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test.bib

@@ -1,5 +1,17 @@
-@Misc{sift-gist,
-  title        = {Datasets for {ANN} neighbor search},
-  howpublished = {\url{http://corpus-texmex.irisa.fr/}},
+@article{gong_set_reconcilation_2020,
+	title = {Space- and computationally-efficient set reconciliation via parity bitmap sketch ({PBS})},
+	volume = {14},
+	issn = {2150-8097},
+	url = {https://dl.acm.org/doi/10.14778/3436905.3436906},
+	doi = {10.14778/3436905.3436906},
+	abstract = {Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large sets A and B of objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learn AΔB, the difference between A and B, and as a result the reconciled set A∪B.Current set reconciliation schemes are based on either invertible Bloom filters (IBF) or error-correction codes (ECC). The former has a low computational complexity of O(d), where d is the cardinality of AΔB, but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity of O(d2). In this work, we propose Parity Bitmap Sketch (PBS), an ECC-based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity of O(d) just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.},
+	language = {en},
+	number = {4},
+	urldate = {2021-03-03},
+	journal = {Proceedings of the VLDB Endowment},
+	author = {Gong, Long and Liu, Ziheng and Liu, Liang and Xu, Jun and Ogihara, Mitsunori and Yang, Tong},
+	month = dec,
+	year = {2020},
+	pages = {458--470},
+	file = {Gong et al. - 2020 - Space- and computationally-efficient set reconcili.pdf}
 }
-