Query Evaluation Cost Research Articles

Utility-Aware and Privacy-Preserving Mobile Query Services

Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users' locations to untrusted LBS providers may lead to privacy concerns. This paper proposes StarCloak, a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. StarCloak combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, StarCloak uses the concept of stars and proposes cloaking graphs for effective location cloaking on road networks. Second, StarCloak supports user-specified k-user anonymity and <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$l$</tex></formula> -segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, StarCloak achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two datasets using real-world road networks, under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar, its most relevant competitor.

Multi-Objective Big Data View Materialization Using NSGA-III

Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..

Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to process big data efficiently, big data is processed using newer distributed storage and processing frameworks. Big data view materialization is a technique to process big data queries efficiently on these distributed frameworks. It generates valuable information, which can be used to take timely decisions, especially in cases of disasters. As there are a very large number of big data views, it is not possible to materialize all of them. Therefore, a subset of big data views needs to be selected for materialization, which optimizes the query response time for a given set of workload queries with minimum overheads. This big data view materialization problem, having objectives minimization of the query evaluation cost of a set of workload queries, while simultaneously minimizing the update processing costs of the materialized views, has been addressed using improved strength pareto evolutionary algorithm (SPEA-2) in this paper. The proposed big data view selection algorithm, which is able to compute a set of diverse non-dominated big data views, is shown to perform better that existing big data view selection algorithms..

Optimizing Multi-Query Evaluation in Federated RDF Systems

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

A Multi-Objective Approach to Big Data View Materialization

Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.

Multi-Objective Big Data View Materialization Using NSGA-II

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

Semantic Optimization of Conjunctive Queries

This work deals with the problem of semantic optimization of the central class of conjunctive queries (CQs). Since CQ evaluation is NP-complete, a long line of research has focussed on identifying fragments of CQs that can be efficiently evaluated. One of the most general restrictions corresponds to generalized hypetreewidth bounded by a fixed constant k ≥ 1; the associated fragment is denoted GHW k . A CQ is semantically in GHW k if it is equivalent to a CQ in GHW k . The problem of checking whether a CQ is semantically in GHW k has been studied in the constraint-free case, and it has been shown to be NP-complete. However, in case the database is subject to constraints such as tuple-generating dependencies (TGDs) that can express, e.g., inclusion dependencies, or equality-generating dependencies (EGDs) that capture, e.g., key dependencies, a CQ may turn out to be semantically in GHW k under the constraints, while not being semantically in GHW k without the constraints. This opens avenues to new query optimization techniques. In this article, we initiate and develop the theory of semantic optimization of CQs under constraints. More precisely, we study the following natural problem: Given a CQ and a set of constraints, is the query semantically in GHW k , for a fixed k ≥ 1, under the constraints, or, in other words, is the query equivalent to one that belongs to GHW k over all those databases that satisfy the constraints? We show that, contrary to what one might expect, decidability of CQ containment is a necessary but not a sufficient condition for the decidability of the problem in question. In particular, we show that checking whether a CQ is semantically in GHW 1 is undecidable in the presence of full TGDs (i.e., Datalog rules) or EGDs. In view of the above negative results, we focus on the main classes of TGDs for which CQ containment is decidable and that do not capture the class of full TGDs, i.e., guarded, non-recursive, and sticky sets of TGDs, and show that the problem in question is decidable, while its complexity coincides with the complexity of CQ containment. We also consider key dependencies over unary and binary relations, and we show that the problem in question is decidable in elementary time. Furthermore, we investigate whether being semantically in GHW k alleviates the cost of query evaluation. Finally, in case a CQ is not semantically in GHW k , we discuss how it can be approximated via a CQ that falls in GHW k in an optimal way. Such approximations might help finding “quick” answers to the input query when exact evaluation is intractable.

From Relation Algebra to Semi-join Algebra: An Approach to Graph Query Optimization

Abstract Many graph query languages rely on composition to navigate graphs and select nodes of interest, even though evaluating compositions of relations can be costly. Often, this need for composition can be reduced by rewriting toward queries using semi-joins instead, resulting in a significant reduction of the query evaluation cost. We study techniques to recognize and apply such rewritings. Concretely, we study the relationship between the expressive power of the relation algebras, which heavily rely on composition, and the semi-join algebras, which replace composition in favor of semi-joins. Our main result is that each fragment of the relation algebras where intersection and/or difference is only used on edges (and not on complex compositions) is expressively equivalent to a fragment of the semi-join algebras. This expressive equivalence holds for node queries evaluating to sets of nodes. For practical relevance, we exhibit constructive rules for rewriting relation algebra queries to semi-join algebra queries and prove that they lead to only a well-bounded increase in the number of steps needed to evaluate the rewritten queries. In addition, on sibling-ordered trees, we establish new relationships among the expressive power of Regular XPath, Conditional XPath, FO-logic and the semi-join algebra augmented with restricted fixpoint operators.

Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promising.

Template-Based Bitmap View Selection for Optimizing Queries Over Tree Data

Developing and exploiting flexible techniques for optimizing the evaluation of queries over loosely structured data (e.g. tree or graph databases) is of crucial importance for modern database applications. In this context, we consider a new type of views which can be materialized as compressed bitmaps over tree data. We introduce the concept of view structural template to define classes of views. We then define and address a novel view selection problem (called view class selection (VCS) problem) where the goal is to select classes of bitmap views in order to optimize the overall evaluation cost of all tree pattern queries (TPQs) that can be issued against a database while satisfying a space constraint and ensuring that all the TPQs can be answered using exclusively the materialized views. We show that the VCS problem is NP-hard and we design two heuristic greedy algorithms which iteratively generate new batches of candidate view classes and make them available for selection. Each algorithm uses a different view class expansion technique to enable the systematic generation of candidate view classes from classes with smaller templates. We run extensive experiments to evaluate both the effectiveness of the algorithms and their efficiency on real, benchmark and synthetic datasets. Our algorithms are able to suggest high quality selections of view classes in a reasonable amount of time.

Bounded conjunctive queries

A query Q is said to be effectively bounded if for all datasets D , there exists a subset D Q of D such that Q ( D ) = Q ( D Q ), and the size of DQ and time for fetching D Q are independent of the size of D . The need for studying such queries is evident, since it allows us to compute Q ( D ) by accessing a bounded dataset D Q , regardless of how big D is. This paper investigates effectively bounded conjunctive queries (SPC) under an access schema A , which specifies indices and cardinality constraints commonly used. We provide characterizations (sufficient and necessary conditions) for determining whether an SPC query Q is effectively bounded under A . We study several problems for deciding whether Q is bounded, and if not, for identifying a minimum set of parameters of Q to instantiate and make Q bounded. We show that these problems range from quadratic-time to NP-complete, and develop efficient (heuristic) algorithms for them. We also provide an algorithm that, given an effectively bounded SPC query Q and an access schema A , generates a query plan for evaluating Q by accessing a bounded amount of data in any (possibly big) dataset. We experimentally verify that our algorithms substantially reduce the cost of query evaluation.

Configuring bitmap materialized views for optimizing XML queries

In recent years the inverted lists evaluation model along with holistic stack-based algorithms have been established as the most prominent techniques for evaluating XML queries on large persistent XML data. In this framework, we are using materialized views for optimizing XML queries. We consider a novel approach which instead of materializing the answer of a view materializes exactly the inverted sublists that are necessary for computing the answer of the view. This originality allows storing view materializations as compressed bitmaps, a solution that minimizes the materialization space and empowers performing optimization operations as CPU-efficient bitwise operations. To realize the potential of bitmap materialized views in optimizing query performance, we define and address the following problem (view configuration problem): given an XML tree and its schema find a template of tree-pattern views (view configuration) such that: (a) the views of this configuration can answer all the queries that can be issued against the schema, (b) their materialization fits in the space provided, and (c) evaluating the queries using these views minimizes the overall query evaluation cost. We consider an instance of this problem for tree pattern queries. Our intension is to find view configurations whose materializations are small enough to be stored in main memory. We find two candidate solution configurations and we identify cases where views can be excluded from materialization in a configuration without affecting query performance. In order to compare our approach with an approach which also can support the optimization of every query on the schema, we implemented an improvement of a state-of-the-art approach which is based on structural indexes. Our experimental results show that our approach is stable, greatly improves evaluating queries without materialized views, outperforms the structural index approach on all test cases and is very close to the optimal. These results characterize our approach as the best candidate for supporting the optimization of queries in the framework of the inverted lists model.

A graph-theoretic approach to optimize keyword queries in relational databases

Keyword search can provide users an easy method to query large and complex databases without any knowledge of structured query languages or underlying database schema. Most of the existing studies have focused on generating candidate structured queries relevant to keywords. Due to the large size of generated queries, the execution costs may be prohibitive. However, existing studies lack the idea of a generalized method to optimize the plan of the large set of generated queries. In this paper, we introduce a graph-theoretic optimization approach. We propose a general graph model, Weighted Operator Graph, to address the costs of keyword query evaluation plans. The proposed model is flexible to integrate all of the cost-based plans in a uniform way. We define a Keyword Query Optimization Problem based on a theoretical cost model as a graph-theoretic problem and show it to be a NP-hard problem. We propose a greedy heuristic Maximum Propagation that reduces the size of the intermediate result as early as possible. The proposed algorithm allows us to achieve efficiency in terms of query evaluation costs. The experimental studies on both synthetic and real data set results show that our work outperforms the existing work.

Automated discovery of multi-faceted ontologies for accurate query answering and future semantic reasoning

There has been a surge of interest in the development of probabilistic techniques to discover meaningful data facts across multiple datasets provided by different organizations. The key aim is to approximate the structure and content of the induced data into a concise synopsis in order to extract meaningful data facts. Performing sensible queries across unrelated datasets is a complex task that requires a complete understanding of each contributing database's schema to define the structure of its information. Alternative approaches that use data modeling enterprise tools have been proposed, in order to give users without complex schema knowledge the ability to query databases. Unfortunately, data modeling-based matching is a content-based technique and incurs significant query evaluation costs, due to attribute level pairwise comparisons. We propose a multi-faceted classification technique for performing structural analysis on knowledge domain clusters, using a novel Ontology Guided Data Linkage (OGDL) framework. This framework supports self-organization of contributing databases through the discovery of structural dependencies, by performing multi-level exploitation of ontological domain knowledge relating to tables, attributes and tuples. The framework thus automates the discovery of schema structures across unrelated databases, based on the use of direct and weighted correlations between different ontological concepts, using a h-gram (hash gram) record matching technique for concept clustering and cluster mapping. We demonstrate the feasibility of our OGDL algorithms through a set of accuracy, performance and scalability experimental tests run on real-world datasets, and show that our system runs in polynomial time and performs well in practice. To the best of our knowledge, this is the first attempt initiated to solve data linkage problems using a multi-faceted cluster mapping strategy, and we believe that our approach presents a significant advancement towards accurate query answering and future real-time online semantic reasoning capacity.

Query optimization using restructured views: Theory and experiments

We study optimization of relational queries using materialized views, where views may be regular or restructured. In a restructured view, some data from the base table(s) are represented as metadata—that is, schema information, such as table and attribute names—or vice versa. Using restructured views in query optimization opens up a new spectrum of views that were not previously available, and can result in significant additional savings in query-evaluation costs. These savings can be obtained due to a significantly larger set of views to choose from, and may involve reduced table sizes, elimination of self-joins, clustering produced by restructuring, and horizontal partitioning. In this paper we propose a general query-optimization framework that treats regular and restructured views in a uniform manner and is applicable to SQL select-project-join queries and views without or with aggregation. Within the framework we provide (1) algorithms to determine when a view (regular or restructured) is usable in answering a query and (2) algorithms to rewrite queries using usable views. Semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. Within our general query-optimization framework, we develop techniques for determining the key of a (regular or restructured) view, and show how this information can be used to further optimize a rewritten query. It is straightforward to integrate all our algorithms and techniques into standard query-optimization algorithms. Our extensive experimental results illustrate how using restructured views (in addition to regular views) in query optimization can result in a significant reduction in query-processing costs compared to a system that uses only regular views.

TwigStack+: Holistic twig join pruning using extended solution extension

XML has been used extensively in many applications as a de facto standard for information representation and exchange over internet. Huge volumes of data are organized or exported in tree-structured form and the desired information can be got by traversing the whole tree structure using a twig pattern query. A new definition, Extended Solution Extension, is proposed in this paper to check the usefulness of an element from both forward and backward directions. Then a novel Extended Solution Extension based algorithm, TwigStack+, is also proposed to reduce the query processing cost, simply because it can check whether other elements can be processed together with the current one. Compared with existing methods, query evaluation cost can be largely reduced. The experimental results on various datasets indicate that the proposed algorithm performs significantly better than the existing ones.

Weighted hypertree decompositions and optimal query plans

Hypertree width is a measure of the degree of cyclicity of hypergraphs. A number of relevant problems from different areas, e.g., the evaluation of conjunctive queries in database theory or the constraint satisfaction in AI, are tractable when their underlying hypergraphs have bounded hypertree width. However, in practical contexts like the evaluation of database queries, we have more information besides the structure of queries. For instance, we know the number of tuples in relations, the selectivity of attributes and so on. In fact, all commercial query-optimizers are based on quantitative methods and do not care on structural properties. In this paper, in order to combine structural decomposition methods with quantitative approaches, the notion of weighted hypertree decomposition is defined. Weighted hypertree decompositions are equipped with cost functions, that can be used for modeling many situations where there is further information on the given problem, besides its hypergraph representation. The complexity of computing hypertree decompositions having the smallest weights, called minimal hypertree decompositions, is analyzed. It is shown that in many cases tractability is lost if weights are added. However, it is proven that, under some—not very severe—restrictions on the allowed cost functions and on the target hypertrees, optimal weighted hypertree decompositions can be computed in polynomial time. For some easier hypertree weighting functions, this problem is also highly parallelizable. Then, a cost function modeling query evaluation costs is provided, and it is shown how to exploit weighted hypertree decompositions for determining (logical) query plans for answering conjunctive queries. Finally, some preliminary results of an experimental comparison of this query optimization technique with the query optimizer of a commercial DBMS are presented.

An Adaptive Materialization Method for Globally Optimized Query Evaluation in a Hierarchical Mediator System

Semantically related mediators form a hierarchical structure based on the definition of views in each mediator, since a view in a mediator is defined by views in other mediators or base tables in several local information servers. A global query is transformed into a set of its sub-queries; and, each sub-query is the unit of evaluation in an information server or a child mediator. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently requested sub-queries are materialized in a mediator. However, the integrating schema in a mediator can be incrementally modified; and, the evaluation frequency of a global query can also be continuously varied. In order to select the optimized set of materialized sub-queries, with respect to their current evaluation frequencies, the proposed method applies a decay factor for modeling the recent access behavior of each sub-query. As a result, it is possible to adjust the optimized set of materialized sub-queries adaptively according to the recent changes in the evaluation frequencies of sub-queries. Consequently, this paper proposes an algorithm for selecting the optimized set of sub-queries to be materialized in each mediator of a hierarchical mediator system adaptively such that the overall query evaluation cost of the hierarchical mediator system can be minimized at all times.

Enabling Schema-Free XQuery with meaningful query focus

The widespread adoption of XML holds the promise that document structure can be exploited to specify precise database queries. However, users may have only a limited knowledge of the XML structure, and may be unable to produce a correct XQuery expression, especially in the context of a heterogeneous information collection. The default is to use keyword-based search and we are all too familiar with how difficult it is to obtain precise answers by these means. We seek to address these problems by introducing the notion of Meaningful Query Focus (MQF) for finding related nodes within an XML document. MQF enables users to take full advantage of the preciseness and efficiency of XQuery without requiring (perfect) knowledge of the document structure. Such a Schema-Free XQuery is potentially of value not just to casual users with partial knowledge of schema, but also to experts working in data integration or data evolution. In such a context, a schema-free query, once written, can be applied universally to multiple data sources that supply similar content under different schemas, and applied "forever" as these schemas evolve. Our experimental evaluation found that it is possible to express a wide variety of queries in a schema-free manner and efficiently retrieve correct results over a broad diversity of schemas. Furthermore, the evaluation of a schema-free query is not expensive: using a novel stack-based algorithm we developed for computing MQF, the overhead is from 1 to 4 times the execution time of an equivalent schema-aware query. The evaluation cost of schema-free queries can be further reduced by as much as 68% using a selectivity-based algorithm we develop to enable the integration of MQF operation into the query pipeline.

ADAPTIVE SELECTION OF MATERIALIZED QUERIES IN A MEDIATOR

In a mediator, query usage should be carefully monitored to determine the optimized set of materialized sub-queries, since the integration schema of a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied. This paper proposes a theoretical basis for adaptive selection of materialized sub-queries such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a query from the past, the accumulated usage frequency of a query decays as time goes by. Consequently, it is possible to find the optimum set of materialized sub-queries which minimizes the total evaluation cost of global queries in linear search complexity.