While a significant amount of research efforts has been reported on developing algorithms, based on joins and semijoins, to tackle distributed query processing, there is relatively little progress made toward exploring the complexity of the problems studied. As a result, proving NP-hardness of or devising polynomial-time algorithms for certain distributed query optimization problems has been elaborated upon by many researchers. However, due to its inherent difficulty, the complexity of the majority of problems on distributed query optimization remains unknown. In this paper we generally characterize the distributed query optimization problems and provide a frame work to explore their complexity. As it will be shown, most distributed query optimization problems can be transformed into an optimization problem comprising a set of binary decisions, termed Sum Product Optimization (SPO) problem. We first prove SPO is NP-hard in light of the NP-completeness of a well-known problem, Knapsack (KNAP). Then, using this result as a basis, we prove that five classes of distributed query optimization problems, which cover the majority of distributed query optimization problems previously studied in the literature, are NP-hard by polynomially reducing SPO to each of them. The detail for each problem transformation is derived. We not only prove the conjecture that many prior studies relied upon, but also provide a frame work for future related studies.

The issues involved in developing a distributed database system (DDBS) in a high-speed environment are discussed. The inadequacy of existing database protocols in utilizing the gigabit network is described. A concurrency control protocol that performs better than traditional DDBSs in high-speed networks is developed. Both analytical and simulation results are presented. The focus is on the concurrency control aspect of DDBS since this protocol is at the heart of the overall functioning of the distributed system.>

Performance studies show that traditional semi-join processing methods are sometimes inefficient because of the storage and processing overhead. To remedy this problem, a new semi-join processing method, called one-shot semi-join execution is proposed. This method allows parallel generation of all the semi-join projections, parallel transmission of all the semi-join projections, and parallel execution of all the semi-joins. The authors apply this method to optimize the response time for processing distributed queries. A response time model is established, which considers both data transmission time and local processing time. Based on this model, an efficient query processing algorithm is developed and analyzed.>

A novel semijoin execution strategy is proposed which allows parallelism and processes multiple semijoins simultaneously. In practice most of the parameters needed for query optimization, such as relation cardinality and selectivity, are of fixed-precision. Imposing this fixed-precision constraint, an efficient distributed query processing algorithm is developed. For situations where the fixed-precision constraint does not apply, a method to truncate the parameters and to use the same algorithm to find near-optimal solutions is proposed. By analyzing the truncation errors, a quantitative comparison between the near-optimal solutions and the optimal ones is provided.>