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Querytime entity resolution
 In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD
, 2006
"... Entity resolution is the problem of reconciling database references corresponding to the same realworld entities. Given the abundance of publicly available databases that have unresolved entities, we motivate the problem of querytime entity resolution: quick and accurate resolution for answering q ..."
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Cited by 22 (2 self)
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Entity resolution is the problem of reconciling database references corresponding to the same realworld entities. Given the abundance of publicly available databases that have unresolved entities, we motivate the problem of querytime entity resolution: quick and accurate resolution for answering
2.1 Query Time
, 2005
"... In the last lecture we saw how to achieve static dictionaries with O(1) worstcase query time, O(n) worstcase space, and O(n) expected construction time (the FKS scheme). We also discussed Cuckoo Hashing, which in addition achieves O(1) expected updates. In this lecture we discuss how various parts ..."
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In the last lecture we saw how to achieve static dictionaries with O(1) worstcase query time, O(n) worstcase space, and O(n) expected construction time (the FKS scheme). We also discussed Cuckoo Hashing, which in addition achieves O(1) expected updates. In this lecture we discuss how various
Answering queries using views
 In PODS Conference
, 1995
"... We consider the problem of computing answers to queries by using materialized views. Aside from its potential in optimizing query evaluation, the problem also arises in applications such as Global Information Systems, Mobile Computing and maintaining physical data independence. We consider the probl ..."
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Cited by 447 (32 self)
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of deciding which literals from the original query can be deleted). We describe a polynomial time algorithm for nding rewritings, and show that under certain conditions, it will nd the minimal rewriting. Finally, we analyze the complexity of the problems when the queries and views may be disjunctive
Tractable reasoning and efficient query answering in description logics: The DLLite family
 J. OF AUTOMATED REASONING
, 2007
"... We propose a new family of Description Logics (DLs), called DLLite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, b ..."
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Cited by 497 (123 self)
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of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring
Querying timeseries streams
 In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology. ACM
, 2008
"... ABSTRACT Index trees created using distance based indexing are difficult to maintain online since the distance function involved is often costly to compute. This problem is intensified when the database we are dealing with, is frequently updated, as only limited time is available to perform the mai ..."
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Cited by 1 (0 self)
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of answering queries is high. To prolong such deferment, we present innovative techniques that maintain the utility of the tree by migrating its pivots and by partially reconstructing it. As the probabilistic guarantee decays with time and crosses the minimum guarantee threshold, all of the deferred updates
TopicSensitive PageRank
, 2002
"... In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search resu ..."
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Cited by 543 (10 self)
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time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topicsensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when
bits, with query time � �
, 2010
"... In the last lecture we introduced the concept of implicit, succinct, and compact data structures, and gave examples for succinct binary tries, as well as showing the equivalence of binary tries, rooted ordered trees, and balanced parenthesis expressions. Succinct data structures were introduced whic ..."
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In the last lecture we introduced the concept of implicit, succinct, and compact data structures, and gave examples for succinct binary tries, as well as showing the equivalence of binary tries, rooted ordered trees, and balanced parenthesis expressions. Succinct data structures were introduced which solve the rank and select problems. In this lecture we introduce compact data structures for suffix arrays and suffix trees. Recall the problem that we are trying to solve. Given a text T over the alphabet Σ, we wish to preprocess T to create a data structure. We then want to be able to use this data structure to search for a pattern P, also over Σ. A suffix array is an array containing all of the suffixes of T in lexicographic order. In the interests of space, each entry in the suffix array stores an index in T, the start of the suffix in question. To find a pattern P in the suffix array, we perform binary search on all suffixes, which gives us all of the positions of P in T. 2
Fast subsequence matching in timeseries databases
 PROCEEDINGS OF THE 1994 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1994
"... We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space ..."
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Cited by 533 (24 self)
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We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature
An algorithm for finding best matches in logarithmic expected time
 ACM Transactions on Mathematical Software
, 1977
"... An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record. The computation required to organize the file is proportional to kNlogN. The expected number of recor ..."
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Cited by 764 (2 self)
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An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record. The computation required to organize the file is proportional to kNlogN. The expected number
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 521 (8 self)
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NN algorithm for the caseÔ�. We also show that the algorithm finds the exact near neigbhor time for data satisfying certain “bounded growth ” condition. Unlike earlier schemes, our LSH scheme works directly on points in the Euclidean space without embeddings. Consequently, the resulting query time
Results 1  10
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