Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called MORTAL. Specifically, given multi-model data containing different data models and a set of queries, it could automatically design a relational schema to store these data while having a great query performance. To demonstrate it clearly, we are centered around the following modules: generating initial state based on loaded multi-model data, influencing learning process by setting parameters, controlling generated relational schema through providing semantic constraints, improving the query performance of relational schema by specifying queries, and a highly interactive interface for showing query performance and storage consumption when users adjust the generated relational schema.
In this paper a tool called RDBNorma is proposed, that uses a novel approach to represent a relational database schema and its functional dependencies in computer memory using only one linked list and used for semi-automating the process of relational database schema normalization up to third normal form. This paper addresses all the issues of representing a relational schema along with its functional dependencies using one linked list along with the algorithms to convert a relation into second and third normal form by using above representation. We have compared performance of RDBNorma with e…
On the generic information capacity of relational schemas with a single binary relation
Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；LISN（信息科学与数字网络实验室）；Birkbeck, University of London（伦敦大学伯贝克学院）；University of Edinburgh（爱丁堡大学）；Hasselt University（哈瑟尔特大学）；University of Wrocław（弗罗茨瓦夫大学）
We study the relational to RDF data exchange problem, where the tar- get constraints are specified using Shape Expression schema (ShEx). We investi- gate two fundamental problems: 1) consistency which is checking for a given data exchange setting whether there always exists a solution for any source instance, and 2) constructing a universal solution which is a solution that represents the space of all solutions. We propose to use typed IRI constructors in source-to- target tuple generating dependencies to create the IRIs of the RDF graph from the values in the relational instance, and we trans…
Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This…
University of Massachusetts（马萨诸塞大学）；Microsoft Research（微软研究院）
Instead of requiring a domain expert to specify the probabilistic dependencies of the data, in this work we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for columns, latent variables that cluster the data, and factors that reflect and represent the foreign key links. Experiments demonstrate the accuracy of the model and the scalability of inference on synthetic and real-world data.
We study the data transformation capabilities associated with schemas that are presented by directed multi-graphs and path equations. Unlike most approaches which treat graph-based schemas as abbreviations for relational schemas, we treat graph-based schemas as categories. A schema $S$ is a finitely-presented category, and the collection of all $S$-instances forms a category, $S$-inst. A functor $F$ between schemas $S$ and $T$, which can be generated from a visual mapping between graphs, induces three adjoint data migration functors, $Σ_F:S$-inst$\to T$-inst, $Π_F: S$-inst $\to T$-inst, and $Δ…
It is increasingly common to find complex data represented through the graph model. Contrary to relational models, graphs offer a high capacity for executing analytical tasks on complex data. Since a huge amount of data is still presented in terms of relational tables, it is necessary to understand how to translate this data into graphs. This paper proposes a complete mapping process that allows transforming any relational database (schema and instance) into a property graph database (schema and instance). Contrary to existing mappings, our solution preserves the three fundamental mapping prop…
We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent instantiation of these schematic answers using a conventional relational DBMS. In this research note, we outline the main idea of this technique -- using abstractions of databases and constrained clauses for deriving schematic answers. The proposed method can …
The digital transformation of companies has led to the evolution of databases towards Big Data. Our work is part of this context and concerns more particularly the mechanisms to extract datasets stored in a Data Lake and to store the data in a Data Warehouse. The latter will allow, in a second time, decisional analysis. In this paper, we present the extraction mechanism limited to relational databases. To automate this process, we used the Model Driven Architecture (MDA), which offers a formalized environment for schema transformation. From the physical schemas describing a Data Lake, we propo…
Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using information on referential integrity constraints, i.e., key/foreign key pairs. Although several such systems have been proposed in the literature, most of them only support queries whose keywords refer to the contents of the target d…
How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling secur…
Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar …
In this paper we present a new family of Intensional RDBs (IRDBs) which extends the traditional RDBs with the Big Data and flexible and 'Open schema' features, able to preserve the user-defined relational database schemas and all preexisting user's applications containing the SQL statements for a deployment of such a relational data. The standard RDB data is parsed into an internal vector key/value relation, so that we obtain a column representation of data used in Big Data applications, covering the key/value and column-based Big Data applications as well, into a unifying RDB framework. We de…
A new family of Intensional RDBs (IRDBs), introduced in [1], extends the traditional RDBs with the Big Data and flexible and 'Open schema' features, able to preserve the user-defined relational database schemas and all preexisting user's applications containing the SQL statements for a deployment of such a relational data. The standard RDB data is parsed into an internal vector key/value relation, so that we obtain a column representation of data used in Big Data applications, covering the key/value and column-based Big Data applications as well, into a unifying RDB framework. Such an IRDB arc…
Most of data on the Web are still stored in relational databases. Therefore, it is more important to make the correspondence between relational databases (RDB) and ontologies for storing the Web data. In this paper, we present an new approach to map the data stored in relational databases into the Semantic Web, we exploit simple mappings based on some specifications of the database schema, and we explain how relational databases can be used to define a mapping mechanism between relational database and OWL ontology. A framework has been developed, which migrates successfully RDB into OWL docume…
Relational data exchange is the problem of translating relational data from a source schema into a target schema, according to a specification of the relationship between the source data and the target data. One of the basic issues is how to answer queries that are posed against target data. While consensus has been reached on the definitive semantics for monotonic queries, this issue turned out to be considerably more difficult for non-monotonic queries. Several semantics for non-monotonic queries have been proposed in the past few years. This article proposes a new semantics for non-monotoni…
Relational DBMSs continue to dominate the database market, and inference problem on external schema of relational DBMS's is still an important issue in terms of data privacy.Especially for the last 10 years, external schema construction for application-specific database usage has increased its independency from the conceptual schema, as the definitions and implementations of views and procedures have been optimized. This paper offers an optimized decomposition strategy for the external schema, which concentrates on the privacy policy and required associations of attributes for the intended use…
This paper presents a new technique for migrating data between different schemas. Our method expresses the schema mapping as a Datalog program and automatically synthesizes a Datalog program from simple input-output examples to perform data migration. This approach can transform data between different types of schemas (e.g., relational-to-graph, document-to-relational) and performs synthesis efficiently by leveraging the semantics of Datalog. We implement the proposed technique as a tool called Dynamite and show its effectiveness by evaluating Dynamite on 28 realistic data migration scenarios.
The relational data model offers unrivaled rigor and precision in defining data structure and querying complex data. Yet the use of relational databases in scientific data pipelines is limited due to their perceived unwieldiness. We propose a simplified and conceptually refined relational data model named DataJoint. The model includes a language for schema definition, a language for data queries, and diagramming notation for visualizing entities and relationships among them. The model adheres to the principle of entity normalization, which requires that all data -- both stored and derived -- m…
Schema evolution is a crucial aspect in database management. The proposed taxonomies of schema changes have neglected the set of operations that involves relationships between entity types: aggregation and references, as well as the possible existence of structural variations for schema types, as most of NoSQL systems are schemaless. The distinction between entity types and relationship types, which is typical of graph schemas, is also not taken into account in the published works. Moreover, NoSQL schema evolution poses the challenge of having different data models, and no standard specificati…
Mapping relational databases to RDF is a fundamental problem for the development of the Semantic Web. We present a solution, inspired by draft methods defined by the W3C where relational databases are directly mapped to RDF and OWL. Given a relational database schema and its integrity constraints, this direct mapping produces an OWL ontology, which, provides the basis for generating RDF instances. The semantics of this mapping is defined using Datalog. Two fundamental properties are information preservation and query preservation. We prove that our mapping satisfies both conditions, even for r…
In data management, and in particular in data integration, data exchange, query optimization, and data privacy, the notion of view plays a central role. In several contexts, such as data integration, data mashups, and data warehousing, the need arises of designing views starting from a set of known correspondences between queries over different schemas. In this paper we deal with the issue of automating such a design process. We call this novel problem "view synthesis from schema mappings": given a set of schema mappings, each relating a query over a source schema to a query over a target sche…
XML has emerged as the standard for representing and exchanging data on the World Wide Web. It is critical to have efficient mechanisms to store and query XML data to exploit the full power of this new technology. Several researchers have proposed to use relational databases to store and query XML data. While several algorithms of schema mapping and query mapping have been proposed, the problem of mapping XML data to relational data, i.e., mapping an XML INSERT statement to a sequence of SQL INSERT statements, has not been addressed thoroughly in the literature. In this paper, we propose an ef…
Storing XML documents in a relational database is a promising solution because relational databases are mature and scale very well and they have the advantages that in a relational database XML data and structured data can coexist making it possible to build application that involve both kinds of data with little extra effort . In this paper, we propose an algorithm schema named XRecursive that translates XML documents to relational database according to the proposed storing structure. The steps and algorithm are given in details to describe how to use the storing structure to storage and quer…
Classical approaches for OLAP assume that the data of all tables is complete. However, in case of incomplete tables with missing tuples, classical approaches fail since the result of a SQL aggregate query might significantly differ from the results computed on the full dataset. Today, the only way to deal with missing data is to manually complete the dataset which causes not only high efforts but also requires good statistical skills to determine when a dataset is actually complete. In this paper, we propose an automated approach for relational data completion called ReStore using a new class …
We investigate the data exchange from relational databases to RDF graphs inspired by R2RML with the addition of target shape schemas. We study the problems of consistency i.e., checking that every source instance admits a solution, and certain query answering i.e., finding answers present in every solution. We identify the class of constructive relational to RDF data exchange that uses IRI constructors and full tgds (with no existential variables) in its source to target dependencies. We show that the consistency problem is coNP-complete. We introduce the notion of universal simulation solutio…
Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, these graphs can be significantly larger than the initial input stored in the databa…
The problem determination of Functional Dependencies between attributes Relation Scheme in the Relational Data Model. El problema de determinar Dependencias Funcionales entre atributos en los esquemas en el Modelo Relacional
