Beyond Equi-joins: Ranking, Enumeration and Factorization
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Wolfgang Gatterbauer | Mirek Riedewald | Nikolaos Tziavelis | Mirek Riedewald | Wolfgang Gatterbauer | Nikolaos Tziavelis
[1] Luc Segoufin,et al. Constant Delay Enumeration for Conjunctive Queries , 2015, SGMD.
[2] Patrick K. Nicholson,et al. Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs , 2018, WWW.
[3] John R. Smith,et al. Supporting Incremental Join Queries on Ranked Inputs , 2001, VLDB.
[4] Wolfgang Gatterbauer,et al. Optimal Join Algorithms Meet Top-k , 2020, SIGMOD Conference.
[5] Bernard Chazelle,et al. A Functional Approach to Data Structures and Its Use in Multidimensional Searching , 1988, SIAM J. Comput..
[6] Dimitrios Gunopulos,et al. Answering top-k queries using views , 2006, VLDB.
[7] Jakub Závodný,et al. Factorised representations of query results: size bounds and readability , 2012, ICDT '12.
[8] Paolo Papotti,et al. Fast and scalable inequality joins , 2017, The VLDB Journal.
[9] Divesh Srivastava,et al. Processing top-k join queries , 2010, Proc. VLDB Endow..
[10] Dániel Marx,et al. Tractable Hypergraph Properties for Constraint Satisfaction and Conjunctive Queries , 2009, JACM.
[11] Man Lung Yiu,et al. Efficient top-k aggregation of ranked inputs , 2007, TODS.
[12] Jakub Závodný,et al. Aggregation and Ordering in Factorised Databases , 2013, Proc. VLDB Endow..
[13] Hung Q. Ngo,et al. Worst-Case Optimal Join Algorithms: Techniques, Results, and Open Problems , 2018, PODS.
[14] Dan Olteanu,et al. Using OBDDs for Efficient Query Evaluation on Probabilistic Databases , 2008, SUM.
[15] Robert E. Tarjan,et al. Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs , 1984, SIAM J. Comput..
[16] M. Naor,et al. Optimal aggregation algorithms for middleware , 2001, PODS '01.
[17] John R. Smith,et al. The onion technique: indexing for linear optimization queries , 2000, SIGMOD '00.
[18] Shaleen Deep,et al. Ranked Enumeration of Conjunctive Query Results , 2019, ArXiv.
[19] Dan Olteanu,et al. Learning Linear Regression Models over Factorized Joins , 2016, SIGMOD Conference.
[20] Mehryar Mohri,et al. Semiring Frameworks and Algorithms for Shortest-Distance Problems , 2002, J. Autom. Lang. Comb..
[21] Mirek Riedewald,et al. Tractable Orders for Direct Access to Ranked Answers of Conjunctive Queries , 2020, PODS.
[22] Johann Brault-Baron,et al. De la pertinence de l'énumération : complexité en logiques propositionnelle et du premier ordre. (The relevance of the list: propositional logic and complexity of the first order) , 2013 .
[23] Dan Suciu,et al. Answering Conjunctive Queries with Inequalities , 2016, Theory of Computing Systems.
[24] Dan Suciu,et al. Boolean Tensor Decomposition for Conjunctive Queries with Negation , 2017, ICDT.
[25] Jakub Závodný,et al. On Factorisation of Provenance Polynomials , 2011, TaPP.
[26] Dániel Marx,et al. Size Bounds and Query Plans for Relational Joins , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[27] Johann Brault-Baron,et al. Hypergraph Acyclicity Revisited , 2014, ACM Comput. Surv..
[28] Hung Q. Ngo,et al. In-Database Learning with Sparse Tensors , 2017, PODS.
[29] Dan E. Willard. Applications of Range Query Theory to Relational Data Base Join and Selection Operations , 1996, J. Comput. Syst. Sci..
[30] Richard Pavley,et al. A Method for the Solution of the Nth Best Path Problem , 1959, JACM.
[31] Wolfgang Gatterbauer,et al. Optimal Algorithms for Ranked Enumeration of Answers to Full Conjunctive Queries , 2019, Proc. VLDB Endow..
[32] Markus Kröll,et al. On the Enumeration Complexity of Unions of Conjunctive Queries , 2018, PODS.
[33] Mihalis Yannakakis,et al. On the Complexity of Database Queries , 1999, J. Comput. Syst. Sci..
[34] Dan Olteanu,et al. Secondary-storage confidence computation for conjunctive queries with inequalities , 2009, SIGMOD Conference.
[35] Guido Moerkotte,et al. Efficient Evaluation of Aggregates on Bulk Types , 1995, DBPL.
[36] Thomas Seidl,et al. Joining interval data in relational databases , 2004, SIGMOD '04.
[37] Wolfgang Gatterbauer,et al. Factorized Graph Representations for Semi-Supervised Learning from Sparse Data , 2020, SIGMOD Conference.
[38] Peter L. Hammer,et al. Boolean Functions - Theory, Algorithms, and Applications , 2011, Encyclopedia of mathematics and its applications.
[39] Clement T. Yu,et al. An algorithm for tree-query membership of a distributed query , 1979, COMPSAC.
[40] Gonzalo Navarro,et al. Optimal Joins using Compact Data Structures , 2019, ICDT.
[41] Yi Lu,et al. Path Problems in Temporal Graphs , 2014, Proc. VLDB Endow..
[42] Timothy M. Chan,et al. Necklaces, Convolutions, and X+Y , 2006, Algorithmica.
[43] Wolfgang Lehner,et al. General dynamic Yannakakis: conjunctive queries with theta joins under updates , 2019, The VLDB Journal.
[44] F. Frances Yao,et al. Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.
[45] Neoklis Polyzotis,et al. Robust and efficient algorithms for rank join evaluation , 2009, SIGMOD Conference.
[46] Jakub Závodný,et al. FDB: A Query Engine for Factorised Relational Databases , 2012, Proc. VLDB Endow..
[47] Dan E. Willard,et al. An Algorithm for Handling Many Relational Calculus Queries Efficiently , 2002, J. Comput. Syst. Sci..
[48] Yufei Tao,et al. A Guide to Designing Top-k Indexes , 2019, SGMD.
[49] Arnaud Durand,et al. On Acyclic Conjunctive Queries and Constant Delay Enumeration , 2007, CSL.
[50] Wolfgang Gatterbauer,et al. Any-k Algorithms for Exploratory Analysis with Conjunctive Queries , 2018, ExploreDB@SIGMOD/PODS.
[51] Jeffrey Xu Yu,et al. Optimal Enumeration: Efficient Top-k Tree Matching , 2015, Proc. VLDB Endow..
[52] Dan Olteanu,et al. Covers of Query Results , 2017, ICDT.
[53] Atri Rudra,et al. Skew strikes back: new developments in the theory of join algorithms , 2013, SGMD.
[54] Jure Leskovec,et al. Community Interaction and Conflict on the Web , 2018, WWW.
[55] Maarten Löffler,et al. Range Searching , 2016, Encyclopedia of Algorithms.
[56] Atri Rudra,et al. Beyond worst-case analysis for joins with minesweeper , 2014, PODS.
[57] Benjamin Moseley,et al. On Functional Aggregate Queries with Additive Inequalities , 2018, PODS.
[58] Ihab F. Ilyas,et al. A survey of top-k query processing techniques in relational database systems , 2008, CSUR.
[59] David J. DeWitt,et al. An Evaluation of Non-Equijoin Algorithms , 1991, VLDB.
[60] Walid G. Aref,et al. Supporting top-kjoin queries in relational databases , 2004, The VLDB Journal.
[61] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[62] Shaleen Deep,et al. Compressed Representations of Conjunctive Query Results , 2017, PODS.
[63] Jack W. Stokes,et al. Latte: Large-Scale Lateral Movement Detection , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).
[64] Jeffrey F. Naughton,et al. Learning Generalized Linear Models Over Normalized Data , 2015, SIGMOD Conference.
[65] David Eppstein,et al. Finding the k Shortest Paths , 1999, SIAM J. Comput..
[66] Pankaj K. Agarwal,et al. Dynamic Enumeration of Similarity Joins , 2021, ICALP.
[67] Atri Rudra,et al. Joins via Geometric Resolutions: Worst-case and Beyond , 2014, PODS.
[68] Vagelis Hristidis,et al. PREFER: a system for the efficient execution of multi-parametric ranked queries , 2001, SIGMOD '01.
[69] R. Varshney,et al. Supporting top-k join queries in relational databases , 2011 .
[70] Michel Minoux,et al. Graphs, dioids and semirings : new models and algorithms , 2008 .
[71] Stefan Manegold,et al. Progressive Join Algorithms Considering User Preference , 2021, CIDR.
[72] Andrés Marzal,et al. Computing the K Shortest Paths: A New Algorithm and an Experimental Comparison , 1999, WAE.
[73] Wolfgang Gatterbauer,et al. Near-Optimal Distributed Band-Joins through Recursive Partitioning , 2020, SIGMOD Conference.
[74] Nicole Schweikardt,et al. Answering Conjunctive Queries under Updates , 2017, PODS.
[75] Markus Kröll,et al. Enumeration Complexity of Conjunctive Queries with Functional Dependencies , 2018, ICDT.
[76] Arnaud Durand,et al. Fine-Grained Complexity Analysis of Queries: From Decision to Counting and Enumeration , 2020, PODS.
[77] Nicole Schweikardt,et al. Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration , 2019, PODS.
[78] Dan Olteanu,et al. Factorized Databases , 2016, SGMD.
[79] Wolfgang Gatterbauer,et al. Towards a Dichotomy for Minimally Factorizing the Provenance of Self-Join Free Conjunctive Queries , 2021, ArXiv.
[80] Wolfgang Lehner,et al. Efficient Query Processing for Dynamically Changing Datasets , 2019, SGMD.
[81] Todd L. Veldhuizen,et al. Leapfrog Triejoin: A Simple, Worst-Case Optimal Join Algorithm , 2012, 1210.0481.
[82] Jakub Závodný,et al. Size Bounds for Factorised Representations of Query Results , 2015, TODS.
[83] E. Lawler. A PROCEDURE FOR COMPUTING THE K BEST SOLUTIONS TO DISCRETE OPTIMIZATION PROBLEMS AND ITS APPLICATION TO THE SHORTEST PATH PROBLEM , 1972 .
[84] Moshe Y. Vardi. The complexity of relational query languages (Extended Abstract) , 1982, STOC '82.
[85] Nicole Schweikardt,et al. Constant Delay Enumeration with FPT-Preprocessing for Conjunctive Queries of Bounded Submodular Width , 2020, MFCS.
[86] D. Gifford. 1962 , 1962, The Selected Letters of John Berryman.
[87] Dan Olteanu,et al. F: Regression Models over Factorized Views , 2016, Proc. VLDB Endow..
[88] Georg Gottlob,et al. Hypertree Decompositions: Questions and Answers , 2016, PODS.
[89] Dan Suciu,et al. What Do Shannon-type Inequalities, Submodular Width, and Disjunctive Datalog Have to Do with One Another? , 2016, PODS.
[90] A. Foran,et al. Quicksort , 1962, Comput. J..
[91] Mam Riess Jones. Color Coding , 1962, Human factors.
[92] Mihalis Yannakakis,et al. Algorithms for Acyclic Database Schemes , 1981, VLDB.