ABSTRACT
The sharing of scientific research data on the Internet is already the trend in academia. More and more data have been published to the public throughout the web on Internet. Due to the rapid growth of data, and the requirements of data service quality, the efficiency of data retrieval services has become an important factor affecting service quality. Based on the characteristics of scientific data, and the actual requirements of Pharmaceutical Information Center (PIC, http://pharmdata.ncmi.cn), we propose an efficient scientific data service retrieval method which can greatly improve retrieval speed and service quality. This method includes two work phases. The first phase is to obtain meaningful search keywords from scientific data using semantic analysis technology, including effective keyword sets construction, and eliminating the impact of invalid search keywords. The second phase is to construct a Hash Index Tree (HI-Tree) for valid keywords. Scientific data retrieval service will just traverse the cached HI-Tree instead of traversing the entire database to minimize the database query operation. Compared with traditional database retrieval methods, the experimental results show that our method improves the retrieval efficiency greatly and make better user experience of the data services.
- Pasquetto, I. V., Randles, B. M., and Borgman, C. L. 2017. On the reuse of scientific data. Data Science Journal. 16, 8 (Mar. 2017), 1--9. DOI=https://doi.org/10.5334/dsj-2017-008.Google ScholarCross Ref
- Zhang, Y., Yuan, F., Zhan, Y., and Wang, L. 2015. Relational database keyword retrieval based on index structure. Journal of Hebei University. 35, 1 (April. 2015), 95--101. DOI=http://doi.org/10.3969/j.issn.1000-1565.2015.01.017.Google Scholar
- Merlo-Galeazzi R, Carrasco-Ochoa J A, Martínez-Trinidad J F, et al. Information retrieval based on a query document using maximal frequent sequences. 2013 32nd International Conference of the Chilean Computer Science Society (SCCC). (Nov.2013), 58--62. DOI=https://doi.org/10.1109/SCCC.2013.13Google ScholarCross Ref
- Liu, X., Wang, J., Zhu, M., Deng, F., and Sun, P. 2013. An effective directory index framework taking advantages of hash table and B (+)-tree. Journal of Xi'an Jiaotong University. 47, 4 (Apr. 2013), 105--111. DOI= http://doi.org/10.7652/xjtuxb201304018.Google Scholar
- Zang, W., Li, J. Fang B., et al. 2015. H-Tree: Hierarchy index for online monitoring of big data streams. Chinese Journal of Computers. 38, 1 (Jan. 2015), 35--44.Google Scholar
- Li, X., Song, B., Yu, G., and Wang, D. 2014. L(k)-index: An efficient k-bisimilarity based structural summary supporting label path. Chinese Journal of Computers. 37, 8 (Aug. 2014), 1732--1742.Google Scholar
- Wang, Y., Gu, Y., Zhou, J., and Qu, W. 2015. A graph-based approach for semantic similar word retrieval. In 2015 International Conference on Behavioral, Economic and Socio-cultural Computing. (Oct. 2015), 24--27, DOI= https://doi.org/10.1109/BESC.2015.7365952.Google ScholarCross Ref
- Tang, X., Alabduljalil, M., Jin, X., and Yang, T. 2017. Partitioned similarity search with cache-conscious data traversal. ACM Transactions on Knowledge Discovery from Data. 11, 3 (April. 2017), 1--38. DOI = https://doi.org/10.1145/3014060.Google ScholarDigital Library
- Galakatos, A., Markovitch, M., Binnig, C., Fonseca, R., and Kraska, T. 2019. Fiting-tree: A data-aware index structure. In Proceedings of the 2019 International Conference on Management of Data. (June. 2019), 1189--1206. DOI = https://doi.org/10.1145/3299869.3319860.Google ScholarDigital Library
- Tang, J., Zhou, Z., Xue, X., and Wang, G. 2019. Using collaborative edge-cloud cache for search in Internet of things. IEEE Internet of Things Journal. 7, 2 (Feb. 2020), 922--936. DOI= https://doi.org/10.1109/JIOT.2019.2946389.Google Scholar
- Tolosa, G., Feuerstein, E., Becchetti, L., and Marchetti-Spaccamela, A. 2017. Performance improvements for search systems using an integrated cache of lists+ intersections. Information Retrieval Journal. 20, 3 (May. 2017), 172--198. DOI=https://doi.org/10.1007/978-3-319-11918-2_22.Google ScholarDigital Library
- Nargesian, F., Zhu, E., Pu, K. Q., and Miller, R. J. 2018. Table union search on open data. Proceedings of the VLDB Endowment. 11, 7, (March. 2018), 813--825. DOI=https://doi.org/10.14778/3192965.3192973.Google ScholarDigital Library
- Yang, H. F., Chen, M. L., & Zhen, Z. 2017. Analysis on applicability of common chinese word segmentation software in literature study of traditional chinese medicine text. DEStech Transactions on Computer Science and Engineering. (May. 2017), 698--708. DOI= https://doi.org/10.12783/dtcse/cst2017/12573.Google Scholar
Index Terms
- An Efficient Method for Scientific Data Retrieval Service
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