211,882 research outputs found
Query Order and the Polynomial Hierarchy
Hemaspaandra, Hempel, and Wechsung [cs.CC/9909020] initiated the field of
query order, which studies the ways in which computational power is affected by
the order in which information sources are accessed. The present paper studies,
for the first time, query order as it applies to the levels of the polynomial
hierarchy. We prove that the levels of the polynomial hierarchy are
order-oblivious. Yet, we also show that these ordered query classes form new
levels in the polynomial hierarchy unless the polynomial hierarchy collapses.
We prove that all leaf language classes - and thus essentially all standard
complexity classes - inherit all order-obliviousness results that hold for P.Comment: 14 page
Making Queries Tractable on Big Data with Preprocessing
A query class is traditionally considered tractable if there exists a polynomial-time (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME al-gorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data off-line, so that queries in the class can be subsequently evaluated on the data efficiently. This paper aims to pro-vide a formal foundation for this approach in terms of com-putational complexity. (1) We propose a set of Π-tractable queries, denoted by ΠT0Q, to characterize classes of queries that can be answered in parallel poly-logarithmic time (NC) after PTIME preprocessing. (2) We show that several natu-ral query classes are Π-tractable and are feasible on big data. (3) We also study a set ΠTQ of query classes that can be ef-fectively converted to Π-tractable queries by re-factorizing its data and queries for preprocessing. We introduce a form of NC reductions to characterize such conversions. (4) We show that a natural query class is complete for ΠTQ. (5) We also show that ΠT0Q ⊂ P unless P = NC, i.e., the set ΠT0Q of all Π-tractable queries is properly contained in the set P of all PTIME queries. Nonetheless, ΠTQ = P, i.e., all PTIME query classes can be made Π-tractable via proper re-factorizations. This work is a step towards understanding the tractability of queries in the context of big data. 1
First-Order Query Evaluation with Cardinality Conditions
We study an extension of first-order logic that allows to express cardinality
conditions in a similar way as SQL's COUNT operator. The corresponding logic
FOC(P) was introduced by Kuske and Schweikardt (LICS'17), who showed that query
evaluation for this logic is fixed-parameter tractable on classes of structures
(or databases) of bounded degree. In the present paper, we first show that the
fixed-parameter tractability of FOC(P) cannot even be generalised to very
simple classes of structures of unbounded degree such as unranked trees or
strings with a linear order relation.
Then we identify a fragment FOC1(P) of FOC(P) which is still sufficiently
strong to express standard applications of SQL's COUNT operator. Our main
result shows that query evaluation for FOC1(P) is fixed-parameter tractable
with almost linear running time on nowhere dense classes of structures. As a
corollary, we also obtain a fixed-parameter tractable algorithm for counting
the number of tuples satisfying a query over nowhere dense classes of
structures
Average case quantum lower bounds for computing the boolean mean
We study the average case approximation of the Boolean mean by quantum
algorithms. We prove general query lower bounds for classes of probability
measures on the set of inputs. We pay special attention to two probabilities,
where we show specific query and error lower bounds and the algorithms that
achieve them. We also study the worst expected error and the average expected
error of quantum algorithms and show the respective query lower bounds. Our
results extend the optimality of the algorithm of Brassard et al.Comment: 18 page
Finding Pairwise Intersections Inside a Query Range
We study the following problem: preprocess a set O of objects into a data
structure that allows us to efficiently report all pairs of objects from O that
intersect inside an axis-aligned query range Q. We present data structures of
size and with query time
time, where k is the number of reported pairs, for two classes of objects in
the plane: axis-aligned rectangles and objects with small union complexity. For
the 3-dimensional case where the objects and the query range are axis-aligned
boxes in R^3, we present a data structures of size and query time . When the objects and
query are fat, we obtain query time using storage
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