Query Cost Research Articles

Optimization approach for data allocation in multidisk database

In the last decade, many parallel process mechanisms have been developed in information systems for enhancing their performance. But I/O throughput rates are still the bottleneck for data processing in the systems. In particular, relational database systems encounter this performance problem dealing with expensive operations such as the join operation. To treat a class of two-way join problems in database, Rotem et al. proposed a linearization method for finding the optimal allocation of relations to multidisk database such that the expected query cost is minimized. For the multidisk allocation problem with N relations and M disks, their model needs MN+ N( N−1)/2+ MN( N−1)/2 0–1 variables. This paper proposes a concise method to reformulate the same problem, which requires only MN+ N( N−1)/2 0–1 variables. The problem can hence be more efficiently solved by the concise method. The analytical superiority of the concise method in terms of the number of iterations and execution times can be seen, through a computational experiment conducted on a set of generated test examples.

Limit laws for partial match queries in quadtrees

It is proved that in an idealized uniform probabilistic model the cost of a partial match query in a multidimensional quadtree after normalization converges in distribution. The limiting distribution is given as a fixed point of a random affine operator. Also a first-order asymptotic expansion for the variance of the cost is derived and results on exponential moments are given. The analysis is based on the contraction method.

Window Query Processing in Linear Quadtrees

The linear quadtree is a spatial access method that is built by decomposing the spatial objects in a database into quadtree blocks and storing these quadtree blocks in a B-tree. The linear quadtree is very useful for geographic information systems because it provides good query performance while using existing B-tree implementations. An algorithm and a cost model are presented for processing window queries in linear quadtrees. The algorithm can handle query windows of any shape in the general case of spatial databases with overlapping objects. The algorithm recursively decomposes the space into quadtree blocks, and uses the quadtree blocks overlapping the query window to search the B-tree. The cost model estimates the I/O cost of processing window queries using the algorithm. The cost model is also based on a recursive decomposition of the space, and it uses very simple parameters that can easily be maintained in the database catalog. Experiments with real and synthetic data sets verify the accuracy of the cost model.

Graph indexing for spatial data traversal in road map databases

This paper proposes a graph indexing technique for processing constrained spatial queries and discusses the application of such a technique to road map databases where the graph topology is relatively stationary. The fundamental idea of our technique is to augment the original graph with selected augmented links so that query processing cost, especially I/O cost, is minimized. Based on the computational results derived from the probabilistic analysis, we found that the proposed graph indexing technique is a promising approach for significantly reducing costs of spatial queries. Scope and purpose Spatial data is found in geographic information systems where data attributes are associated with nodes and links in directed graphs. Queries on spatial data are generally expensive because of the recursive nature of spatial data traversal. We propose a graph indexing technique to expedite queries on spatial data. The graph index is an instrument for early identification of the relevant nodes and links to the query so that repeated accesses to the same data pages can be eliminated. This paper presents the graph indexing technique in the context of road map databases and shows that the graph indexing technique can improve significantly on the efficiency of constrained queries on spatial data.

CLASSIFYING LOCAL QUERIES FOR GLOBAL QUERY OPTIMIZATION IN MULTIDATABASE SYSTEMS

A multidatabase system (MDBS) integrates information from multiple pre-existing local databases. A major challenge for global query optimization in an MDBS is that some required local information about local database systems such as local cost models may not be available at the global level due to local autonomy. A feasible method to tackle this challenge is to group local queries on a local database system into classes and then use the costs of sample queries from each query class to derive a cost formula for the class via regression analysis. This paper discusses the issues on how to classify local queries so that a good cost formula can be derived for each query class. Two classification approaches, i.e. bottom-up and top-down, are suggested. The relationship between these two approaches is discussed. Classification rules that can be used in the approaches are identified. Problems regarding composition and redundancy of classification rules are studied. Classification algorithms are given. To test the membership of a query in a class, an efficient algorithm based on ranks is introduced. In addition, a hybrid classification approach that combines the bottom-up and top-down ones is also suggested. Experimental results demonstrate that the suggested query classification techniques can be used to derive good local cost formulas for global query optimization in an MDBS.

Range queries in dynamic OLAP data cubes

A range query applies an aggregation operation (e.g., SUM) over all selected cells of an OLAP data cube where the selection is specified by providing ranges of values for numeric dimensions. Range sum queries on data cubes are a powerful analysis tool. Many application domains require that data cubes are updated often and the information provided by analysis tools are current or “near current”. Existing techniques for range sum queries on data cubes, however, can incur update costs in the order of the size of the data cube. Since the size of a data cube is exponential in the number of its dimensions, rebuilding the entire data cube can be very costly and is not realistic. To cope with this dynamic data cube problem, a new approach has been introduced recently, which achieves constant time per range sum query while constraining each update cost within O( n d/2 ), where d is the number of dimensions of the data cube and n is the number of distinct values of the domain at each dimension. In this paper, we provide a new algorithm for the problem which requires O( n 1/3) time for each range sum query and O( n d/3 ) time for each update. Our algorithm improves the update time by a factor of O( n d/6 ) in contrast to the current one for the problem O( n d/2 ). Like all existing techniques, our approach to answering range sum queries is also based on some precomputed auxiliary information (prefix sums) that is used to answer ad hoc queries at run time. Under both the product model and a new model introduced in this paper, the total cost for updates and range queries of the proposed algorithm is smallest compared with the cost by all known algorithms. Therefore our algorithm reduces the overall time complexity for range sum queries significantly.

Asymptotic distributions for partial match queries inK-d trees

The distributional performance of the cost of a partial match query is investigated in some sorts of K-d trees. The trees under consideration are Bentley's K-d tree, the locally balanced K-d-t tree, and the random relaxed K-d tree. For each of these trees it is proved that in the uniform probabilistic model the normalized cost of a partial match query converges in distribution. The limiting distributions occur as fixed points of random affine operators. Also explicit first-order asymptotic expansions for the variances of the costs and results on exponential moments are derived. The analysis is based on the contraction method. © 2000 John Wiley & Sons, Inc. Random Struct. Alg., 17: 403–427, 2000

INTERSECTION PROBLEMS ON SEGMENTS UNDER BOUNDARY UPDATES WITH APPLICATION TO PERSISTENT LISTS

We consider some intersection problems on segments of [Formula: see text] in the partially dynamic setting called boundary update, where updates occur at the boundary of a given region. In particular, we maintain a set S of line segments under (boundary) insertions and deletions, such that, given a line segment ℓ either of fixed slope or originating from a fixed point and given a point p∈S∩ℓ, we can efficiently and orderly report all segments intersecting ℓ; insertions/deletions of segments occur at the boundaries of a vertical infinite slab. We provide practical algorithms requiring [Formula: see text] space, [Formula: see text] time per update and [Formula: see text] time per query, where k is the number of reported segments. Our results allow both modeling a moving window over a larger data set and answering segment intersection queries at an extra query cost of [Formula: see text]; also, they provide a methodology for designing access methods to temporal databases as well as a new kind of partially persistent lists.

Approximating Minimum-Weight Triangulations in Three Dimensions

Let S be a set of noncrossing triangular obstacles in R 3 with convex hull H . A triangulation T of H is compatible with S if every triangle of S is the union of a subset of the faces of T. The weight of T is the sum of the areas of the triangles of T. We give a polynomial-time algorithm that computes a triangulation compatible with S whose weight is at most a constant times the weight of any compatible triangulation. One motivation for studying minimum-weight triangulations is a connection with ray shooting. A particularly simple way to answer a ray-shooting query (``Report the first obstacle hit by a query ray'') is to walk through a triangulation along the ray, stopping at the first obstacle. Under a reasonably natural distribution of query rays, the average cost of a ray-shooting query is proportional to triangulation weight. A similar connection exists for line-stabbing queries (``Report all obstacles hit by a query line'').

Cost-based optimization of decision support queries using transient-views

Next generation decision support applications, besides being capable of processing huge amounts of data, require the ability to integrate and reason over data from multiple, heterogeneous data sources. Often, these data sources differ in a variety of aspects such as their data models, the query languages they support, and their network protocols. Also, typically they are spread over a wide geographical area. The cost of processing decision support queries in such a setting is quite high. However, processing these queries often involves redundancies such as repeated access of same data source and multiple execution of similar processing sequences. Minimizing these redundancies would significantly reduce the query processing cost. In this paper, we (1) propose an architecture for processing complex decision support queries involving multiple, heterogeneous data sources; (2) introduce the notion of transient-views — materialized views that exist only in the context of execution of a query — that is useful for minimizing the redundancies involved in the execution of these queries; (3) develop a cost-based algorithm that takes a query plan as input and generates an optimal “covering plan”, by minimizing redundancies in the original plan; (4) validate our approach by means of an implementation of the algorithms and a detailed performance study based on TPC-D benchmark queries on a commercial database system; and finally, (5) compare and contrast our approach with work in related areas, in particular, the areas of answering queries using views and optimization using common sub-expressions. Our experiments demonstrate the practicality and usefulness of transient-views in significantly improving the performance of decision support queries.

Optimization techniques for queries with expensive methods

Object-relational database management systems allow knowledgeable users to define new data types as well as new methods (operators) for the types. This flexibility produces an attendant complexity, which must be handled in new ways for an object-relational database management system to be efficient. In this article we study techniques for optimizing queries that contain time-consuming methods. The focus of traditional query optimizers has been on the choice of join methods and orders; selections have been handled by “pushdown” rules. These rules apply selections in an arbitrary order before as many joins as possible, using th e assumption that selection takes no time. However, users of object-relational systems can embed complex methods in selections. Thus selections may take significant amounts of time, and the query optimization model must be enhanced. In this article we carefully define a query cost framework that incorporates both selectivity and cost estimates for selections. We develop an algorithm called Predicate Migration, and prove that it produces optimal plans for queries with expensive methods. We then describe our implementation of Predicate Migration in the commercial object-relational database management system Illustra, and discuss practical issues that affect our earlier assumptions. We compare Predicate Migration to a variety of simplier optimization techniques, and demonstrate that Predicate Migration is the best general solution to date. The alternative techniques we present may be useful for constrained workloads.

Solving Local Cost Estimation Problem for Global Query Optimization in Multidatabase Systems

To meet users‘ growing needs for accessing pre-existing heterogeneous databases, a multidatabase system (MDBS) integrating multiple databases has attracted many researchers recently. A key feature of an MDBS is local autonomy. For a query retrieving data from multiple databases, global query optimization should be performed to achieve good system performance. There are a number of new challenges for global query optimization in an MDBS. Among them, a major one is that some local optimization information, such as local cost parameters, may not be available at the global level because of local autonomy. It creates difficulties for finding a good decomposition of a global query during query optimization. To tackle this challenge, a new query sampling method is proposed in this paper. The idea is to group component queries into homogeneous classes, draw a sample of queries from each class, and use observed costs of sample queries to derive a cost formula for each class by multiple regression. The derived formulas can be used to estimate the cost of a query during query optimization. The relevant issues, such as query classification rules, sampling procedures, and cost model development and validation, are explored in this paper. To verify the feasibility of the method, experiments were conducted on three commercial database management systems supported in an MDBS. Experimental results demonstrate that the proposed method is quite promising in estimating local cost parameters in an MDBS.

Querying the World Wide Web

The World Wide Web is a large, heterogeneous, distributed collection of documents connected by hypertext links. The most common technology currently used for searching the Web depends on sending information retrieval requests to "index servers". One problem with this is that these queries cannot exploit the structure and topology of the document network. The authors propose a query language, WebSQL, that takes advantage of multiple index servers without requiring users to know about them, and that integrates textual retrieval with structure and topology-based queries. They give a formal semantics for WebSQL using a calculus based on a novel "virtual graph" model of a document network. They propose a new theory of query cost based on the idea of "query locality," that is, how much of the network must be visited to answer a particular query. Finally, they describe a prototype implementation of WebSQL written in Java.

A cost model for sort-domain traversal strategy in object-oriented databases

Some factors such as set-valued attributes, partial instantiation and clustering need to be considered to estimate the processing cost of an object-oriented query. In this paper, we propose a cost model which reflects the effect of such factors. The cost of a query is estimated using the number of objects participated in the instantiated paths and the number of tuples and the size of the reference relation which is an intermediate result of class traversais. The proposed cost model utilizes a small set of statistics maintained by the system, and estimates the costs more accurately.

Redundant MKH files design among multiple disks for concurrent partial match retrieval

It is known that the average query cost of a file in a multiple disk system can be dramatically reduced by storing redundant records of the file onto different disks. However, additional space is needed for the redundant records. In addition, the achievement of minimum redundance is difficult. Recently, Chen et al. (1994) showed that the problem of allocating all buckets of a multiple key hashing (MKH) file optimally among multiple disks for concurrent partial match retrieval can be reduced to a smaller sized subfile, called the remainder of the file. Based upon their reduction work, this article suggests two redundant storage schemes for MKH files in a multiple disk system. We point out that only modest copies of the remainder are necessary to be redundantly stored, in order to get perfectly optimal average query response time performance for an MKH file. We also emphasize that by using different clusterings for different copies, both the record organization cost and the bucket allocation cost can be simultaneously reduced, and thus, the cost can be dramatically reduced.

Building knowledge base management systems

Advanced applications in fields such as CAD, software engineering, real-time process control, corporate repositories and digital libraries require the construction, efficient access and management of large, shared knowledge bases. Such knowledge bases cannot be built using existing tools such as expert system shells, because these do not scale up, nor can they be built in terms of existing database technology, because such technology does not support the rich representational structure and inference mechanisms required for knowledge-based systems. This paper proposes a generic architecture for a knowledge base management system intended for such applications. The architecture assumes an object-oriented knowledge representation language with an assertional sublanguage used to express constraints and rules. It also provides for general-purpose deductive inference and special-purpose temporal reasoning. Results reported in the paper address several knowledge base management issues. For storage management, a new method is proposed for generating a logical schema for a given knowledge base. Query processing algorithms are offered for semantic and physical query optimization, along with an enhanced cost model for query cost estimation. On concurrency control, the paper describes a novel concurrency control policy which takes advantage of knowledge base structure and is shown to outperform two-phase locking for highly structured knowledge bases and update-intensive transactions. Finally, algorithms for compilation and efficient processing of constraints and rules during knowledge base operations are described. The paper describes original results, including novel data structures and algorithms, as well as preliminary performance evaluation data. Based on these results, we conclude that knowledge base management systems which can accommodate large knowledge bases are feasible.

Methodology for index configurations in object-oriented databases

In this paper, we explore the problem of the optimal index configuration in object-oriented database systems. The problem is to find a set of nested attribute indexes such that they are used to minimize the overall cost of processing retrieval queries and maintaining the indexes for update queries in a given workload. We also take into account a space limit for storing the indexes. The primary idea of the proposed methodology is to reduce the problem to that of selecting indexes in such a way that the sum of cost savings is maximized subject to a given storage capacity. The correctness of our approach is proved using analytic cost formulas. We characterize the computational difficulty of the problem, which is shown to be NP-hard. We propose an effective approximation algorithm. The algorithm is compared with an optimal searching algorithm. Experiment results show the approximation algorithm performs well for various input situations.

Combinatorial optimization of distributed queries

In relational distributed databases a query cost consists of a local cost and a transmission cost. Query optimization is a combinatorial optimization problem. As the query size grows, the optimization methods based on exhaustive search become too expensive. We propose the following strategy for solving large distributed query optimization problems in relational database systems: (1) represent each query-processing schedule by a labeled directed graph; (2) reduce the number of different schedules by pruning away invalid or high-cost solutions; and (3) find a suboptimal schedule by combinatorial optimization. We investigate several combinatorial optimization techniques: random search, single start, multistart, simulated annealing, and a combination of random search and local simulated annealing. The utility of combinatorial optimization is demonstrated in the problem of finding the (sub)optimal semijoin schedule that fully reduces all relations of a tree query. The combination of random search and local simulated annealing was superior to other tested methods.

Multidimensional digital searching—alternative data structures

AbstractWe analyze the average cost of partial match queries in M‐dimensional Digital Search Trees and Patricia Tries. Our results allow a precise comparison of the average behavior of these data structures with the M‐dimensional Tries studied by Flajolet and Puech [J. Assoc. Comput. Mach., 33, 371–407 (1986)]. It turns out that Patricia is superior to Digital Search Tres, the latter ones being superior to Tries. © 1994 John Wiley & Sons, Inc.

Making the knowledge base systems more efficient: a method to detect inconsistent queries

The processing cost of queries with an empty answer, both in the database and knowledge base context, is usually high. One purpose of semantic query optimization methods in the database context is to use semantic knowledge to detect such types of queries. Although semantic query optimization is well known in the database context, this is not the case for knowledge base systems (KBSs). This paper presents a method that allows the detection of queries with an empty answer using only semantic information expressed in the knowledge base definition. The method can be applied in the context of KBSs that provide some of the following features: structuring mechanisms, assertional knowledge, temporal information, and handling of inequality expressions. >