Optimal Parallel Quantum Query Algorithms

Worst-Case Optimal Algorithms for Parallel Query Processing

In this paper, we study the communication complexity for the problem of computing a conjunctive query on a large database in a parallel setting with p servers. In contrast to previous work, where upper and lower bounds on the communication were specified for particular structures of data (either data without skew, or data with specific types of skew), in this work we focus on worst-case analysi...

On Optimal Pipeline Processing in Parallel Query

A key assumption underlying query optimization schemes for parallel processing is that their cost models can deal with the multitude of eeects encountered during the execution phase. Unfortunately, this is rarely the case and the optimal processing is only achieved in a few situations. In this paper we address the problem to achieve optimal processing under a pipelined execution strategy. The a...

Parallel Algorithms for Top-k Query Processing

The general problem of answering top-k queries can be modeled using lists of objects sorted by their local scores. Fagin et al. proposed the “middleware cost” for a top-k query algorithm, and proposed the efficient sequential Threshold Algorithm (TA). However, since the size of the dataset can be incredible huge, the middleware cost of sequential TA may be intolerable. So, in this paper, we pro...

Optimal Parallel Randomized Algorithms for . . .

Understanding Quantum Algorithms via Query Complexity

Query complexity is a model of computation in which we have to compute a function f(x1, . . . , xN ) of variables xi which can be accessed via queries. The complexity of an algorithm is measured by the number of queries that it makes. Query complexity is widely used for studying quantum algorithms, for two reasons. First, it includes many of the known quantum algorithms (including Grover’s quan...