Abstract
Identifying frequently occurring items is a basic building block in many data stream applications. A great deal of work for efficiently identifying frequent items has been studied on the landmark and sliding window models. In this work, we revisit this problem on a new streaming model based on time decay, where the importance of every arrival item is decreased over the time. To address the importance changes over the time, we propose a new heap structure, named Quasi-heap, which maintains the item order using a lazy update mechanism. Two approximation algorithms, Space Saving with Quasi-heap (SSQ) and Filtered Space Saving with Quasi-heap (FSSQ), are proposed to find the frequently occurring items based on the Quasi-heap structure. Extensive experiments demonstrate the superiority of proposed algorithms in terms of both efficiency (i.e., response time) and effectiveness (i.e., accuracy).
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Notes
- 1.
Frequent Itemset Mining Dataset Repository http://fimi.cs.helsinki.fi/data/.
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Acknowledgement
This work was supported by the public key plan of Zhejiang Province (2014C23005), National Science and Technology Supporting plan (2013BAH62F02 and 2013BAH27F01), China mobile research fund of education ministry (mcm20130671), the cultural relic protection science and technology project of Zhejiang Province, University of Macau RC (MYRG2014-00106-FST), and NSFC of China (61502548).
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Wu, S., Lin, H., U, L.H., Gao, Y., Lu, D. (2016). Finding Frequent Items in Time Decayed Data Streams. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_2
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DOI: https://doi.org/10.1007/978-3-319-45817-5_2
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