 WoS5 Europe PMC2
2018 Kwashie, S., Liu, J., Li, J., Liu, L., Stumptner, M., & Yang, L. (2018). Certus: An effective entity resolution approach with graph differential dependencies (GDDs). Proceedings of the VLDB Endowment, 12(6), 653-666.
DOI Scopus36 WoS29
2017 Pokharel, S., Choo, K. K. R., & Liu, J. (2017). Mobile cloud security: an adversary model for lightweight browser security. Computer standards and interfaces, 49, 71-78.
DOI Scopus14 WoS11
2017 Pokharel, S., Choo, K. K. R., & Liu, J. (2017). Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication. Concurrency and computation, 29(7), 1-13.
DOI Scopus4 WoS3
2017 Li, J., Liu, L., Liu, J., & Green, R. (2017). Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data. Health information science and systems, 5(5), 1-10.
DOI Scopus5 WoS4 Europe PMC2
2017 Wang, J., Yu, J. X., Liu, J., & Pang, C. (2017). A revised result on chasing tree patterns under schema graphs. Information Processing Letters, 119, 19-24.
DOI
2017 Li, J., Ma, S., Le, T., Liu, L., & Liu, J. (2017). Causal Decision Trees. IEEE transactions on knowledge and data engineering, 29(2), 257-271.
DOI Scopus57 WoS41
2016 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., Sun, B., & Ma, S. (2016). From observational studies to causal rule mining. ACM transactions on intelligent systems and technology, 7(2, article no. 14), 1-27.
DOI Scopus39 WoS16
2016 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2016). Detecting signals of detrimental prescribing cascades from social media. Artificial Intelligence in Medicine, 71, 43-56.
DOI Scopus15 WoS13 Europe PMC7
2016 Li, J., Baig, M. M., Sarowar Sattar, A. H. M., Ding, X., Liu, J., & Vincent, M. W. (2016). A hybrid approach to prevent composition attacks for independent data releases. Information Sciences, 367-368, 324-336.
DOI Scopus29 WoS19
2015 Sun, Y., Li, J., Liu, J., Chow, C., Sun, B., & Wang, R. (2015). Using causal discovery for feature selection in multivariate numerical time series. Machine Learning, 101(1-3), 377-395.
DOI Scopus85 WoS65
2015 Vincent, M., Liu, J., Liu, H. C., & Link, S. (2015). 'Differential dependencies: reasoning and discovery' revisited. ACM transactions on database systems, 40(2article no. 14), 1-14.
DOI Scopus3 WoS3
2014 Liu, Y., Ye, F., Liu, J., & He, S. (2014). Mining approximate keys based on reasoning from XML data. Applied Mathematics and Information Sciences, 8(4), 20095-22016.
DOI
2014 Sun, Y., Li, J., Liu, J., Sun, B., & Chow, C. (2014). An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing, 138, 189-198.
DOI Scopus138 WoS105
2014 Li, J., Liu, J., Toivonen, H., Satou, K., Sun, Y., & Sun, B. (2014). Discovering statistically non-redundant subgroups. Knowledge Based Systems, 67, 315-327.
DOI Scopus18 WoS17
2014 Sarowar Sattar, A. H. M., Li, J., Liu, J., Heatherly, R., & Malin, B. (2014). A probabilistic approach to mitigate composition attacks on privacy in non-coordinated environments. Knowledge Based Systems, 67, 361-372.
DOI Scopus26 WoS18 Europe PMC1
2013 Sarowar Sattar, A. H. M., Li, J., Ding, X., Liu, J., & Vincent, M. (2013). A general framework for privacy preserving data publishing. Knowledge Based Systems, 54, 276-287.
DOI Scopus32 WoS24
2013 Jiao, H., Liu, J., Li, J., & Liu, C. (2013). A two-layer multi-dimensional trustworthiness metric for web service composition. Web Technologies and Applications: 15th Asia-Pacific Web Conference, APWeb 2013, Sydney, Australia, April 2013. Proceedings, 7808 LNCS, 151-162.
DOI
2013 Liu, J., Ye, F., Li, J., & Wang, J. (2013). On discovery of functional dependencies from data. Data and Knowledge Engineering, 86, 146-159.
DOI Scopus6 WoS6
2013 Liu, H., Li, J., Liu, L., Liu, J., Lee, I., & Zhao, J. (2013). Exploring groups from heterogeneous data via sparse learning. Lecture notes in computer science, 7818 LNAI(PART 1), 556-567.
DOI Scopus1
2013 Li, J., Liu, J., Toivonen, H., & Yong, J. (2013). Effective pruning for the discovery of conditional functional dependencies. Computer Journal, 56(3), 378-392.
DOI Scopus21 WoS10
2012 Liu, J., Li, J., Liu, C., & Chen, Y. (2012). Discover dependencies from data - A review. IEEE Transactions on Knowledge and Data Engineering, 24(2), 251-264.
DOI Scopus130 WoS82
2012 Baig, M. M., Li, J., Liu, J., Ding, X., & Wang, H. (2012). Data privacy against composition attack. Lecture notes in computer science, 7238 LNCS(PART 1), 320-334.
DOI Scopus11
2012 Vincent, M. W., Liu, J., & Mohania, M. (2012). The implication problem for 'closest node' functional dependencies in complete XML documents. Journal of computer and system sciences, 78(4), 1045-1098.
DOI Scopus7 WoS5
2012 Liu, J., Liu, C., Haerder, T., & Yu, J. (2012). Updating typical XML views. Lecture notes in computer science, 7238(PART 1), 126-140.
DOI Scopus2
2011 Li, J., Liu, J., Baig, M., & Wong, R. C. W. (2011). Information based data anonymization for classification utility. Data and Knowledge Engineering, 70(12), 1030-1045.
DOI Scopus38 WoS28
2011 Liu, J., Kang, Z. B., & Wang, E. K. (2011). Recursive method for opacity expansion at finite temperature. Chinese Physics C, 35(1), 44-49.
DOI
2010 Shahriar, M. S., & Liu, J. (2010). Towards the preservation of functional dependency in XML data transformation. International Journal of Intelligent Information and Database Systems, 4(5), 431-461.
DOI Scopus1
2009 Shahriar, M. D. S., & Liu, J. (2009). Preserving key in XML data transformation. Acta informatica, 46(7), 475-507.
DOI
2009 Shahriar, M. D. S., & Liu, J. (2009). On transiting key in XML data transformation for integration. International Journal of Security and its Applications, 3(1), 101-116.
2009 Shahriar, M. D. S., & Liu, J. (2009). Towards a definition of referential integrity constraints for XML. International Journal of Software Engineering and Its Applications, 3(1), 69-82.
2007 Vincent, M., Liu, J., & Mohania, M. (2007). On the equivalence between FDs in XML and FDs in relations. Acta Informatica, 44(3-4), 207-247.
DOI Scopus21 WoS12
2006 Liu, C., Vincent, M., & Liu, J. (2006). Constraint preserving transformation from relational schema to XML schema. World wide web, 9(1), 93-110.
DOI Scopus31 WoS20
2004 Vincent, M. W., Liu, J., & Liu, C. (2004). Strong functional dependencies and their application to normal forms in XML. ACM Transactions on Database Systems, 29(3), 445-462.
DOI Scopus158 WoS111
2004 Liu, J., & Vincent, M. (2004). Querying relational databases through XSLT. Data and knowledge engineering, 48(1), 103-128.
DOI Scopus4 WoS2
2004 Vincent, M. W., Liu, J., & Liu, C. (2004). Redundancy free mappings from relations to XML. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 3129, 346-356.
DOI Scopus2
2004 Vincent, M., & Liu, J. (2004). Irrelevant updates and self-maintainability in transitive closure database views. Information Processing Letters, 89(1), 25-29.
DOI
2004 Vincent, M., Liu, J., & Liu, C. (2004). Strong functional dependencies and their application to normal forms in XML. ACM transactions on database systems.
DOI
2003 Vincent, M. W., Liu, J., & Liu, C. (2003). Redundancy free mappings from relations to XML. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2762, 55-67.
DOI Scopus3
2003 Liu, J., & Vincent, M. (2003). Derivation of incremental equations for PNF nested relations. Acta cybernetica, 23(2), 76-82.
DOI Scopus1
2003 Liu, J., Vincent, M., & Mohania, M. (2003). Maintaining views in object-relational databases. Knowledge and Information Systems, 5(1), 50-82.
DOI
2002 Liu, J., & Vincent, M. (2002). Containment and disjointedness in partitioned normal form relations. Acta informatica, 38(5), 325-342.
DOI Scopus2
2002 Liu, J., & Liu, C. (2002). A declarative way of extracting XML data in XSL. Lecture notes in computer science.
DOI

Year Citation
2025 Gao, W., Xu, Z., Li, J., Liu, L., Liu, J., Le, T. D., . . . Chen, Y. (2025). TSI: A Multi-view Representation Learning Approach for Time Series Forecasting. In M. Gong, Y. Song, Y. S. Koh, W. Xiang, & D. Wang (Eds.), Event/exhibition information: 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, Australia, 25/11/2024-29/11/2024
Source details - Title: AI 2024: Advances in Artificial Intelligence (Vol. 15442 LNAI, pp. 291-302). Singapore: SPRINGER-VERLAG SINGAPORE PTE LTD.
DOI
2025 Dzakpasu, D. Q., Liu, J., Li, J., & Liu, L. (2025). Improving Intersectional Group Fairness Using Conditional Generative Adversarial Network and Transfer Learning. In Event/exhibition information: 37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024, Melbourne, Australia, 25/11/2024-29/11/2024
Source details - Title: AI 2024: Advances in Artificial Intelligence (Vol. 15442 LNAI, pp. 139-153). Singapore: Springer Nature Singapore.
DOI
2024 Park, J. Y., Liu, L., Liu, J., & Li, J. (2024). Causal Disentanglement for Adversarial Defense. In Event/exhibition information: 36th Australasian Joint Conference on Artificial Intelligence, AJCAI 2023, Brisbane, Australia, 28/11/2023 - 01/12/2023
Source details - Title: Australasian Joint Conference on Artificial Intelligence AI 2023: AI 2023: Advances in Artificial Intelligence (Vol. 14471 LNAI, pp. 315-327). Singapore: Springer Nature Singapore.
DOI
2023 Xu, Z., Liu, J., Cheng, D., Li, J., Liu, L., & Wang, K. (2023). Disentangled Representation with Causal Constraints for Counterfactual Fairness. In Event/exhibition information: 27th Pacific-Asia Conferenceon Knowledge Discovery and Data Mining (PAKDD 2023), Osaka, Japan, 25/05/2023-28/05/2023
Source details - Title: Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2023: Advances in Knowledge Discovery and Data Mining (Vol. 13935 LNCS, pp. 471-482). Switzerland: Springer.
DOI Scopus8
2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Recommending personalized interventions to increase employability of disabled jobseekers. In J. Gama (Ed.), Event/exhibition information: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, 16/05/2022-19/05/2022
Source details - Title: Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part III (Vol. 13282 LNAI, pp. 92-104). Switzerland: Springer.
DOI Scopus3 WoS2
2019 Liu, J., & Bailey, J. (2019). Preface - AI 2019: Advances in Artificial Intelligence. In J. Liu, & J. Bailey (Eds.), Event/exhibition information: 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019, Adelaide, Australia, 02/12/2019-05/12/2019
Source details - Title: AI 2019: Advances in Artificial Intelligence (pp. v-vi). Switzerland: Springer.
DOI
2015 Li, J., Sattar, S. A., Baig, M. M., Liu, J., Heatherly, R., Tang, Q., & Malin, B. (2015). Methods to mitigate risk of composition attack in independent data publications. In Medical Data Privacy Handbook (pp. 179-200). Switzerland: Springer International Publishing.
DOI Scopus6
2015 Kwashie, S., Liu, J., Li, J., & Ye, F. (2015). Conditional differential dependencies (CDDs). In T. Morzy, P. Valduriez, & L. Bellatreche (Eds.), Lecture Notes in Computer Science (Vol. 9282, pp. 3-17). Switzerland: SPRINGER-VERLAG BERLIN.
DOI Scopus6 WoS10
2010 Baig, M. M., Li, J., Liu, J., Wang, H., & Wang, J. (2010). Privacy protection for genomic data: Current techniques and challenges. In Studies in Computational Intelligence (Vol. 265, pp. 175-193). Berlin: Springer Berlin Heidelberg.
DOI
2010 Shahriar, M. D. S., & Liu, J. (2010). Transformation of data with constraints for integration: an information system approach. In S. Shahriar (Ed.), Event/exhibition information: International Conference, DTA 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, 10/12/2009-12/12/2009
Source details - Title: Database Theory and Application (Vol. 64, pp. 74-81). Germany: Springer.
DOI
2009 Shahriar, M. D. S., & Liu, J. (2009). Preserving referential integrity constraints in XML data transformation. In D. Slezak (Ed.), Event/exhibition information: International Conference, DTA 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, 10/12/2009-12/12/2009
Source details - Title: Database Theory and Application (Vol. 64, pp. 57-65). Germany: Springer Nature.
DOI
2009 Shahriar, M. D. S., & Liu, J. (2009). Checking satisfactions of XML referential integrity constraints. In J. Liu (Ed.), Event/exhibition information: 5th International Conference on Active Media Technology (AMT 2009), Beijing, China, 22/10/2009-24/10/2009
Source details - Title: Active Media Technology : 5th International Conference, AMT 2009, proceedings (Vol. 5820 LNCS, pp. 148-159). Germany: Springer.
DOI Scopus2 WoS1
2009 Shahriar, M. S., & Liu, J. (2009). On the performances of checking XML key and functional dependency satisfactions. In R. Meersman, T. Dillon, & P. Herrero (Eds.), Source details - Title: Lecture notes in computer science (Vol. 5871, pp. 1254-1271). Berlin: Springer.
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2005 Kwon, Y. I., Park, H. H., Liu, J., & Nacimento, M. (2005). Radial projection: a feature extraction method for topographical shapes. In Source details - Title: Advances in Mulitmedia Information Processing - PCM 2005 - Lecture Notes in Computer Science Series (LNCS 3767) (pp. 582-593). Germany: Springer.
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2005 Vincent, M. W., & Liu, J. (2005). Checking functional dependency satisfaction in XML. In S. Bressan, S. Ceri, E. Hunt, Z. G. Ives, Z. Bellahsene, M. Rys, & R. Unland (Eds.), Event/exhibition information: Third International XML Database Symposium, XSym 2005, Trondheim, Norway, 28/09/2005 - 29/08/2005
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2004 Vincent, M. W., Schrefl, M., Liu, J., Liu, C., & Dogen, S. (2004). Generalized inclusion dependencies in XML. In J. X. Yu, X. M. Lin, H. J. Lu, & Y. C. Zhang (Eds.), Source details - Title: Advanced web technologies and applications : 6th Asia-Pacific Web Conference, APWeb 2004 Proceedings (Vol. 3007, pp. 224-233). Germany: SPRINGER-VERLAG BERLIN.
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2004 Liu, J., Vincent, M., & Liu, C. (2004). Functional dependencies, from relational to XML. In M. Broy, & A. Zamulin (Eds.), Source details - Title: Perspectives of System Informatics: 5th International Andrei Ershov Memorial Conference, PSI 2003, Akademgorodok, Novosibirsk, Russia, July 9-12, 2003 : revised papers (Vol. 2890, pp. 531-538). Germany: Springer.
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2003 Vincent, M. W., & Liu, J. (2003). Functional dependencies for XML. In X. Zhou, Y. C. Zhang, & M. E. Orlowska (Eds.), Event/exhibition information: 5th Asia-Pacific Web Conference, APWeb 2003, Xian, China, 23/04/2003 - 25/04/2003
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Year Citation
2025 Gao, W., Li, J., Liu, L., Le, T. D., Chen, X., Du, X., . . . Chen, Y. (2025). From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction. In Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). IOS Press.
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2025 Wang, H., Liu, L., Li, J., Xu, Z., Liu, J., Cao, Z., & Cheng, D. (2025). Off-policy Evaluation for Multiple Actions in the Presence of Unobserved Confounders. In Www 2025 Proceedings of the ACM Web Conference (pp. 413-424). US: ACM.
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2025 Chen, X., Li, J., Liu, J., Liu, L., Peters, S., Le, T. D., . . . Walsh, A. (2025). Diffusion Models for Attribution. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 39 (pp. 2266-2274). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
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2025 Gao, W., Li, J., Cheng, D., Liu, L., Liu, J., Le, T., . . . Zhao, Y. (2025). Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction. In Ijcai International Joint Conference on Artificial Intelligence (pp. 9638-9646). Canada: International Joint Conferences on Artificial Intelligence Organization.
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2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2024). Conditional instrumental variable regression with representation learning for causal inference. In 12th International Conference on Learning Representations, ICLR 2024 (pp. 1-17). US: International Conference on Learning Representations (ICLR).
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2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., Gao, W., & Le, T. D. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 38 (pp. 11480-11488). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
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2024 Dzakpasu, D. Q., Liu, J., Li, J., & Liu, L. (2024). Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness. In International Conference on Information and Knowledge Management Proceedings (pp. 560-569). US: ACM.
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2024 Xu, Z., Cheng, D., Li, J., Liu, J., Liu, L., & Yu, K. (2024). CAUSAL INFERENCE WITH CONDITIONAL FRONT-DOOR ADJUSTMENT AND IDENTIFIABLE VARIATIONAL AUTOENCODER. In 12th International Conference on Learning Representations Iclr 2024 (pp. 1-22). US: International Conference on Learning Representations (ICLR).
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2023 Cheng, D., Xie, Y., Xu, Z., Li, J., Liu, L., Liu, J., . . . Feng, Z. (2023). Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference. In Proceedings IEEE International Conference on Data Mining Icdm (pp. 51-60). US: IEEE.
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2023 Xu, Z., Cheng, D., Li, J., Liu, J., Liu, L., & Wang, K. (2023). Disentangled Representation for Causal Mediation Analysis. In Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023 Vol. 37 (pp. 10666-10674). US: Association for the Advancement of Artificial Intelligence.
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2023 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023). Causal Inference with Conditional Instruments Using Deep Generative Models. In B. Williams, Y. Chen, & J. Neville (Eds.), Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023 Vol. 37 (pp. 7122-7130). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
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2023 Deho, O. B., Joksimovic, S., Liu, L., Li, J., Zhan, C., & Liu, J. (2023). Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution. In Proceedings of the 10th ACM Conference on Learning @ Scale (L@S 2023) (pp. 48-58). New York, NY, USA: Association for Computing Machinery (ACM).
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2023 Chen, X., Li, J., Liu, J., Peters, S., Liu, L., Le, T. D., & Walsh, A. (2023). Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation. In Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023 (pp. 1064-1069). US: IEEE.
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2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Li, X., Liu, J., & Waters, T. (2023). Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4970-4980). US: ASSOC COMPUTING MACHINERY.
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2023 Cheng, D., Xu, Z., Li, J., Liu, L., Le, T. D., & Liu, J. (2023). Learning Conditional Instrumental Variable Representation for Causal Effect Estimation. In D. Koutra, C. Plant, M. G. Rodriguez, E. Baralis, & F. Bonchi (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 14169 LNAI (pp. 525-540). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2022 Cheng, D., Li, J., Liu, L., Zhang, J., Le, T. D., & Liu, J. (2022). Ancestral instrument method for causal inference without complete knowledge. In L. Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 4843-4849). US: International Joint Conferences on Artificial Intelligence Organization.
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2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). What is the most effective intervention to increase job retention for this disabled worker?. In KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3981-3991). US: ACM - Association for Computing Machinery Inc.
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2022 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Decision Support for Disability Employment using Counterfactual Survival Analysis. In Proceedings 2022 IEEE International Conference on Big Data Big Data 2022 (pp. 2103-2112). US: IEEE.
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2022 Xu, Z., Xu, Z., Liu, J., Cheng, D., Li, J., Liu, L., & Wang, K. (2022). Assessing Classifier Fairness with Collider Bias. In J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng, & F. Teng (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2022: Advances in Knowledge Discovery and Data Mining Vol. 13281 LNAI (pp. 262-276). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2022 Deho, O. B., Liu, L., Joksimovic, S., Li, J., Zhan, C., & Liu, J. (2022). Assessing the Causal Impact of Online Instruction due to COVID-19 on Students' Grades and its aftermath on Grade Prediction Models. In ACM International Conference Proceeding Series (pp. 32-38). US: Association for Computing Machinery.
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2022 Park, J. Y., Liu, L., Liu, J., & Li, J. (2022). Randomize Adversarial Defense in a Light Way. In Proceedings 2022 IEEE International Conference on Big Data Big Data 2022 (pp. 1080-1089). US: IEEE.
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2021 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2021). Recommending the most effective intervention to improve employment for job seekers with disability. In KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3616-3626). US: Association for Computing Machinery.
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2021 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2021). Divide and conquer: targeted adversary detection using proximity and dependency. In Z. Gong, X. Li, S. G. Oguducu, L. Chen, B. F. Manjon, & X. Wu (Eds.), Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021 (pp. 125-132). US: IEEE.
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2021 Park, J. Y., Liu, L., Li, J., & Liu, J. (2021). Training Neural Networks with Random Noise Images for Adversarial Robustness. In International Conference on Information and Knowledge Management Proceedings (pp. 3358-3362). US: ASSOC COMPUTING MACHINERY.
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2020 Islam, M. Z., Liu, J., Li, J., Liu, L., & Kang, W. (2020). Evidence Weighted Tree Ensembles for Text Classification. In SIGIR 2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1737-1740). US: ACM.
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2020 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2020). LoPAD: A local prediction approach to anomaly detection. In H. W. Lauw, R. C. W. Wong, A. Ntoulas, E. P. Lim, S. K. Ng, & S. J. Pan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12085 (pp. 660-673). Singapore: Springer.
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2020 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2020). Intervention Recommendation for Improving Disability Employment. In X. T. Wu, C. Jermaine, L. Xiong, X. H. Hu, O. Kotevska, S. Y. Lu, . . . J. Saltz (Eds.), Proceedings 2020 IEEE International Conference on Big Data Big Data 2020 (pp. 1671-1680). US: IEEE.
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2020 Cheng, D., Li, J., Liu, L., Liu, J., Yu, K., & Le, T. D. (2020). Causal query in observational data with hidden variables. In G. DeGiacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Eds.), Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 2551-2558). Netherlands: IOS PRESS.
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2020 Liu, J., Li, J., Ye, F., Liu, L., Le, T., Xiong, P., & Liu, H. C. (2020). Building fair predictive models. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020 Proceedings Vol. 12576 LNAI (pp. 216-229). Switzerland: Springer Nature.
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2019 Islam, M. Z., Liu, J., Liu, L., Li, J., & Kang, W. (2019). Semantic explanations in ensemble learning. In Q. Yang, Z. H. Zhou, Z. Gong, M. L. Zhang, & S. J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14–17, 2019, Proceedings, Part I Vol. 11439 LNAI (pp. 29-41). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2019 Islam, M. Z., Liu, J., Li, J., Liu, L., & Kang, W. (2019). A semantics aware random forest for text classification. In International Conference on Information and Knowledge Management Proceedings (pp. 1061-1070). China: ASSOC COMPUTING MACHINERY.
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2019 Liu, J., Kwashie, S., Li, J., Liu, L., & Bewong, M. (2019). Linking graph entities with multiplicity and provenance. In Ceur Workshop Proceedings Vol. 2446 (pp. 1-7). Germany: Rheinisch-Westfaelische Technische Hochschule Aachen Lehrstuhl Informatik V.
2019 Ansah, J., Kwashie, S., Liu, L., Liu, J., Kang, W., & Li, J. (2019). A graph is worth a thousand words: Telling event stories using timeline summarization graphs. In Web Conference 2019 Proceedings of the World Wide Web Conference Www 2019 (pp. 2565-2571). US: ACM Digital Library.
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2019 Chow, C., Mussared, A., Fabris, R., Do, P., Liu, J., Li, J., . . . Zappia, L. (2019). Online water quality monitoring and instruments: the voice of experience. In OzWater'18 (pp. 1-7). Australia: Australian Water Association.
2018 Stumptner, M., Mayer, W., Grossmann, G., Liu, J., Li, W., Casanovas, P., . . . Bainbridge, B. (2018). An Architecture for Establishing Legal Semantic Workflows in the Context of Integrated Law Enforcement. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10791 (pp. 124-139). Switzerland: Springer.
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2018 Ansah, J., Kang, W., Liu, L., Liu, J., & Li, J. (2018). Information propagation trees for protest event prediction. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10939 LNAI (pp. 777-789). Switzerland: Springer International Publishing.
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2018 Ansah, J., Kang, W., Liu, L., Liu, J., & Li, J. (2018). SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection. In H. Hacid, W. Cellary, H. Wang, H. Y. Paik, & R. Zhou (Eds.), Web Information Systems Engineering – WISE 2018 19th International Conference Vol. 11233 LNCS (pp. 281-296). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2018 Zhang, Z., Li, J., Wang, H., Liu, L., & Liu, J. (2018). Which type of classifier to use for networked data, connectivity based or feature based?. In H. Hacid (Ed.), Web Information Systems Engineering – WISE 2018 proceedings Vol. 11233 LNCS (pp. 364-380). Germany: Springer.
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2017 Li, J., Liu, J., Liu, L., Le, T. D., Ma, S., & Han, Y. (2017). Discrimination detection by causal effect estimation. In J. Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, . . . M. Toyoda (Eds.), Proceedings 2017 IEEE International Conference on Big Data Big Data 2017 Vol. 2018-January (pp. 1087-1094). US: IEEE.
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2017 Bewong, M., Liu, J., Liu, L., & Li, J. (2017). Utility aware clustering for publishing transactional data. In J. Kim, K. Shim, L. Cao, J. G. Lee, X. Lin, & Y. S. Moon (Eds.), Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23–26, 2017, Proceedings, Part II Vol. 10235 LNAI (pp. 481-494). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2017 Bewong, M., Liu, J., Liu, L., Li, J., & Choo, K. K. R. (2017). A relative privacy model for effective privacy preservation in transactional data. In Proceedings 16th IEEE International Conference on Trust Security and Privacy in Computing and Communications 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems Trustcom Bigdatase Icess 2017 (pp. 394-401). US: IEEE.
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2017 Ding, X., Ou, Y., Jia, J., Jin, H., & Liu, J. (2017). Efficient subgraph search on large anonymized graphs. In Proceedings 2017 International Conference on Green Informatics Icgi 2017 (pp. 223-228). US: IEEE.
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2017 Kang, W., Chen, J., Li, J., Liu, J., Liu, L., Osborne, G., . . . Neale, G. (2017). Carbon: Forecasting civil unrest events by monitoring news and social media. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10604 LNAI (pp. 859-865). Singapore: Springer.
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2016 Chen, J., Kang, W., Li, J., Liu, J., Liu, L., Cooper, B., . . . Moschou, T. (2016). A temporal classification based predictive model of recurring societal events. In 14th Australasian Data Mining Conference Ausdm 2016 (pp. 93-99). Australia: Australian Computer Society.
2016 Abdel-Fatao, H., Li, J., Liu, J., & Ashfaqur, R. (2016). An effective spatio-temporal approach for predicting future semantic locations. In M. A. Cheema, W. Zhang, & L. Chang (Eds.), ADC 2016: Databases Theory and Applications Vol. 9877 LNCS (pp. 283-294). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
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2015 Mwintieru Nofong, V., Liu, J., & Li, J. (2015). Efficient mining of non-derivable emerging patterns. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9093 (pp. 244-256). Germany: SPRINGER-VERLAG BERLIN.
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2015 Kwashie, S., Liu, J., Li, J., & Ye, F. (2015). Efficient discovery of differential dependencies through association rules mining. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Databases theory and applications Vol. 9093 (pp. 3-15). Germany: SPRINGER-VERLAG BERLIN.
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2015 Abdel-Fatao, H., Li, J., & Liu, J. (2015). STMM: Semantic and temporal-aware Markov chain model for mobility prediction. In C. Zhang, W. Huang, Y. Shi, P. S. Yu, Y. Zhu, Y. Tian, . . . J. He (Eds.), Lecture Notes in Computer Science Vol. 9208 (pp. 103-111). Germany: SPRINGER INTERNATIONAL PUBLISHING AG.
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2015 Abdel-Fatao, H., Li, J., & Liu, J. (2015). Unifying spatial, temporal and semantic features for an effective GPS trajectory-based location recommendation. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9093 (pp. 41-53). Germany: SPRINGER-VERLAG BERLIN.
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2014 Ding, X., Jia, J., Li, J., Liu, J., & Jin, H. (2014). Top-k similarity matching in large graphs with attributes. In S. S. Bhowmick, C. E. Dyreson, C. S. Jensen, M. L. Lee, A. Muliantara, & B. Thalheim (Eds.), DASFAA 2014: International Conference on Database Systems for Advanced Applications Vol. 8422 LNCS (pp. 156-170). US: SPRINGER-VERLAG BERLIN.
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2014 Liu, H. C., Vincent, M., Liu, J., & Li, J. (2014). Logics for representing data mining tasks in inductive databases. In D. Burke, & M. Trick (Eds.), Databases Theory and Applications: 25th Australasian Database Conference, ADC 2014, Brisbane, QLD, Australia, July 14-16, 2014. Proceedings Vol. 8506 LNCS (pp. 214-222). US: Springer.
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2014 Nofong, V. M., Liu, J., & Li, J. (2014). A study on the applications of emerging sequential patterns. In H. Wang, & M. A. Sharaf (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8506 LNCS (pp. 62-73). US: SPRINGER-VERLAG BERLIN.
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2014 Kwashie, S., Liu, J., Li, J., & Ye, F. (2014). Mining differential dependencies: A subspace clustering approach. In H. Wang, & M. A. Sharaf (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8506 LNCS (pp. 50-61). US: SPRINGER-VERLAG BERLIN.
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2013 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., & Sun, B. (2013). Mining causal association rules. In W. Ding, T. Washio, H. Xiong, G. Karypis, B. Thuraisingham, D. Cook, & X. Wu (Eds.), IEEE 13th International Conference on Data Mining Workshops 2013 proceedings (pp. 114-123). US: IEEE Press.
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2013 Ding, X., Yu, Q., Li, J., Liu, J., & Jin, H. (2013). Distributed anonymization for multiple data providers in a cloud system. In Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, Wuhan, China, April 22-25, 2013. Proceedings, Part I Vol. 7825 LNCS (pp. 346-360). Germany: Springer Berlin Heidelberg.
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2013 Jiao, H., Liu, J., Li, J., & Liu, C. (2013). A paradox for trust and reputation in the e-commerce world. In Conferences in Research and Practice in Information Technology Series Vol. 135 (pp. 69-78). US: ACM Digital Library.
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2012 Natarajan, K., Li, J., Liu, J., & Koronios, A. (2012). Rule storage for an efficient rule based inconsistency check. In K. S. Soliman (Ed.), Innovation and Sustainable Competitive Advantage from Regional Development to World Economies Proceedings of the 18th International Business Information Management Association Conference Vol. 4 (pp. 2053-2068). US: INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA.
2012 Jiao, H., Liu, J., Li, J., & Liu, C. (2012). Give rookies a chance: A trust-based institutional online supplier recommendation framework. In D. Gritzalis, S. Furnell, & M. Theoharidou (Eds.), IFIP Advances in Information and Communication Technology Vol. 376 AICT (pp. 400-411). Germany: SPRINGER-VERLAG BERLIN.
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2011 Ye, F., Liu, J., Qian, J., & Shi, Y. (2011). Discovering association rules change from large databases. In H. Deng, D. Q. Miao, J. S. Lei, & F. L. Wang (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7002 LNAI (pp. 388-395). PEOPLES R CHINA, Taiyuan: SPRINGER-VERLAG BERLIN.
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2011 Jiao, H., Liu, J., Li, J., & Liu, C. (2011). Trust for web service composers. In Proceedings of the 12th annual Global Information Technology Management Association world conference (pp. 1-14). US: GITMA.
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2011 Ye, F., Liu, J., Qian, J., & Xue, X. (2011). A framework for mining functional dependencies from large distributed databases. In Proceedings : International Conference on Artificial Intelligence and Computational Intelligence : AICI 2010 Vol. 3 (pp. 109-113). US: IEEE.
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2011 Shahriar, M. S., & Liu, J. (2011). On mining association rules with semantic constraints in XML. In 6th International Conference on digital information management (ICDIM 2011) (pp. 1-5). Australia: IEEE - The Institute of Electrical and Electronic Engineering.
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2011 Jiao, H., Liu, J., Li, J., & Liu, C. (2011). A framework for reputation bootstrapping based on reputation utility and game theories. In G. Wang, S. R. Tate, J. J. Chen, & K. Sakurai (Eds.), Proc 10th IEEE Int Conf on Trust Security and Privacy in Computing and Communications Trustcom 2011 8th IEEE Int Conf on Embedded Software and Systems Icess 2011 6th Int Conf on Fcst 2011 (pp. 344-351). US: IEEE COMPUTER SOC.
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2011 Baig, M. M., Li, J., Liu, J., & Wang, H. (2011). Cloning for privacy protection in multiple independent data publications. In International Conference on Information and Knowledge Management Proceedings (pp. 885-894). New York: ACM.
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2011 Shahriar, M. S., & Liu, J. (2011). Semantic constraint-based XML updating. In Y. Zhang, A. Cuzzocrea, J. H. Ma, K. I. Chung, T. Arslan, & X. F. Song (Eds.), BSBT 2010, DTA 2010: Database Theory and Application, Bio-Science and Bio-Technology Vol. 118 CCIS (pp. 100-109). Germany: Springer.
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2011 Zhou, R., Liu, C., Li, J., Wang, J., & Liu, J. (2011). Evaluating contained rewritings for XPath queries on materialized views. In J. Yu, M. Kim, & R. Unland (Eds.), Database Systems for Advanced Applications Vol. 6587 (pp. 481-495). Germany: Springer.
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2010 Shahriar, M. S., & Liu, J. (2010). Towards evolving constraints in data transformation for XML data warehousing. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 5968 LNCS (pp. 79-86). Germany: Springer Berlin Heidelberg.
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2009 Shahriar, M. D. S., & Liu, J. (2009). Evolving constraints in XML data transformation. In G. Grundspenkis, & J. Janis (Eds.), Advances in Databases and Information Systems: 13th East European Conference, ADBIS 2009, Proceedings. Riga, Latvia: Riga Technical University.
2009 Shahriar, M. D. S., & Liu, J. (2009). Towards the Utilization of Constraints in XML Data Integration. In B. Bouquet, & P. Paolo (Eds.), Proceedings of the International Conference for Digital Libraries and the Semantic Web. Italy: Universtiy of Trento.
2009 Shahriar, M. D. S., & Liu, J. (2009). Performances of checking key and functional dependency satisfaction for XML. In G. Grundspenkis, & J. Janis (Eds.), Advances in databases and information systems : Local proceedings. Riga, Latvia: Riga Technical University.
2009 Baig, M. M., Li, J., Liu, J., & Wang, H. (2009). Studying genotype-phenotype attack on k-anonymised medical and genomic data. In Conferences in Research and Practice in Information Technology Series Vol. 101 (pp. 159-166). Australia: Australian Computer Society.
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2009 Shahriar, M. D. S., & Liu, J. (2009). On defining functional dependancy for XML. In Proceedings of the 3rd International Conference on Semantic Computing (ICSC 2009) (pp. 595-600). US: IEEE.
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2008 Tian, J., Liu, J., Pan, W., Vincent, M., & Liu, C. (2008). Performance analysis and improvement for transformation operators in XML data integration. In Y. Zhang, G. Yu, E. Bertino, & G. Xu (Eds.), Proceedings of the 12th East European conference on advances in databases and information systems , ADBIS. Vol. 4976 (pp. 214-226). Berlin: Springer-Verlag.
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2008 Shahriar, M. D. S., & Liu, J. (2008). On defining referential integrity for XML. In Proceedings of the international symposium on computer science and its applications (CSA '08) (pp. 286-291). USA: IEEE.
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2008 Shahriar, M. D. S., & Liu, J. (2008). Preserving functional dependency in XML data transformation. In P. Atzeni, A. Caplinskas, & H. Jaakkola (Eds.), Proceedings of the 12th East European conference on advances in databases and information systems , ADBIS. Vol. 5207 (pp. 262-278). Berlin: Springer-Verlag.
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2008 Pan, W., Liu, J., & Tian, J. (2008). An implementation of XML data integration. In J. Cordeiro, & J. Filipe (Eds.), Proceedings of the tenth international conference on enterprise information systems, ICEIS Vol. DISI (pp. 111-116). Portugal: INSTICC.
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2008 LI, J., Liu, J., Liu, C., Wang, G., Yu, J., & Yang, C. (2008). Computing structural similarity of source XML schemas against domain XML schema. In F. Fekete, & A. Alan (Eds.), Proceedings of the 19th Australasian databse conference : ADC 2008 Vol. 75 (pp. 155-164). Australia: Australian Computer Society.
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2008 Shahriar, M. D. S., & Liu, J. (2008). Transition of keys in XML data transformation. In Proceedings of the international symposium on computer science and its applications (CSA '08) (pp. 175-180). US: IEEE.
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2008 Shahriar, M. D. S., & Liu, J. (2008). On defining keys for XML. In X. He, Q. Wu, Q. V. Nguyen, & W. Ja (Eds.), Proceedings of the IEEE 8th International Conference on Computer and Information Technology Workshops (pp. 86-91). USA: IEEE.
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2008 Pan, W., Liu, J., & Hawryszkiewycz, I. (2008). A method for describing knowledge work processes. In International workshop on advanced information systems for enterprises , IWAISE 08 (pp. 46-52). US: IEEE.
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2005 Kwon, Y. I., Park, H. H., Liu, J., & Nacimento, M. (2005). Radial projection: a feature extraction method for topographical shapes. In Y. S. Ho, & H. J. Kim (Eds.), Lecture Notes in Computer Science Vol. 3767 LNCS (pp. 582-593). Germany: Springer-Verlag.
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