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智能媒体与数据工程研究所

发布时间:2020-04-30点击量:

智能媒体与数据工程研究所,依托“计算机科学与技术”双一流建设学科的“数据科学与知识工程”方向,着重服务于国家大数据战略发展需求,以“数据为驱动,应用为导向”,面向海量、多源、异构、高维复杂数据,研究大数据采集、处理、分析、挖掘和应用等中的关键科学问题和主要技术瓶颈。研究对象主要聚焦于复杂媒体数据、序列数据、网络数据、时空数据、区块链数据、医学影像数据、基因组数据等,采用机器学习、模式识别、知识发现、博弈论、香农信息论、可证明安全理论等多种优化算法及理论,运用并行分布式计算、云计算、雾计算等新兴计算模式,探索复杂数据内在的规律,并面向各种应用场景研究理论结合实践的各种应用技术。

研究所目前共有专任教师31名,教授10名,副教授12名,高级工程师4名,讲师以及博士后5名。其中,国家优秀青年基金获得者1名,陕西省杰出青年基金获得者1名,陕西高等学校教学名师1名。研究所先后主持国家自然科学基金重点项目1项,国家自然科学基金优秀青年科学基金1项,国家重点研发计划项目课题2项,国家重大专项课题2项,国家自然科学基金面上项目23项,国家自然科学基金青年基金7项,省级重点研发计划项目3项,省级自然科学基金9项,省重大基础研究计划项目1项,省科技计划项目1项,国防预研2项,国家支撑计划1项,横向课题70多项。

近些年来,研究成果丰硕,所发表的高水平论文覆盖了数据与知识工程领域所有顶级国际会议和刊物,包括SIGMOD、SIGKDD、VLDB、ICDE、TKDE、VLDB JTDSC等,累计发表CCF A类文章30多篇,发表CCF B类文章60多篇,其它SCI和EI文章200多篇。获得发明专利70余项,著作权30余项。研究所主持获得了多个省部级奖项,包括:陕西省科学技术二等奖;陕西省自然科学奖二等奖;电子工业部科技进步二等奖;国家教委科技进步二等奖等研究所面向国家与产业需求开展研究工作,与华为、腾讯、中兴、IBM、大唐移动等众多知名公司展开了广泛的合作研究,与中国电子科技集团、兵器工业集团、航空航天和兵器工业部、总参通信与测绘等相关的研究所和部门有着深入的科研合作。研究所完成的系统和关键部件在军民领域得到了多项具体应用,包括应用到陆航自主化直升机、总参测绘、总参通信,兵工档案管理等等,并有完成的系统在国际通信展、澜沧江等国际展示舞台上得以展出并由中央媒体报道。

研究所坚持立德树人,在人才培养方面,获得过Intel杯大学生电子设计竞赛金奖、全国大学生电子设计竞赛一等奖、中国互联网+创新创业大赛全国银奖、全国优秀工程硕士实践奖等。研究所培养的研究生获得了良好的理论基础、动手技能、工程实践能力和科研能力,就业质量高,主要就业单位包括行业领先研究所、华为、腾讯、中兴、阿里、百度、大疆等。培养的博士生能够知名高校和研究所就业以及海外深造。

代表性论文(CCFA类):

[1] Yanguo Peng, Long Wang, Jiangtao Cui, Ximeng Liu, Hui Li, Jianfeng Ma. LS-RQ: ALightweight andForward-secureRangeQuery onGeographicallyEncryptedData. IEEE TDSC. 2020,publishedonline.

[2] Meng Wang, Hui Li, Jiangtao Cui, Ke Deng, Sourav S Bhowmick, Zhenhua Dong. PINOCCHIO: Probabilistic Influence-based Location Selection over Moving Objects. IEEE TKDE. 2016, 28(11): 3068-3082. published online.

[3] Hui Li, Sourav S Bhowmick, Jiangtao Cui, Yunjun Gao, Jianfeng Ma. GetReal: Towards Realistic Selection of Influence Maximization Strategies in Competitive Networks. ACM SIGMOD. 2015, 1525-1537. published online.

[4] Hui Li, Sourav S Bhowmick, Aixin Sun, Jiangtao Cui. Conformity-aware Influence Maximization in Online Social Networks. Springer VLDBJ. 2015, 24(1): 117-141. published online.

[5] Yingfan Liu, Jiangtao Cui, Zi Huang, Hui Li, Hengtao Shen. SK-LSH: An Efficient Index Structure for Approximate Nearest Neighbor Search. Morgan Kaufmann/ACM VLDB. 2014, 7(9): 745-756. published online.

[6] Xiaofeng Zhu, Zi Huang, Hong Cheng, Jiangtao Cui, Hengtao Shen. Sparse Hashing for Fast Multimedia Search. ACM TOIS. 2013, 31(2): 9. published online.

[7] Yanni Li*, Hui Li, Tihua Duan, Sheng Wang, Zhi Wang, Yang Cheng. A Real Linear and Parallel Multiple Longest CommonSubsequences (MLCS) Algorithm. ACM SIGKDD.2016,pp.1725-1734.published online.

[8] Yanni LI,Yuping WANG, Zhensong Zhang, Yaxin Wang, Ding Ma, Jianbin Huang.A Novel Fast and Memory Efficient Parallel MLCS Algorithm for Longer and Large-Scale Sequences Alignments.IEEE ICDE. 2016,pp.1033-1044.published online.

[9] Ziyu Guan, Lijun Zhang, Jinye Peng, Jianping Fan. Multi-view Concept Learning for Data Representation. IEEETKDE. 2015, pages 3016-3028. published online.

[10] Ziyu Guan, Shengqi Yang, Huan Sun, Mudhakar Srivatsa, Xifeng Yan. Fine-Grained Knowledge Sharing in Collaborative Environments. IEEE TKDE. 2015,pages 2163-2174. published online.

[11] Ziyu Guan, Gengxin Miao, Russell McLoughlin, Xifeng Yan, Deng Cai. Co-Occurrence Based Diffusion for Expert Search on the Web. IEEE TKDE.2013, pages 1001-1014. published online.

[12] Ziyu Guan, Fei Xie, Wanqing Zhao, Xiaopeng Wang, Long Chen, Wei Zhao, Jinye Peng. Tag-based Weakly-supervised Hashing for Image Retrieval.Morgan Kaufmann IJCAI.2018, 3776- 3782. published online.

[13] Ziyu Guan, Long Chen, Wei Zhao, Yi Zheng, Shulong Tan, Deng Cai. Weakly-supervised Deep Learning for Customer Review Sentiment Classification.Morgan Kaufmann IJCAI. 2016, pages 3719-3725. published online.

[14] Ziyu Guan, Xifeng Yan, Lance M. Kaplan. Measuring Two-Event Structural Correlations on Graphs.Kaufmann/ACM VLDB.2012,pages 1400-1411. published online.

[15] Hongwei Huo.MSQ-Index: a succinct Index for fast graph similarity search.IEEE TKDE. 2019. published online.

[16] Jianbin Huang,Heli Sun, Qinbao Song, Hongbo Deng, Jiawei Han. Revealing Density-Based Clustering Structure from the Core-Connected Tree of a Network.IEEE TKDE. 2013, 25(8): 1876-1889. published online.

[17] Jianbin Huang, XuejunHuangfu, Heli Sun, Hui Li, Peixiang Zhao, Hong Cheng, Qinbao Song. Backward Path Growth for Efficient Mobile Sequential Recommendation. IEEE TKDE. 2015, 27(1): 46-60. published online.

[18] Zhou Yang, Heli Sun, Jianbin Huang, Zhongbing Sun, Hui Xiong, Shaojie Qiao, Ziyu Guan, Xiaolin Jia. An Efficient Destination Prediction Approach Based on Future Trajectory Prediction and Transition Matrix Optimization. IEEE TKDE. 2018, accepted.

[19]Shaojie Qiao,Nan Han,Yunjun Gao,Rong-Hua Li, Jianbin Huang,Jun Guo,Louis Alberto Gutierrez.A Fast Parallel Community Discovery Model on Complex Networks Through Approximate Optimization.IEEE TKDE.2018,30(9):1638-1651. published online.

[20] Wei Feng, Jianyong Wang, Jiaweihan, CharuAggarwal,Jianbin Huang. StreamCube: Hierarchical Spatio-temporal Hashtag Clustering for Event Exploration over the Twitter Stream. IEEE ICDE. 2015, pp.1561-1572. published online.

[21] Yanni Li, Yuping Wang, Zhensong Zhang, Yaxin Wang, Ding Ma and Jianbin Huang. A Novel Fast and Memory Efficient Parallel MLCS Algorithm for Long and Large-Scale Sequences Alignments. IEEE ICDE. 2016, published online.

[22] Xiaoke Ma,Di Dong, Quan Wang. Community detection in multi-layer networks using joint nonnegative matrix factorization.IEEE TKDE. 2019,31(2):273-286. published online.

[23] Xiaoke Ma, Di Dong,Evolutionary nonnegative matrix factorization algorithms for community detection in dynamic networks.IEEE TKDE. 2017,29(5):1045-1058.published online.

[24] Xiaoke Ma, Wanxin Tang, et al.,Extracting stage-specific and dynamic modulesthrough analyzing multiple networks associatedwith cancer progression. IEEE/ACMTCBB. 2018,15(8):647-658. published online.

[25] Liang Bao, Chase Q. Wu, et al. Performance Modeling and Workflow Scheduling of Microservice-based Applications in Clouds. IEEE TPDS. 2019, vol.30, pages:2114-2129. published online.

[26] Liang Bao, Xin Liu, Fangzheng Wang, and Baoyin Fang. ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks. IEEE/ACM ASE 2019. published online.

[27] Liang Bao, Chase Q. Wu, et al. LAS: Logical-Block Affinity Scheduling in Big Data Analytics Systems. In Proceedings of the IEEE INFOCOM. 2018, pages: 15-19. published online.

[28] Liang Bao, Xin Liu, Ziheng Xu, and Baoyin Fang. AutoConfig: Automatic Configuration Tuning for Distributed Message Systems. In Proceedings of IEEE/ACM ASE. 2018, pages: 29-40. published online.

[29] Xiaoli Wang, Bharadwaj Veeravalli, Haiming Ma. On the Design of a Time, Resource, and Energy Efficient Multi-installment Large-Scale Workload Scheduling Strategy for Nework-based Compute Platforms. IEEE TPDS. 2019, 30(5):1120-1133. published online.

[30] Xiaoli Wang, Bharadwaj Veeravalli. Performance Characterization on Handling Large-scale Partitionable Workloads on Networked Compute Platforms. IEEE TPDS. 2017, 28(10):2925-2938. published online.

[31] Xiaofang Xia, Yang Xiao, Wei Liang. “ABSI: An Adaptive Binary Splitting Algorithm for Malicious Meter Inspection in Smart Grid”, IEEE TIFS. 2019, vol. 14, issue 2, pp.445-458. published online.

[32] Xiaofang Xia, Yang Xiao, Wei Liang. “SAI: A Suspicion Assessment Based Inspection Algorithm to Detect Malicious Users in the Smart Grid”, IEEE TIFS. 2019, vol. 15, pp. 361-374. published online.

[33] Hongwei Huo.Efficient compression and indexing for highly repetitive DNA sequence collections. IEEE/ACM TCBB. 2020. published online.

[34] Zhao Wei, Guan Ziyu, Chen Long, He Xiaofei, Cai Deng, Wang Beidou, Wang Quan. Weakly-Supervised Deep Embedding for Product Review sentiment Analysis. IEEE TKDE. 2018, 30(1):185-197. published online.

[35] Zhao Wei, Zhang Boxuan, Wang Beidou, Guan Ziyu, Guan Wanxian, Qiu Guang, Ning Wei, Chen Jiming, Liu Hongmin. Personalized Attraction Enhanced Sponsored Search with Multi-task Learning. ACM SIGKDD, 2019, pages 2632–2642. published online.

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