Towards an Efficient Evaluation of General Queries

Database applications often require to evaluate queries containing quantifiers or disjunctions, e.g., for handling general integrity constraints. Existing efficient methods for processing quantifiers depart from the relational model as they rely on non-algebraic procedures. Looking at quantified query evaluation from a new angle, we propose an approach to process quantifiers that makes use of relational algebra operators only. Our approach performs in two phases. The first phase normalizes the queries producing a canonical form. This form permits to improve the translation into relational algebra performed during the second phase. The improved translation relies on a new operator - the complement-join - that generalizes the set difference, on algebraic expressions of universal quantifiers that avoid the expensive division operator in many cases, and on a special processing of disjunctions by means of constrained outer-joins. Our method achieves an efficiency at least comparable with that of previous proposals, better in most cases. Furthermore, it is considerably simpler to implement as it completely relies on relational data structures and operators

An inside analysis of a genetic-programming based optimizer

The use of evolutionary algorithms has been proposed as a powerful random search strategy to solve the join order problem. Specifically, genetic programming used in query optimization has been proposed as an alternative to the limitations of dynamic programming with large join queries. However, very little is known about the impact and behavior of the genetic operations used in this type of algorithms. In this paper, we present an analysis that helps us to understand the effect of these operations during the optimization execution. Specifically, we study five different aspects: the age of the members in the population in terms of generations, the number of query execution plans (QEP) discarded without producing new offsprings, the average QEP life time in generations, the efficiency of the genetic operations and the evolution of the best cost. All in all, our analysis allows us to understand the impact of crossovers compared to mutation operations and the dynamically changing effects of these operations.Peer Reviewe

AP-Tree: Efficiently support continuous spatial-keyword queries over stream

© 2015 IEEE. We investigate the problem of processing a large amount of continuous spatial-keyword queries over streaming data, which is essential in many applications such as location-based recommendation and advertising, thanks to the proliferation of geo-equipped devices and the ensuing location-based social media applications. For example, a location-based e-coupon system may allow potentially millions of users to register their continuous spatial-keyword queries (e.g., interests in nearby sales) by specifying a set of keywords and a spatial region; the system then delivers each incoming spatial-textual object (e.g., a geo-tagged e-coupon) to all the matched queries (i.e., users) whose spatial and textual requirements are satisfied. While there are several prior approaches aiming at providing efficient query processing techniques for the problem, their approaches belong to spatial-first indexing method which cannot well exploit the keyword distribution. In addition, their textual filtering techniques are built upon simple variants of traditional inverted indexes, which do not perform well for the textual constraint imposed by the problem. In this paper, we address the above limitations and provide a highly efficient solution based on a novel adaptive index, named AP-Tree. The AP-Tree adaptively groups registered queries using keyword and spatial partitions, guided by a cost model. The AP-Tree also naturally indexes ordered keyword combinations. We present index construction algorithm that seamlessly and effectively integrates keyword and spatial partitions. Consequently, our method adapts well to the underlying spatial and keyword distributions of the data. Our extensive experiments demonstrate that AP-Tree achieves up to an order of magnitude improvement on efficiency compared with prior state-of-the-art methods

Analytical response time estimation in parallel relational database systems

Techniques for performance estimation in parallel database systems are well established for parameters such as throughput, bottlenecks and resource utilisation. However, response time estimation is a complex activity which is difficult to predict and has attracted research for a number of years. Simulation is one option for predicting response time but this is a costly process. Analytical modelling is a less expensive option but requires approximations and assumptions about the queueing networks built up in real parallel database machines which are often questionable and few of the papers on analytical approaches are backed by results from validation against real machines. This paper describes a new analytical approach for response time estimation that is based on a detailed study of different approaches and assumptions. The approach has been validated against two commercial parallel DBMSs running on actual parallel machines and is shown to produce acceptable accuracy