Query processing under GLAV mappings for relational and graph databases

Query Containment Problem (QCP) is one of the most fundamental decision problems in database query processing and optimization. Complexity of QCP for conjunctive queries has been fully understood since 1970s. But, as Chaudhuri and Vardi noticed in their classical 1993 paper this understanding is based on the assumption that query answers are sets of tuples, and it does not transfer to the situation when multi-set (bag) semantics is considered. Now, 30 years later, decidability of QCP for bag semantics remains an open question, one of the most intriguing open questions in database theory. In this paper we show a series of undecidability results for some generalizations of this problem. We show, for example, that the problem whether, for given two boolean conjunctive queries φ s and φ b , and a linear function F, the inequality F(φ s (D)) =< φ b (D) holds for each database instance D, is undecidable.

Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query processing in database management systems is one of the important topics that grabs attentions. Until now, many techniques have been implemented for query processing in database system. The purpose of these methods is to optimize query processing in the database. The main topics that is interested in query processing in the database makes run-time adjustments of processing or summarizing topics by using the new approaches. The aim of this research is to optimize processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid query processing. The proposed algorithm is fundamentally divided into three parts: separator, replacement policy, and query similarity detector. In order to improve the optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed algorithm, one of the advantages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This optimization method reduces the system load during high traffic load times for adaptive query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of query cost in the database with this method is 2.7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized.

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries.In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.

article Free Access Share on Containment of conjunctive queries: beyond relations as sets Authors: Yannis E. Ioannidis Univ. of Wisconsin, Madison Univ. of Wisconsin, MadisonView Profile , Raghu Ramakrishnan View Profile Authors Info & Claims ACM Transactions on Database SystemsVolume 20Issue 3Sept. 1995 pp 288–324https://doi.org/10.1145/211414.211419Published:01 September 1995Publication History 89citation759DownloadsMetricsTotal Citations89Total Downloads759Last 12 Months36Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

Query answering is an important problem in AI, database and knowledge representation. In this paper, we develop saturation-based Boolean conjunctive query answering and rewriting procedures for the guarded, the loosely guarded and the clique-guarded fragments. Our query answering procedure improves existing resolution-based decision procedures for the guarded and the loosely guarded fragments and this procedure solves Boolean conjunctive query answering problems for the guarded, the loosely guarded and the clique-guarded fragments. Based on this query answering procedure, we also introduce a novel saturation-based query rewriting procedure for these guarded fragments. Unlike mainstream query answering and rewriting methods, our procedures derive a compact and reusable saturation, namely a closure of formulas, to handle the challenge of querying for distributed datasets. This paper lays the theoretical foundations for the first automated deduction decision procedures for Boolean conjunctive query answering and the first saturation-based Boolean conjunctive query rewriting in the guarded, the loosely guarded and the clique-guarded fragments.

This paper is a revised and extended version of a paper entitled “Find the items which certainly have (most of the) important characteristics to a sufficient degree” by the same authors, prepared for the 7th World Congress of the International Fuzzy Systems Associations (IFSA’97), Prague, June 25–29, 1997 (Proceedings published by Academia, Praha, Vol. 2, pp. 243–248).

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

In this paper we address the problem of efficiently evaluating containment (i.e., subset, equality, and superset) queries over set-valued data. We propose a novel indexing scheme, the Ordered Inverted File (OIF) which, differently from the state-of-the-art, indexes set-valued attributes in an ordered fashion. We introduce query processing algorithms that practically treat containment queries as range queries over the ordered postings lists of OIF and exploit this ordering to quickly prune unnecessary page accesses. OIF is simple to implement and our experiments on both real and synthetic data show that it greatly outperforms the current state-of-the-art methods for all three classes of containment queries.

In recent years, large amount of data modeled by graphs, namely graph data, have been collected in various domains. Efficiently processing queries on graph databases has attracted a lot of research attentions. Supergraph query is a kind of new and important queries in practice. A supergraph query, q, on a graph database D is to retrieve all graphs in D such that q is a supergraph of them. Because the number of graphs in databases is large and subgraph isomorphism testing is NP-complete, efficiently processing such queries is a big challenge. This paper first proposes an optimal compact method for organizing graph databases. Common subgraphs of the graphs in a database are stored only once in the compact organization of the database, in order to reduce the overall cost of subgraph isomorphism testings from stored graphs to queries during query processing. Then, an exact algorithm and an approximate algorithm for generating significant feature set with optimal order are proposed to construct indices on graph databases. The optimal order on the feature set is to reduce the number of subgraph isomorphism testings during query processing. Based on the compact organization of graph databases, a novel algorithm of testing subgraph isomorphisms from multiple graphs to one graph is presented. Finally, based on all these techniques, a query processing method is proposed. Analytical and experimental results show that the proposed algorithms outper-form the existing similar algorithms by one to two orders of magnitude.

This paper concerns query answering in relational databases. We assume a universe U of attributes and a set of values associated with each attribute. A database scheme is a given collection R = {1, 2, ..., n} of subsets of U, called relation schemes. A query in R is any subset of U. We call "context" any joinable subset of R. An unambiguous context is one in which a query receives the same answer independently of the subcontext used for the computation. A formal treatment of unambiguous contexts is presented and it is shown that they provide a suitable basis for discussing the universal relation assumption, the relationship uniqueness assumption, maximal objects, and other related concepts.

This paper concerns query answering in relational databases. We assume a universe U of attributes and a set of values associated with each attribute. A database scheme is a given collection R = {1, 2, ..., n} of subsets of U, called relation schemes. A query in R is any subset of U. We call "context" any joinable subset of R. An unambiguous context is one in which a query receives the same answer independently of the subcontext used for the computation. A formal treatment of unambiguous contexts is presented and it is shown that they provide a suitable basis for discussing the universal relation assumption, the relationship uniqueness assumption, maximal objects, and other related concepts.

The importance of big data, one of the most popular and researched topics of today, has been a subject of constant debate. Big data appear in almost every aspect of our lives, such as healthcare, education, shopping, social media, and industry; however, its storage and processing is not very efficient when using traditional methods. Therefore, the aim of this study is to obtain big data using a novel social shopping application that collects data from its users. The application is designed to collect information about people, products, and friendships among people, as well as product relationships, and post creations, and also enables setting up various options to mark products, such as like, buy, have, or help. The data gathered by the system is analyzed using both relational and non-relational databases, and the performance of these databases is then compared using specific queries, to reveal the model which performs better and more efficiently. Three different database models were designed and implemented for the system; a relational database, a document database, and a graph database. As a result of the experiments, there is no single model that is superior to the others as all three databases have their advantages and disadvantages. Therefore, the database selection should be decided based on the application domain.

Conjunctive-query containment is the problem of deciding whether the answers of a given conjunctive query on an arbitrary database instance are always contained in the answers of a second query on the same instance. This is a very relevant question in query optimization, data integration, and other data management and artificial intelligence areas. The problem has been deeply studied and understood for the, so-called, set-semantics, i.e., when query answers and database instances are modelled as sets of tuples. In particular, it has been shown by Chandra and Merlin to be NPTIME-COMPLETE. On the contrary, when investigated under bag-semantics, a.k.a. multiset semantics, which allows for replicated tuples both in the underlying instance and in the query answers, it is not even clear whether the problem is decidable. Since this is exactly the standard interpretation for commercial relational database systems, the question turns out to be an important one. Multiple works on variations and restrictions of the bag-containment problem have been reported in the literature and, although the general problem is still open, we contribute with this article by solving a special case that has been identified as a major open problem on its own. More specifically, we study projection-free queries, i.e., queries without existentially quantified variables, and show decidability for the bag-containment problem of a projection-free conjunctive query into a generic conjunctive query. We prove indeed that deciding containment in this setting is in ¶i^p_2. Our approach relies on the solution of a special case of the Diophantine inequality problem via a reduction to the linear inequality problem and clearly exposes inherent difficulties in the analysis of the general question.

Many flexible approaches allow users to query the relational database with vague or imprecise terms similar to natural language; most of these proposals are based on the fuzzy set theory which provides an environment for working with the uncertainty described by the linguistic terms. This paper presents an overview of the most important proposals, to highlight important models concerning fuzzy query databases we consider two classifications: the first classification deals with flexible queries in crisp relational databases and the second deals with flexible queries in fuzzy relational databases. This paper aims to help researchers understand these approaches for further research in this area.