Found 2 Possible Name Variants:

Document

**Published in:** LIPIcs, Volume 68, 20th International Conference on Database Theory (ICDT 2017)

Multiround algorithms are now commonly used in distributed data processing systems, yet the extent to which algorithms can benefit from running more rounds is not well understood. This paper answers this question for several rounds for the problem of computing the equijoin of n relations. Given any query Q with width w, intersection width iw, input size IN, output size OUT, and a cluster of machines with M=\Omega(IN \frac{1}{\epsilon}) memory available per machine, where \epsilon > 1 and w \ge 1 are constants, we show that:
1. Q can be computed in O(n) rounds with O(n(INw + OUT)2/M) communication cost with high probability.
Q can be computed in O(log(n)) rounds with O(n(INmax(w, 3iw) + OUT)2/M) communication cost with high probability.
Intersection width is a new notion we introduce for queries and generalized hypertree decompositions (GHDs) of queries that captures how connected the adjacent components of the GHDs are.
We achieve our first result by introducing a distributed and generalized version of Yannakakis's algorithm, called GYM. GYM takes as input any GHD of Q with width w and depth d, and computes Q in O(d + log(n)) rounds and O(n (INw + OUT)2/M) communication cost. We achieve our second result by showing how to construct GHDs of Q with width max(w, 3iw) and depth O(log(n)). We describe another technique to construct GHDs with longer widths and lower depths, demonstrating other tradeoffs one can make between communication and the number of rounds.

Foto N. Afrati, Manas R. Joglekar, Christopher M. Re, Semih Salihoglu, and Jeffrey D. Ullman. GYM: A Multiround Distributed Join Algorithm. In 20th International Conference on Database Theory (ICDT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 68, pp. 4:1-4:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)

Copy BibTex To Clipboard

@InProceedings{afrati_et_al:LIPIcs.ICDT.2017.4, author = {Afrati, Foto N. and Joglekar, Manas R. and Re, Christopher M. and Salihoglu, Semih and Ullman, Jeffrey D.}, title = {{GYM: A Multiround Distributed Join Algorithm}}, booktitle = {20th International Conference on Database Theory (ICDT 2017)}, pages = {4:1--4:18}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-024-8}, ISSN = {1868-8969}, year = {2017}, volume = {68}, editor = {Benedikt, Michael and Orsi, Giorgio}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2017.4}, URN = {urn:nbn:de:0030-drops-70462}, doi = {10.4230/LIPIcs.ICDT.2017.4}, annote = {Keywords: Joins, Yannakakis, Bulk Synchronous Processing, GHDs} }

Document

**Published in:** LIPIcs, Volume 48, 19th International Conference on Database Theory (ICDT 2016)

We optimize multiway equijoins on relational tables using degree information. We give a new bound that uses degree information to more tightly bound the maximum output size of a query. On real data, our bound on the number of triangles in a social network can be up to 95 times tighter than existing worst case bounds. We show that using only a constant amount of degree information, we are able to obtain join algorithms with a running time that has a smaller exponent than existing algorithms - for any database instance. We also show that this degree information can be obtained in nearly linear time, which yields asymptotically faster algorithms in the serial setting and lower communication algorithms in the MapReduce setting.
In the serial setting, the data complexity of join processing can be expressed as a function O(IN^x + OUT) in terms of input size IN and output size OUT in which x depends on the query. An upper bound for x is given by fractional hypertreewidth. We are interested in situations in which we can get algorithms for which x is strictly smaller than the fractional hypertreewidth. We say that a join can be processed in subquadratic time if x < 2. Building on the AYZ algorithm for processing cycle joins in quadratic time, for a restricted class of joins which we call 1-series-parallel graphs, we obtain a complete decision procedure for identifying subquadratic solvability (subject to the 3-SUM problem requiring quadratic time). Our 3-SUM based quadratic lower bound is tight, making it the only known tight bound for joins that does not require any assumption about the matrix multiplication exponent omega. We also give a MapReduce algorithm that meets our improved communication bound and handles essentially optimal parallelism.

Manas R. Joglekar and Christopher M. Ré. It's All a Matter of Degree: Using Degree Information to Optimize Multiway Joins. In 19th International Conference on Database Theory (ICDT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 48, pp. 11:1-11:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{joglekar_et_al:LIPIcs.ICDT.2016.11, author = {Joglekar, Manas R. and R\'{e}, Christopher M.}, title = {{It's All a Matter of Degree: Using Degree Information to Optimize Multiway Joins}}, booktitle = {19th International Conference on Database Theory (ICDT 2016)}, pages = {11:1--11:17}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-002-6}, ISSN = {1868-8969}, year = {2016}, volume = {48}, editor = {Martens, Wim and Zeume, Thomas}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2016.11}, URN = {urn:nbn:de:0030-drops-57800}, doi = {10.4230/LIPIcs.ICDT.2016.11}, annote = {Keywords: Joins, Degree, MapReduce} }

Document

**Published in:** LIPIcs, Volume 18, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012)

We consider Markov decision processes (MDPs) with specifications given as Büchi (liveness) objectives. We consider the problem of computing the set of almost-sure winning vertices from where the objective can be ensured with probability 1. We study for the first time the average case complexity of the classical algorithm for computing the set of almost-sure winning vertices for MDPs with Buchi objectives. Our contributions are as follows:
First, we show that for MDPs with constant out-degree the expected number of iterations is at most logarithmic and the average case running time is linear (as compared to the worst case linear number of iterations and quadratic time complexity). Second, for the average case analysis over all MDPs we show that the expected number of iterations is constant and the average case running time is linear (again as compared to the worst case linear number of iterations and
quadratic time complexity). Finally we also show that given that all MDPs are equally likely, the probability that the classical algorithm requires more than constant number of iterations is exponentially small.

Krishnendu Chatterjee, Manas Joglekar, and Nisarg Shah. Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012). Leibniz International Proceedings in Informatics (LIPIcs), Volume 18, pp. 461-473, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2012)

Copy BibTex To Clipboard

@InProceedings{chatterjee_et_al:LIPIcs.FSTTCS.2012.461, author = {Chatterjee, Krishnendu and Joglekar, Manas and Shah, Nisarg}, title = {{Average Case Analysis of the Classical Algorithm for Markov Decision Processes with B\"{u}chi Objectives}}, booktitle = {IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012)}, pages = {461--473}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-47-7}, ISSN = {1868-8969}, year = {2012}, volume = {18}, editor = {D'Souza, Deepak and Radhakrishnan, Jaikumar and Telikepalli, Kavitha}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2012.461}, URN = {urn:nbn:de:0030-drops-38817}, doi = {10.4230/LIPIcs.FSTTCS.2012.461}, annote = {Keywords: MDPs, Buchi objectives, Average case analysis} }

X

Feedback for Dagstuhl Publishing

Feedback submitted

Please try again later or send an E-mail