Data indexing, software performance, SIMD, performance optimization, vectorization.
Most recent papers
- Samy Chambi, Daniel Lemire, Owen Kaser, Robert Godin, Better bitmap performance with Roaring bitmaps, Software: Practice and Experience 46 (5), 2016. (Cited at least 48 times.)
- Daniel Lemire and Leonid Boytsov, Decoding billions of integers per second through vectorization, Software: Practice & Experience 45 (1), 2015. (Cited at least 105 times.)
- Xiaodan Zhu, Peter Turney, Daniel Lemire, Andre Vellino, Measuring academic influence: Not all citations are equal, Journal of the Association for Information Science and Technology 66 (2), 2015. (Cited at least 38 times.)
Recently cited papers
- Daniel Lemire, Owen Kaser, Kamel Aouiche, Sorting improves word-aligned bitmap indexes, Data & Knowledge Engineering 69 (1), 2010. (Cited at least 101 times.)
- Daniel Lemire, Faster retrieval with a two-pass dynamic-time-warping lower bound, Pattern recognition 42 (9), 2009. (Cited at least 101 times.)
- Owen Kaser and Daniel Lemire, Tag-Cloud Drawing: Algorithms for Cloud Visualization, Tagging and Metadata for Social Information Organization (WWW 2007), 2007. (Cited at least 250 times.)
- Daniel Lemire and Anna Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, SDM '05, 2005. (Cited at least 573 times.)
- Engineering Fast Indexes for Big Data Applications (Spark Summit East 2017, Boston)
- Engineering Fast Indexes for Big Data Applications (deep dive) (Spark Summit East 2017, Boston)
- Algorithms, how content finds 'you' (Discoverability Summit, Toronto, 2016)
Recent International Conference Program Committees
- ACM Conference on Information and Knowledge Management (ACM CIKM 2013, 2017)
- ACM Conference on Web Search and Data Mining (ACM WSDM 2013)
- ACM Conference on Information Retrieval (ACM SIGIR 2015, 2016)
- ACM Conference on Recommender Systems (ACM RecSys 2012, 2017)
- ACM/IEEE Joint Conference on Digital Libraries (JCDL 2016)
- World Wide Web Conference (WWW 2017)
Daniel Lemire is a full professor in computer science at the University of Quebec (TELUQ). His research is focused on data indexing techniques. For example, he worked on bitmap indexes, column-oriented databases and integer compression. He is also interested in database design and probabilistic algorithms (e.g., universal hashing). His work on bitmap indexes is used by companies such as eBay, LinkedIn, Facebook and Netflix in their data warehousing, within big-data platforms such as Apache Hive, Druid, Apache Spark, Netflix Atlas, LinkedIn Pinot and Apache Kylin. The version control system Git is also accelerated by the same compressed bitmaps. Some of his techniques were adopted by Apache Lucene, the search engine behind sites such as Wikipedia or platforms such as Solr and Elastic. One of his hashing techniques has been adopted by Google TensorFlow. His Slope One recommender algorithm is a standard reference in the field of recommender systems. He is a beneficiary of the Google Open Source Peer Bonus Program. He has written over 50 peer-reviewed publications, including more than 30 journal articles. He has held competitive research grants for the last 15 years. He serves on the program committees of leading computer science conferences (e.g., ACM CIKM, WWW, ACM WSDM, ACM SIGIR, ACM RecSys).
ContactDaniel Lemire, professor LICEF Research Center, TELUQ Université du Québec 5800 Saint-Denis Office 1105 Montreal (Quebec) H2S 3L5 Canada
Room 12.166, Email : lemire (at) gmail (dot) com
When visiting, come by the eleventh floor, take the stairs and find my office on the twelfth floor near the LICEF Research Center.