Query processing in distributed, taxonomy-based information sources

TL;DR

This paper presents algorithms for efficient query processing over distributed, taxonomy-based information sources, analyzing centralized and distributed architectures, and evaluating their performance through simulations on large networks.

Contribution

It introduces a hypergraph-based algorithm for centralized query processing and explores multiple distributed implementations, covering a broad range of system architectures.

Findings

The centralized algorithm is efficient in data complexity.

Distributed architectures can be effectively implemented using different criteria.

Simulation results demonstrate scalability on networks with up to 10,000 nodes.

Abstract

We address the problem of answering queries over a distributed information system, storing objects indexed by terms organized in a taxonomy. The taxonomy consists of subsumption relationships between negation-free DNF formulas on terms and negation-free conjunctions of terms. In the first part of the paper, we consider the centralized case, deriving a hypergraph-based algorithm that is efficient in data complexity. In the second part of the paper, we consider the distributed case, presenting alternative ways implementing the centralized algorithm. These ways descend from two basic criteria: direct vs. query re-writing evaluation, and centralized vs. distributed data or taxonomy allocation. Combinations of these criteria allow to cover a wide spectrum of architectures, ranging from client-server to peer-to-peer. We evaluate the performance of the various architectures by simulation on a network with O(10^4) nodes, and derive final results. An extensive review of the relevant literature is finally included.