PPT-Similarity Joins

for Strings and Sets William Cohen SELECT RaSaSbTb FROM RST WHERE RaSa and SbTb WHIRL approach Link items as needed by Q WHIRL approach Query Q SELECT RaSaSbTb FROM RST . Presented by:. Akshay. Kumar. Pankaj. . Prateek. Are these similar?. Number ‘1’ vs. color ‘red’. Number ‘1’ vs. ‘small’. Horse vs. Rider. True vs. false . ‘. Monalisa. ’ vs. ‘Virgin of the rocks’. energies. D.A. . Artemenkov. , G.I. . . Lykasov. , . A.I. . . Malakhov. Joint Institute for Nuclear Research. malakhov@lhe.jinr.ru. Hadron Structure 2015, June 29 – July 3, 2015, . Horn. ý. . . . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. Theory and Applications. Danai Koutra (CMU). Tina Eliassi-Rad (Rutgers) . Christos Faloutsos (CMU). SDM 2014. , Friday April 25. th. 2014, Philadelphia, PA. Who we are. Danai Koutra, CMU. Node and graph similarity,. -. based Clustering. Mohammad. . Rezaei. , Pasi Fränti. rezaei@cs.uef.fi. Speech. and . Image. . Processing. . Unit. University of Eastern Finland. . August 2014. Keyword-Based Clustering. An object such as a text document, website, movie and service can be described by a set of keywords. LPAR 2008 . –. Doha, Qatar. Nikolaj . Bjørner. , . Leonardo de Moura. Microsoft Research. Bruno . Dutertre. SRI International. Satisfiability Modulo Theories (SMT). Accelerating lemma learning using joins. from . GOMMA. Michael . Hartung. , Lars Kolb, . Anika. . Groß. , Erhard Rahm. Database . Research Group. University of . Leipzig. 9th . Intl. . . Conf. . on Data Integration. in . the. Life . Sciences. Word . Similarity: . Distributional Similarity (I). Problems with thesaurus-based . meaning. We don’t have a thesaurus for every language. Even if we do, . they have problems with . recall. M. any . Case-based reasoning. Introduction. Common term in everyday language, where two objects usually are considered similar if they look or sound similar. Similarity is a core concept within CBR. From a CBR perspective: «Two problems are similar if they have similar solutions». a Multi-Layered Indexing Approach. Yongjiang Liang, . Peixiang Zhao. CS @ FSU. zhao@cs.fsu.edu. Outline. Introduction. State-of-the-art solutions. ML-Index & similarity search. Experiments. Conclusion. 1. Recap. Map-reduce ✔️. Algorithms with multiple map-reduce steps. Naïve . bayes. test routine for large datasets and large models. Cleanly describing these algorithms. workflow (or dataflow) languages . Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining in terms of their similarities (or dissimilarities). Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse.