Noise-tolerant approximate blocking for dynamic real-time entity resolution

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8444 LNAI, Issue PART 2, 2014, Pages 449-460

  Liang, Huizhi ^a   Wang, Yanzhe ^a   Christen, Peter ^a   Gayler, Ross ^b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b Veda Advantage   (Australia)

Author keywords

Entity Resolution; Indexing; Locality Sensitive Hashing; Real time

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS; INDEXING (OF INFORMATION);

DISTANCE RANGES; ENTITY RESOLUTIONS; HIGH PROBABILITY; LOCALITY SENSITIVE HASHING; MULTIPLE DATA SOURCES; REAL-TIME; REAL-WORLD DATASETS; REAL-WORLD ENTITIES;

DATA MINING;

EID: 84901276901     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-06605-9_37     Document Type: Conference Paper

References (18)

1. Christen, P.: Data Matching. Data-Centric Systems and Appl. Springer (2012)
2. Christen, P., Gayler, R.W.: Adaptive temporal entity resolution on dynamic databases. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 558-569. Springer, Heidelberg (2013)
3. Lange, D., Naumann, F.: Cost-aware query planning for similarity search. Information Systems, 455-469 (2012)
4. Bhattacharya, I., Getoor, L., Licamele, L.: Query-time entity resolution. In: SIGKDD, pp. 529-534 (2006)
5. Christen, P., Gayler, R., Hawking, D.: Similarity-aware indexing for real-time entity resolution. In: CIKM, pp. 1565-1568 (2009)
6. Ramadan, B., Christen, P., Liang, H., Gayler, R.W., Hawking, D.: Dynamic similarity-aware inverted indexing for real-time entity resolution. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013 Workshops. LNCS (LNAI), vol. 7867, pp. 47-58. Springer, Heidelberg (2013)
7. Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: VLDB, pp. 518-529 (1999)
8. Kim, H.S., Lee, D.: HARRA: Fast iterative hashed record linkage for large-scale data collections. In: EDBT, pp. 525-536 (2010)
9. Bawa, M., Condie, T., Ganesan, P.: LSH forest: Self-tuning indexes for similarity search. In: WWW, pp. 651-660 (2005)
10. Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe LSH: Efficient indexing for high-dimensional similarity search. In: VLDB, pp. 950-961 (2007)
11. Das Sarma, A., Jain, A., Machanavajjhala, A., Bohannon, P.: An automatic blocking mechanism for large-scale de-duplication tasks. In: CIKM, pp. 1055-1064 (2012)
12. Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: Scene: A scalable two-stage personalized news recommendation system. In: SIGIR, pp. 125-134 (2011)
13. Anand, R., Ullman, J.D.: Mining of massive datasets. Cambridge University Press (2011)
14. Gan, J., Feng, J., Fang, Q., Ng, W.: Locality-sensitive hashing scheme based on dynamic collision counting. In: SIGMOD, pp. 541-552 (2012)
15. Michelson, M., Knoblock, C.A.: Learning blocking schemes for record linkage. In: AAAI, pp. 440-445 (2006)
16. Yan, S., Lee, D., Kan, M.Y., Giles, L.C.: Adaptive sorted neighborhood methods for efficient record linkage. In: DL, pp. 185-194 (2007)
17. Draisbach, U., Naumann, F., Szott, S., Wonneberg, O.: Adaptive windows for duplicate detection. In: ICDE, pp. 1073-1083 (2012)
18. Christen, P.: Preparation of a real voter data set for record linkage and duplicate detection research. Technical report, Australian National University (2013)