We study the problem of estimating the value of sums of the form Sp≜∑(xip) when one has the ability to sample xi≥0 with probability proportional to its magnitude. When p=2, this problem is equivalent to estimating the selectivity of a self-join query in database systems when one can sample rows randomly. We also study the special case when {xi} is the degree sequence of a graph, which corresponds to counting the number of p-stars in a graph when one has the ability to sample edges randomly. Our algorithm for a (1±ε)-multiplicative approximation of Sp has query and time complexities O(mloglognϵ2S1/pp). Here, m=∑xi/2 is the number of edges in the graph, or equivalently, half the number of records in the database table. Similarly, n is the number of vertices in the graph and the number of unique values in the database table. We also provide tight lower bounds (up to polylogarithmic factors) in almost all cases, even when {xi} is a degree sequence and one is allowed to use the structure of the graph to try to get a better estimate. We are not aware of any prior lower bounds on the problem of join selectivity estimation. For the graph problem, prior work which assumed the ability to sample only vertices uniformly gave algorithms with matching lower bounds (Gonen et al. in SIAM J Comput 25:1365–1411, 2011). With the ability to sample edges randomly, we show that one can achieve faster algorithms for approximating the number of star subgraphs, bypassing the lower bounds in this prior work. For example, in the regime where Sp≤n, and p=2, our upper bound is O~(n/S1/2p), in contrast to their Ω(n/S1/3p) lower bound when no random edge queries are available. In addition, we consider the problem of counting the number of directed paths of length two when the graph is directed. This problem is equivalent to estimating the selectivity of a join query between two distinct tables. We prove that the general version of this problem cannot be solved in sublinear time. However, when the ratio between in-degree and out-degree is bounded—or equivalently, when the ratio between the number of occurrences of values in the two columns being joined is bounded—we give a sublinear time algorithm via a reduction to the undirected case.
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Opting Into Optimal Matchings
We revisit the problem of designing optimal, individually rational matching mechanisms (in
a general sense, allowing for cycles in directed graphs), where each player — who is associated
with a subset of vertices — matches as many of his own vertices when he opts into the matching
mechanism as when he opts out. We offer a new perspective on this problem by considering an
arbitrary graph, but assuming that vertices are associated with players at random. Our main
result asserts that, under certain conditions, any fixed optimal matching is likely to be individually
rational up to lower-order terms. We also show that a simple and practical mechanism is
(fully) individually rational, and likely to be optimal up to lower-order terms. We discuss the
implications of our results for market design in general, and kidney exchange in particular.
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- NSF-PAR ID:
- 10057815
- Date Published:
- Journal Name:
- SIAM: ACM-SIAM Symposium on Discrete Algorithms (SODA17)
- Page Range / eLocation ID:
- 2351 to 2363
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1137/1.9781611974782.155
