Query decomposition is a technique that involves breaking down a complex query into simpler subqueries and then combining the results to obtain the final answer. Query decomposition can be useful for several reasons, such as:
- Improving the efficiency and scalability of query processing by reducing the size and complexity of the query and enabling parallel execution of subqueries.
- Enhancing the effectiveness and accuracy of query answering by exploiting the structure and semantics of the query and the data sources.
- Facilitating the integration and interoperability of heterogeneous and distributed data sources by allowing queries to span multiple data models, schemas, languages, and formats.
In this article, we will introduce the main concepts and challenges of query decomposition, review some existing approaches and applications, and discuss some open research directions and opportunities.
Query decomposition can be performed at different levels of abstraction, depending on the type and structure of the query and the data sources. Some common levels are:
- Logical level: The query is decomposed into logical subqueries that preserve the meaning and semantics of the original query. For example, a conjunctive query can be decomposed into a set of subqueries that correspond to its conjuncts.
- Algebraic level: The query is decomposed into algebraic subqueries that correspond to the operators and operands of a query algebra, such as relational algebra or SPARQL algebra. For example, a join query can be decomposed into a set of subqueries that perform selections, projections, and joins on the input relations.
- Physical level: The query is decomposed into physical subqueries that correspond to the execution plans and operators of a query engine, such as hash joins, sort-merge joins, scans, filters, etc. For example, a join query can be decomposed into a set of subqueries that perform hash partitions, hash builds, hash probes, etc.
The level of decomposition depends on the goals and requirements of the query processing system. For example, logical decomposition can be useful for semantic optimization and reasoning, algebraic decomposition can be useful for cost-based optimization and rewriting, and physical decomposition can be useful for parallelization and distribution.
Query decomposition faces several challenges that need to be addressed by effective methods and techniques. Some of these challenges are:
- How to decompose a query into subqueries that are meaningful, coherent, consistent, and complete?
- How to ensure that the results of the subqueries can be combined to obtain the correct answer to the original query?
- How to optimize the decomposition process to minimize the number and complexity of subqueries and maximize their performance and quality?
- How to handle queries that involve multiple data sources with different data models, schemas, languages, formats, etc.?
- How to deal with queries that involve uncertainty, incompleteness, inconsistency, or ambiguity in the data or the query?
Several approaches have been proposed in the literature to address these challenges and apply query decomposition to various domains and scenarios. Some examples are:
- Query decomposition for relational databases: This involves decomposing SQL queries into simpler SQL subqueries that can be executed more efficiently by relational database systems. For example, [1] proposes a method for decomposing queries with nested subqueries into equivalent queries without nesting by using views and rewriting techniques. [2] proposes a method for decomposing queries with aggregation functions into equivalent queries without aggregation by using auxiliary relations and join operations.
- Query decomposition for graph databases: This involves decomposing graph queries into simpler graph subqueries that can be executed more effectively by graph database systems. For example, [3] proposes a method for decomposing graph pattern matching queries into subqueries that match smaller graph patterns by using graph partitioning and pruning techniques. [4] proposes a method for decomposing graph traversal queries into subqueries that traverse smaller subgraphs by using graph summarization and indexing techniques.
- Query decomposition for knowledge graphs: This involves decomposing knowledge graph queries into simpler knowledge graph subqueries that can be answered more accurately by knowledge graph systems. For example, [5] proposes a method for decomposing SPARQL queries into subqueries that can be answered by different knowledge sources by using ontology mapping and query rewriting techniques. [6] proposes a method for decomposing natural language questions into subquestions that can be answered by knowledge graphs by using semantic parsing and question answering techniques.
Query decomposition is an active research area that offers many opportunities for further exploration and innovation. Some possible directions for future
.jpg)
0 Comments