Semantic Query Optimization Research Articles

Handling redundant processing in OBDA query execution over relational sources

Redundant processing is a key problem in the translation of initial queries posed over an ontology into SQL queries, through mappings, as it is performed by ontology-based data access systems. Examples of such processing are duplicate answers obtained during query evaluation, which must finally be discarded, or common expressions evaluated multiple times from different parts of the same complex query. Many optimizations that aim to minimize this problem have been proposed and implemented, mostly based on semantic query optimization techniques, by exploiting ontological axioms and constraints defined in the database schema. However, data operations that introduce redundant processing are still generated in many practical settings, and this is a factor that impacts query execution. In this work we propose a cost-based method for query translation, which starts from an initial result and uses information about redundant processing in order to come up with an equivalent, more efficient translation. The method operates in a number of steps, by relying on certain heuristics indicating that we obtain a more efficient query in each step. Through experimental evaluation using the Ontop system for ontology-based data access, we exhibit the benefits of our method.

Handling Redundant Processing in OBDA Query Execution Over Relational Sources

Abstract Redundant processing is a key problem in the translation of initial queries posed over an ontology into SQL queries, through mappings, as it is performed by ontology-based data access systems. Examples of such processing are duplicate answers obtained during query evaluation, which must finally be discarded, or common expressions evaluated multiple times from different parts of the same complex query. Many optimizations that aim to minimize this problem have been proposed and implemented, mostly based on semantic query optimization techniques, by exploiting ontological axioms and constraints defined in the database schema. However, data operations that introduce redundant processing are still generated in many practical settings, and this is a factor that impacts query execution. In this work we propose a cost-based method for query translation, which starts from an initial result and uses information about redundant processing in order to come up with an equivalent, more efficient translation. The method operates in a number of steps, by relying on certain heuristics indicating that we obtain a more efficient query in each step. Through experimental evaluation using the Ontop system for ontology-based data access, we exhibit the benefits of our method.

Query rewriting for semantic query optimization in spatial databases

Query processing is an important challenge for spatial databases due to the use of complex data types that represent spatial attributes. In particular, due to the cost of spatial joins, several optimization algorithms based on indexing structures exist. The work in this paper proposes a strategy for semantic query optimization of spatial join queries. The strategy detects queries with empty results and rewrites queries to eliminate unnecessary spatial joins or to replace spatial by thematic joins. This is done automatically by analyzing the semantics imposed by the database schema through topological dependencies and topological referential integrity constraints. In this way, the strategy comes to complement current state-of-art algorithms for processing spatial join queries. The experimental evaluation with real data sets shows that the optimization strategy can achieve a decrease in the time cost of a join query using indexing structures in a spatial database management system (SDBMS).

Дослідження методів і технологій інтеграції онтологічної моделі з реляційними даними

A study of methods and technological solutions on Data Mapping concerning the integration of an ontological and relational data models has been carried out.The main objective of the investigation is to accelerate and reduce the cost of constructing some ontological models for systems that process distributed data in a heterogeneous environment.At present, the theoretical basis for the integration of various data models (Data Mapping) is a di-rection that has been defined as Ontology-based data integration. The theoretical and practical develop-ment of this area is the Ontology-Based Data Access (OBDA) approach, which integrates ontological models presented in the form of RDF graphs with relational data.The methodology of Data Mapping application for distributed data processing systems has been developed. As an example the process of data models consolidation for Ukrainian State Budget monitor-ing system is given. The database of Ukrainian State Budget monitoring system consists of many rela-tional tables, which contain reports of the State Treasury on the implementation of revenue and expendi-ture parts of both state and municipal budgets, as well as the regional section. In addition, the database contains data on lending and arrears of budget institutions.The ontology model connects to data sources through a declarative specification provided in terms of display, including classes and their properties.The given application converts a SPARQL queries into a SQL query to the relational database. The generated SQL query may be execute by the Oracle 11g database driver, which returns the result as a data snapshot. Then, to improve the performance of SPARQL queries, it should be used the semantic query optimization method.Indicative application of the methodology on the example of the construction of an ontological model of the system of monitoring the state budget of Ukraine in Protege 5. It has been demonstrated the results of the execution of the SPARQL-queries to the relational data of the budget process under the Oracle 11g Database. It has been shown the directions semantic optimization for SPARQL-queries, which allow improving obtained data quality.The proposed methodology allows: to integrate data presented by different models — ontological and relational knowledge acquisition; to overcome constraints when databases based on outdated data models are being merged with modern ontological-oriented systems; to eliminate data redundancy in designing knowledge-based systems.

Mind your dependencies for semantic query optimization

Semantic query optimization uses dependencies between attributes to formulate query transformations and revise the number of processed rows, with direct impact on performance. Commercial databases present facilities to define dependencies as not enforced constraints. The goal is to help the query optimizer in cases where the database is denormalized or simply lost dependencies in the design. However, feeding these facilities is a manual task which is tedious and error-prone. An attractive alternative is the automatic discovery of dependencies, but the cost of finding dependencies increases with the number of rows and attributes in the dataset. In this paper, we stick to the automatic discovery approach, but to reduce the cost we focus on dependencies matching the current queries in the pipe (ie., workload). Initially, we rely on a large set of functional dependencies computed in batch with state of the art algorithms in the literature. Over time our focused dependency selector (FDSel) chooses exemplars to feed the query optimizer. Therewith we eliminate further manual interactions. The automatically selected exemplars exhibit statistical properties that resemble those of the initial dependency set. This demonstrates the effectiveness of our proposed approach. In the best case scenario, by applying the FDSel for join elimination on a real-world database, we reduce query response time by more than one order of magnitude.

Semantic Optimization in Tractable Classes of Conjunctive Queries

This paper reports on recent advances in semantic query optimization. We focus on the core class of conjunctive queries (CQs). Since CQ evaluation is NP-complete, a long line of research has concentrated on identifying fragments of CQs that can be efficiently evaluated. One of the most general such restrictions corresponds to bounded generalized hypertreewidth, which extends the notion of acyclicity. Here we discuss the problem of reformulating a CQ into one of bounded generalized hypertreewidth. Furthermore, we study whether knowing that such a reformulation exists alleviates the cost of CQ evaluation. In case a CQ cannot be reformulated as one of bounded generalized hypertreewidth, we discuss how it can be approximated in an optimal way. All the above issues are examined both for the constraint-free case, and the case where constraints, in fact, tuple-generating and equality-generating dependencies, are present

A Survey on Accessing Dataspaces

Dataspaces provide a co-existence approach for heterogeneous data. Relationships among these heterogeneous data are often incrementally identified, such as object associations or attribute synonyms. With the different degree of relationships recognized, various query answers may be obtained. In this paper, we review the major techniques for processing and optimizing queries in dataspaces, according to their different abilities of handling relationships, including 1) simple search query without considering relationships, 2) association query over object associations, 3) heterogeneity query with attribute correspondences, and 4) similarity query for similar objects. Techniques such as indexing, query rewriting, expansion, and semantic query optimization are discussed for these query types. Finally, we highlight possible directions in accessing dataspaces.

SQOPI: Semantic Query Optimization Framework

query optimization uses semantic knowledge in databases to rewrite queries and logic programs for the purpose of more efficient query evaluation. There has been a large body of work in the area of semantic query optimization. But, unfortunately, till now no commercial application of sematic query optimization techniques has received wide attention. In this paper, we address this problem by developing a unified framework (Application Programming Interface) called SQOPI that could be used by any application developer to semantically optimize queries executed against relational database regardless of DBMS type used. Our results show that SQOPI improves both time and I/O efficiency.

The backchase revisited

Semantic query optimization is the process of finding equivalent rewritings of an input query given constraints that hold in a database instance. In this paper, we report about a Chase & Backchase (C&B) algorithm strategy that generalizes and improves on well-known methods in the field. The implementation of our approach, the Pegasussystem, outperforms existing C&B systems an average by two orders of magnitude. This gain in performance is due to a combination of novel methods that lower the complexity in practical situations significantly.

Rule Evaluation Algorithm for Semantic Query Optimisation

Semantic Query Optimisation (SQO) in Relational Database Management Systems (RDMSs) is a query optimisation ap- proach which uses rules learned from past queries in order to execute new queries more intelligently without accessing database, whenever possible. The approach is composed of several components: Query Representation, Query Optimisation, Automatic Rule Derivation and Rule Maintenance. This paper focused on the query optimisation component. In RDMSs, during the traditional SQO, different alternative queries of a given query can be constructed us ing matching rule(s) from the rule set, and then its optimiser selects one of the alternatives as an optimum query which will give the same answer set but it can be executed faster than the original query. One of the main problems occurs during this process is to have many matched rules e.g., if the number of the rules is N , the number of the alternative queries is 2 N 1. The construction and the optimisation of these alternatives also take time in ad dition to the execution of the query. In order to overcome this problem, in this paper we propose a new Rule Evaluation Algorithm. The main goal of the algo- rithm is to evaluate matching rule(s) and select useful/promising rules. And then use selected rules to construct an optimum query. The algorithm can answer the question of the utility of rules in the query optimisation. The system of the approach based on the algorithm has been implemented and its computational results are given. The experimental results show that the algorithm can trim the number of the rules significantly.

Semantic query optimization in the presence of types

Both semantic and type-based query optimization rely on the idea that queries may exhibit non-trivial rewritings if the state space of the database is restricted. While these two problems have always been studied as separate problems in previous work, in this paper we present a unifying, logic-based query optimization framework that builds upon the classical chase algorithm and brings both problems together. As a major challenge, our novel setting requires chasing conjunctive queries with union and negation in the presence of dependencies containing negation and disjunction. Tackling this problem, we study the applicability of the chase in this setting, develop novel conditions that guarantee its termination, identify fragments for which minimal query computation (w.r.t. a generic cost function) is always possible, and investigate the complexity of related decision problems.

Implementing Semantic Query Optimization in Relational Databases

query optimization is applied to relational databases using the inductive learning approach. This approach generates an alternate query using the learning framework and the algorithm. The alternate query should be semantically equivalent to original query. The semantically equivalent query generated should be less expensive than the original query. These can be implemented in SQL using the SQL hints. These hints allow user to implement the desired plan for the query. Keywordsquery; semantic query optimization; SQL hints; inductive learning; algorithm for learning; learning framework

Comparable dependencies over heterogeneous data

To study the data dependencies over heterogeneous data in dataspaces, we define a general dependency form, namely comparable dependencies (CDS), which specifies constraints on comparable attributes. It covers the semantics of a broad class of dependencies in databases, including functional dependencies (FDS), metric functional dependencies (MFDS), and matching dependencies (MDS). As we illustrated, comparable dependencies are useful in real practice of dataspaces, such as semantic query optimization. Due to heterogeneous data in dataspaces, the first question, known as the validation problem, is to tell whether a dependency (almost) holds in a data instance. Unfortunately, as we proved, the validation problem with certain error or confidence guarantee is generally hard. In fact, the confidence validation problem is also NP-hard to approximate to within any constant factor. Nevertheless, we develop several approaches for efficient approximation computation, such as greedy and randomized approaches with an approximation bound on the maximum number of violations that an object may introduce. Finally, through an extensive experimental evaluation on real data, we verify the superiority of our methods.

UB-Tree Indexing for Semantic Query Optimization of Range Queries

UB-Tree Indexing for Semantic Query Optimization of Range Queries

A dynamic self-learning method for semantic query optimisation

Semantic Query Optimisation (SQO) uses rules learned from past queries in order to execute new queries more intelligently without accessing a database, whenever possible. It has several components: Query Representation, Query Optimisation, Automatic Rule Derivation and Rule Maintenance. Automatic Rule Derivation is the main focus in this paper. A dynamic statistical learning method takes the answer set of a query, and divides it into two groups: dependent and independent attributes. Then it tests values of these attributes on whether these attributes are related or not. If they are, the method can derive new rules. Elimination is done according to the averaged rank of coefficients of linear multiple regression analysis. The method is efficient, fast and completely dynamic. It can be done on any database at any time, without any need for reconstruction on the components. Computational results of the method prove that it limits the number of rules easily and reduces the derivation time.

An XML document generator for semantic query optimization experimentation

PurposeXML semantic query optimization (XSQO) is an important area in eXtensible Markup Language (XML) query processing. However, the experiments evaluating semantic optimization methods often suffer because of the lack of suitable data sets. To evaluate XSQO methods it is necessary to be able to build datasets with specific characteristics. In particular, it is necessary to be able to set: selectivity of embedded elements, selectivity of values of elements, depth, fan‐out and size. The aim of this paper is to describe the requirements of such a generator, and the challenges of building the generator.Design/methodology/approachThe paper considers that there is currently no generator that gives this flexibility, so the paper discusses the design and building of such a generator.FindingsThe main characteristic of the generator is that it is possible to adapt existing XML documents, including XML benchmarks, for experiments that evaluate XSQO methods. With the generator, users are able to modify not only the structure of XML documents but also content quickly and directly.Originality/valueThe paper provides information of value to information technology professionals.

A semantic approach to optimize linear datalog programs

In this work, we present a semantic query optimization approach to improve the efficiency of the evaluation of a subset of SQL:1999 recursive queries. Using datalog notation, we can state our main contribution as an algorithm that builds a program P′ equivalent to a given program P, when both are applied over a database d satisfying a set of functional dependencies. The input program P is a linear recursive datalog program. The new program P′ has less different variables and, sometimes, less atoms in rules, thus it is cheaper to evaluate. Using coral and ibm db2, P′ is empirically shown to be more efficient than the original program.

Semantic Query Optimization: Correctness and Control

We developed a semantic query optimization framework for deductive databases based on equivalent transformation (ET) rules. ET rules, prepared from the semantic knowledge of databases, such as integrity constraints, transform given queries into syntactically different but semantically equivalent and more efficient forms. We formally prove the correctness of query transformations by ET rules. For efficiency, we propose a two-phase heuristic-based strategy to guide query transformations and introduce a condition-based control strategy to prevent unwanted, unnecessary transformations. We give examples demonstrating the possible optimization.

Using rule mining modules combined with automatic rule derivation on semantic query optimisation

Semantic Query Optimisation (SQO) is a considerably new approach to query optimisation, compared to the approaches used by the commercial databases. It takes the original query into its optimiser and analyses it by the use of automatically derived rules. From the answer and condition(s) of this query, the new rule(s) may be learned. The approach yields considerable time savings on query optimisation, especially when the query answer can be found from the rules. A main concern is that automatic rule derivation requires a long time in the SQO approach because of the database connection and its retrievals. To solve this problem, we apply mathematic logic to determine where a transition (from one rule to another) exists, and from here produce new rules. In this paper, two rule-mining modules useful in the SQO approach are Rule Transition and 'If and only If'. Computational results of the modules are very promising for learning new rules, and these rules improve the query executions.

SDQE: towards automatic semantic query optimization in P2P systems

Conventional information retrieval technology (i.e. VSM) faces many difficulties when being implemented in complex P2P systems for the lack of global statistic information (e.g. IDF) and central services. In this paper, we suggest a novel query optimization scheme (Semantic Dual Query Expansion, SDQE) that makes full use of the context information supplied by the local document collection. Latent Semantic Indexing (LSI) is used to explore the local context information. By comparing the different local context information hidden in different document collections, it is possible to solve the synonymy-polysemy problem in VSM. The experiments prove that our scheme is effective to improve the retrieval performance in P2P systems without knowing the global statistic information.