Abstract
Nowadays, the development of e-government has ushered in the era of big data. Data is playing an increasingly important role in the government’s social management and public services. Government big data refers to various information resources such as documents, forms, or charts generated or obtained by government departments in the process of performing their duties. Government data is large in scale, various sourced, and diverse, so there are some difficulties in how to effectively fuse and analyze the complex government data to gain accurate decisions under the premise of ensuring key information will be preserved and key features will be taken seriously. Starting from the definition of government data, this paper firstly analyzed the meaning and the characteristics of government data, and problems in dealing with government data. Then discussed data fusion framework and techniques for government data in three directions: data level, feature level, and decision level. Finally, summarized existing technology and put forward problems that needed to be studied in the future.
Keywords
Y. Yang, J. Guo, C. Liu, J. Liu, S. Chen, X. Ning—These authors contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
White, F.E.: Data fusion lexicon (1991)
Hall, D.L., Llinas, J.: An introduction to multi-sensor data fusion (2016)
Shen, W.: Exploration for pudong new district government data integration service platform. Inf. Technol. Stand. 13–18 (2021)
Lau, B.P.L., et al.: A survey of data fusion in smart city applications. Inf. Fusion 52, 357–374 (2019)
Yujie, C., Gang, L., Jin, M., Xiao, W.: Emergency information fusion oriented to the whole process of decision making in big data environment. Doc. Inf. Knowl. (5), 95–104 (2018)
Yi, Z., Yujun, C., Bowen, D., Juhua, P., Zhang, X.: Multimodal data fusion model for smart city (2016)
Jie, M., Yan, G., Hongyu, P., Yunkai, Z.: Intelligent city data fusion framework based on multi-source data. Libr. Inf. Serv. 63(15), 6 (2019)
Lenzerini, M.: Tutorial-data integration: a theoretical perspective. In: Symposium on Principles of Database Systems, pp. 233–246 (2003)
Ping, W., Huang, F., Li, G., Liu, X.: Framework for multi-sources spatial data integration analysis. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (2006)
Dong, W.X., Dong, B.B., Zheng, D.X., Yang, W.: Data governance technology. J. Softw. 9(30), 2830–2856 (2019)
Zhou, J., Hong, X., Jin, P.: Information fusion for multi-source material data: progress and challenges. Appl. Sci. 9(17), 3473 (2019)
Bohannon, P., Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for data cleaning. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007 (2007)
Chu, X., Ilyas, I.F., Papotti, P.: Holistic data cleaning: put violations into context (2013)
Xu, C., Jiang, L., Yue, P., Wu, Z., Liang, Z.: Study on government data sharing and spatial association based on linked data technology. J. Geomat. 45(210(06)), 149–153 (2020)
Wang, Z., Zhang, D., Zhou, X., Yang, D., Yu, Z., Yu, Z.: Discovering and profiling overlapping communities in location-based social networks. IEEE Trans. Syst. Man Cybern. Syst. 44(4), 499–509 (2013)
Yang, B., Li, S.: Multifocus image fusion and restoration with sparse representation. IEEE Trans. Instrum. Meas. 59(4), 884–892 (2010)
Yu, N., Qiu, T., Bi, F., Wang, A.: Image features extraction and fusion based on joint sparse representation. IEEE J. Sel. Top. Signal Process. 5(5), 1074–1082 (2011)
Zhang, H., Nasrabadi, N.M., Zhang, Y., Huang, T.S.: Multi-observation visual recognition via joint dynamic sparse representation. In: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, 6–13 November 2011 (2011)
Yan, J., Zheng, W., Xu, Q., Lu, G., Li, H., Bei, W.: Sparse kernel reduced-rank regression for bimodal emotion recognition from facial expression and speech. IEEE Trans. Multimedia 18(7), 1319–1329 (2016)
Zhang, H., Patel, V.M., Chellappa, R.: Low-rank and joint sparse representations for multi-modal recognition. IEEE Trans. Image Process. 26(10), 4741–4752 (2017)
Xin, Z.: Research on image feature representations based on deep neural network. Ph.D. thesis, National University of Defense Technology (2018)
Wenkai, W.: Text feature representation and sentiment analysis based on deep neural network (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
Zhang, X., et al.: Deep fusion of multiple semantic cues for complex event recognition. IEEE Trans. Image Process. 25(3), 1033–1046 (2015)
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML (2011)
Srivastava, N., Salakhutdinov, R., et al.: Multimodal learning with deep Boltzmann machines. In: NIPS, vol. 1, p. 2. Citeseer (2012)
Zhao, L., Hu, Q., Wang, W.: Heterogeneous feature selection with multi-modal deep neural networks and sparse group lasso. IEEE Trans. Multimedia 17(11), 1936–1948 (2015)
Rastegar, S., Soleymani, M., Rabiee, H.R., Shojaee, S.M.: MDL-CW: a multimodal deep learning framework with cross weights. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2601–2609 (2016)
Wang, G.-H., Mao, S.-Y., He, Y., Che, Z.-Y.: Optimal decision fusion when priori probabilities and risk functions are fuzzy. Inf. Fusion 5(1), 5–14 (2004)
Xiao, H.H., Kui, Y.U., Wang, H.: The method of learning Bayesian networks combining association rules with knowledge. Microelectron. Comput. 25(12), 7072 (2008)
Kharya, S., Soni, S., Swarnkar, T.: Weighted Bayesian association rule mining algorithm to construct Bayesian belief network. In: 2019 International Conference on Applied Machine Learning (ICAML), pp. 27–33. IEEE (2019)
Liu, X., Deng, J.: Improved DS method based on conflict evidence correction. J. Electron. Meas. Instrum. 31(9), 1499–1506 (2017)
Chen, Z., Wang, J.-Y.: The research on evidence combination method based on conflict relation network. Acta Electonica Sinica 49(1), 125 (2021)
Wang, X., Di, P., Yin, D.: Conflict evidence fusion method based on lance distance and credibility entropy. Syst. Eng. Electron. 9(5), 1–14 (2021)
Sun, Q., Ye, X., Gu, W.: A new combination rules of evidence theory. Acta Electron. Sin. 28(8), 117–119 (2000)
Han, D.-Q., Deng, Y., Han, C.-Z., Hou, Z.: Weighted evidence combination based on distance of evidence and uncertainty measure. J. Infrared Millim. Waves 30(5), 396–400 (2011)
Guo, X., Sun, Z., Zhou, Y., Qi, L., Zhang, Y.: Evidence conflict measurement method based on pignistic probability transformation and singular value decomposition. J. Commun. 42(4), 150–157 (2021)
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 2333–2338 (2013)
Zadeh, A., Liang, P.P., Mazumder, N., Poria, S., Morency, L.P.: Memory fusion network for multi-view sequential learning (2018)
Le, H., Sahoo, D., Chen, N.F., Hoi, S.: Multimodal transformer networks for end-to-end video-grounded dialogue systems. In: Meeting of the Association for Computational Linguistics (2019)
Holzinger, A., Malle, B., Saranti, A., Pfeifer, B.: Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI. Inf. Fusion 71(7639), 28–37 (2021)
Zhang, W., Yu, J., Zhao, W., Ran, C.: DMRFNet: deep multimodal reasoning and fusion for visual question answering and explanation generation. Inf. Fusion 72(3), 70–79 (2021)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, B. et al. (2022). A Review of Data Fusion Techniques for Government Big Data. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-9709-8_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9708-1
Online ISBN: 978-981-16-9709-8
eBook Packages: Computer ScienceComputer Science (R0)