Prototype Validation of the Rectangular Attribute Cardinality Map for Query Optimization in Database Systems

Since the very beginning of query processing in database systems, cost-based query optimization has been the essential strategy for effectively answering complex queries on large documents. XML documents can be efficiently stored and processed using native XML database management systems. Even though such systems can choose from a huge repertoire of join operators (e. g., Structural Joins and Holistic Twig Joins) and various index access operators to efficiently evaluate queries on XML documents, the development of full-fledged XML query optimizers is still in its infancy. Especially the evaluation of complex XQuery expressions using these operators is not well understood and needs further research. The extensible, rule-based, and cost-based XML query optimization framework proposed in this chapter, serves as a testbed for exploring how and whether well-known concepts from relational query optimization (e. g., join reordering) can be reused and which new techniques can make a significant contribution to speed-up query execution. Using the best practices and an appropriate cost model that will be developed using this framework, it can be turned into a robust cost-based XML query optimizer in the future.

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Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2019100103 ◽

2019 ◽

Vol 14 (4) ◽

pp. 50-63 ◽

Cited By ~ 1

Author(s):

Deepak Kumar ◽

Deepti Mehrotra ◽

Rohit Bansal

Keyword(s):

Query Optimization ◽

Query Language ◽

Database Systems ◽

Amazon Mechanical Turk ◽

Optimization Approach ◽

Crowd Sourcing ◽

Market Place ◽

Multi Objective ◽

Ant Lion Optimizer ◽

Ant Lion

Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.

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CLASSIFYING LOCAL QUERIES FOR GLOBAL QUERY OPTIMIZATION IN MULTIDATABASE SYSTEMS

International Journal of Cooperative Information Systems ◽

10.1142/s0218843000000156 ◽

2000 ◽

Vol 09 (03) ◽

pp. 315-355 ◽

Cited By ~ 2

Author(s):

QIANG ZHU ◽

P.-Å. LARSON

Keyword(s):

Query Optimization ◽

Database Systems ◽

Cost Models ◽

Classification Rules ◽

Top Down ◽

Bottom Up ◽

Multidatabase System ◽

Local Database ◽

Hybrid Classification ◽

Global Query

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.

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