Spatio-Temporal data analytics:
Spatio-Temporal data, generated by either GPS-embedded equipment (such as iPhones) or manual inputs from users, contains large amount of noise, which makes the quality of the data analysis significantly low in many applications. To solve the problem, we propose a reinforcement learning algorithm to robustly decrease the influence of the noise. The algorithm can also automatically determine the number of clusters based on the data distributions. In addition, spatio-temporal data (such as social media data) often arrive in a stream manner, therefore, it is straightforward to design an online data processing model for detecting events hidden in the streams. For such a sake, we propose an online algorithm for hierarchical categorizations and detections of events in the streams. The results are published in Sigir，ACM TIST，CIKM, ICDE, IJCAI, Sigmod, and PVLDB.
Social media data mining and analytics
Social Network Analysis:
In this area, we tackle node influence and sampling techniques on the social network, we also apply the algorithms to recommendation algorithms. Our theoretical study shows the convergence speed of the node sampling is related to the topology structure of the social network, that motivates us to propose a dynamic tuning algorithm for the sampling. The algorithm significantly speeds up the convergence speed of the social network sampling. The related results are published in ICDE, ACM TODS, TKDE, KIS, and WWWJ.
Web data crawling:
Spatio-temporal data are stored and managed in various web hidden databases which can only provide a restricted access to the data. However, mining tasks often require a large amount of data from the hidden databases for their analysis. We solve the problem with two techniques: sampling techniques for interface extension and crawling techniques for kNN-based hidden databases. The related results are published in TKDE.
Application scenarios of social media data mining
Relevant publication
[1 ]Dichao Li, Zhiguo Gong, and Defu Zhang: A Common Topic Transfer Learning Model for Crossing City POI Recommendations. IEEE Transactions on Cybernetics. Accepted (2018).
[2] Na Ta, Guoliang Li, Tianyu Zhao, Jianhua Feng, Hanchao Ma, Zhiguo Gong: An Efficient Ride-Sharing Framework for Maximizing Shared Route. IEEE Trans. Knowl. Data Eng. 30(2): 219-233 (2018)
[3] Yuhong Li, Jie Bao, Member, Yanhua Li, Member, Yingcai Wu, Zhiguo Gong, and Yu Zheng. Mining the Most Influential k-Location Set From Massive Trajectories. IEEE Transactions on Big Data, (2018).
[4] Juan Lu, Zhiguo Gong, Xuemin Lin. A Novel and Fast SimRank Algorithm. IEEE Transactions on Knowledge and Data Engineering (2017)
[5] Zhenguo Yang, Qing Li, Zheng Lu, Yun Ma, Zhiguo Gong, Wenyin Liu. Dual Structure Constrained Multimodal Feature Coding for Social Event Detection in Flickr Data. ACM Transactions on Internet Technology (2017)
[6] Hui Yan, Zhiguo Gong, Nan Zhang, Tao Huang, Hua Zhong, Jun Wei. Crawling Hidden Objects with kNN Queries. IEEE Transactions on Knowledge and Data Engineering. (2016)
[7] Huiqi Hu Guoliang Li Zhifeng Bao Jianhua Feng Zhiguo Gong. Top-k Spatial-Textual Similarity Join. IEEE Transactions on Knowledge and Data Engineering. (2016)
[8] Zhuojie Zhou, Nan Zhang, Zhiguo Gong, and Gautam Gas. Faster Random Walks By Rewiring Online Social Networks On-The-Fly. ACM Transactions on Database Systems. (2016)
[9] Hui Yan, Zhiguo Gong, Nan Zhang, Tao Huang, Hua Zhong, Jun Wei. “Aggregate Estimation in Hidden Databases with Checkbox Interfaces”. IEEE Transactions on Knowledge and Data Engineering, 27(5) (2015), pp.1192-1204.
[10] Ruicheng Zhong, Guoliang Li, Kian-lee Tan, Lizhu Zhou, Zhiguo Gong. “G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks”. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(8) (2015): 2175-2189
[11] Bailong Liao, Leong Hou U, Man Lung Yiu, and Zhiguo Gong. “Beyond Millisecond Latency k NN Search on Commodity Machine”. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(10): 2618-2631 (2015).
[12] Minghe Yu, Guoliang Li, Ting Wang, Jianhua Feng, Zhiguo Gong. “Efficient Filtering Algorithms for Location-Aware Publish/Subscribe”. IEEE Trans. Knowl. Data Eng. 27(4): 950-963 (2015)
[13] Yiyang Yang, Zhiguo Gong, Leong Hou U. “Identifying Points of Interest using heterogenous Features”. ACM Transactions on Intelligent Systems and Technology. 5(4): 68:1-68:27 (2014).
[14] Leong Hou U, Hongjun Zhao, Man Lung Yiu, Yuhong Li, and Zhiguo Gong. “Towards Online Shortest Paths Computation”. IEEE Transaction on Knowledge and Data Engineering. 26(4): 1012-1025 (2014).
[15] Jinjin Guo, Zhiguo Gong, A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data, In Proceedings of the 26th IJCAI2017.
[16] Yiyang, Zhiguo Gong, Qing Li, Leong Hou U, Ruichu Cai, Zhifeng Hao. A Robust Noise Resistant Algorithm for POI Identification from Flickr Data. In Proceedings of the 26th IJCAI2017.
[17] Ngai Meng Kou, Yan Li, Hao Wang, Leong Hou U, Zhiguo Gong, “Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments”, Proceedings of the SIGMOD, 2017.
[18] Jinjin Guo, Zhiguo Gong. A Nonparametric Model for Event Discovery in the Geospatial-Temporal Space. ACM CIKM2016.
[19] Yuhong Li, Jie Bao, Yanhua Li, Yingcai Wu, Zhiguo Gong, Yu Zheng. ACM Sigspatial 2016
[20] Ngai Meng Kou, Leng Hou U, Nikos Mamoulis, Zhiguo Gong. Weighted Coverage based Reviewer Assignment. In Proceedings of ACM Sigmod2015.
[21] Yuhong Li, Leong Hou U, Man Lung Yiu, and Zhiguo Gong. Quick-Motif: An Efficient and Scalable Framework for Exact Motif Discovery. In Proceedings of ICDE2015.
[22] Ngai Meng Kou, Leong Hou U, Nikos Mamoulis, Yuhong Li, Ye Li, Zhiguo Gong. A Topic-based Reviewer Assignment System. PVLDB 8(12): 1852-1863 (2015).
[23] Yuhong Li, Yu Zheng, Shenggong Ji, Wenjun Wang, Leong Hou U, and Zhiguo Gong. Location Selection for Ambulance Stations: A Data-Driven Approach. In Proceedings of ACM Sigspatial 2015.