TY - GEN
T1 - Fishing in the stream
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
AU - Kraus, Naama
AU - Carmel, David
AU - Keidar, Idit
N1 - Funding Information: Naama Kraus is grateful to the Hasso-Plattner-Institut (HPI) for the scholarship for doctoral studies. We thank Alexander Spiegelman for helpful insights and suggestions. Publisher Copyright: © 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Similarity search is the task of retrieving data items that are similar to a given query. In this paper, we introduce the time-sensitive notion of similarity search over endless data-streams (SSDS), which takes into account data quality and temporal characteristics in addition to similarity. SSDS is challenging as it needs to process unbounded data, while computation resources are bounded. We propose Stream-LSH, a randomized SSDS algorithm that bounds the index size by retaining items according to their freshness, quality, and dynamic popularity attributes. We show that Stream-LSH increases recall when searching for similar items compared to alternative approaches using the same space capacity.
AB - Similarity search is the task of retrieving data items that are similar to a given query. In this paper, we introduce the time-sensitive notion of similarity search over endless data-streams (SSDS), which takes into account data quality and temporal characteristics in addition to similarity. SSDS is challenging as it needs to process unbounded data, while computation resources are bounded. We propose Stream-LSH, a randomized SSDS algorithm that bounds the index size by retaining items according to their freshness, quality, and dynamic popularity attributes. We show that Stream-LSH increases recall when searching for similar items compared to alternative approaches using the same space capacity.
KW - Dynamic popularity
KW - Locality sensitive hashing
KW - Retention policy
KW - Similarity search
KW - Stream search
UR - https://www.scopus.com/pages/publications/85047735585
U2 - 10.1109/BigData.2017.8258016
DO - 10.1109/BigData.2017.8258016
M3 - Conference contribution
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 964
EP - 969
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
Y2 - 11 December 2017 through 14 December 2017
ER -