A full-screen facility for defining relational and entity-relationship database schemas

This paper presents techniques and tools to transform spreadsheets into relational databases and back. A set of data refinement rules is introduced to map a tabular datatype into a relational database schema. Having expressed the transformation of the two data models as data refinements, we obtain for free the functions that migrate the data. We use well-known relational database techniques to optimize and query the data. Because data refinements define bi-directional transformations we can map such database back to an optimized spreadsheet. We have implemented the data refinement rules and we constructed Haskell-based tools to manipulate, optimize and refactor Excel-like spreadsheets.

Fundamental notions of relative information capacity between database structures are studied in the context of the relational model. Four progressively less restrictive formal definitions of "dominance" between pairs of relational database schemata are given. Each of these is shown to capture intuitively appealing, semantically meaningful properties which are natural for measures of relative information capacity between schemata. Relational schemata, both with and without key dependencies, are studied using these notions. A significant intuitive conclusion concerns the informal notion of relative information capacity often suggested in the conceptual database literature, which is based on accessability of data via queries. Results here indicate that this notion is too general to accurately measure whether an underlying semantic connection exists between database schemata. Another important result of the paper shows that under any natural notion of information capacity equivalence, two relational schemata (with no dependencies) are equivalent if and only if they are identical (up to re-ordering of the attributes and relations). The approach and definitions used here can form part of the foundation for a rigorous investigation of a variety of important database problems involving data relativism, including those of schema integration and schema translation.

Ontologies represents main building material for creation of Semantic Web. Without ontologies it could not be possible to enable machine processability of data on the Semantic Web. Data is usually stored inside relational databases that represent the backbone for dynamical web sites. That data represents the invisible Web, ignored by search engines and Web crawlers. This paper presents a way to extract semantics from relational database schema and create OWL ontology automatically. Because relational database schema can capture very limited semantics, ontologies are enriched with additional WordNet semantics.

In this paper, we develop techniques to produce interoperable queries with object and relational databases. A user poses a local query in a local query language, against a local object or relational schema. We transparently produce appropriate queries with respect to a remote target object or relational schema, corresponding to some remote database which contains data relevant to the user's query. Mapping knowledge to resolve representational heterogeneities in local and remote schemas is expressed in a canonical representation, CRmapping, and is independent of the particular data model. A canonical representation CRquery is also used to resolve heterogeneities of query languages. A set of heterogeneous transformation algorithms define the appropriate transformations from the local queries to the remote queries. The use of canonical representations (CR) allows us to represent queries independent of the particular query language, and to resolve representational conflicts in a uniform manner, independent of models and query languages.

In this paper Semantic-Enhanced Entity Relationship (SEER) and Semantic – Relational Database (SRD) diagrams are proposed. SEER and SRD are extensions of Enhanced Entity Relationship (EER) and Relational Database (RD) diagrams respectively that are based on dealing with Semantic Integrity Constraints. EER and RD do not take semantic constraints in matter. A lots of information are lost because of the shortage of documentations. The proposed SEER and SRD models embed the semantic constraints using a new notations and pseudo code to give a full diagram for a database system. The proposed models handle the documentation shortage problem.

In this paper, we introduce a XML modeling methodology to design W3C XML schema using UML and we propose a unified design methodology for relational database schema to store XML data efficiently in relational databases. In the second place, in order to verify objectivity of the unified design methodology. Using Ronald Bourret’s method, first we introduce the method of the transformation from XML schema to the object model and second we translate the object model into the relational database schema. Therefore we show the mutual consistency between those consequences, and so can verify the unified design methodology, we proposed in this paper.KeywordsClass DiagramCyclic GraphClass TableMutual ConsistencyDefault ClauseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The rapid growth of available data arises the need for more sophisticated techniques for semantic access to information. It has been proved that using conceptual model or ontology over relational data sources is necessary to overcome many problems related with accessing the structured data. However, the task of wrapping the data residing in a database by means of an ontology is mainly done manually.The research we are carrying out studies the reuse of relational sources in the context of semantics-based access to information. This problem is tackled in two phases: (i) extracting semantics hidden in the relational sources by wrapping them by means of an ontology, (ii) understanding the methodology for semantic extension of such ontologies. In this paper we focus on the first sub-problem and present an automatic procedure for extracting from a relational database schema its conceptual view. The semantic mapping between the database and its conceptualization is captured by associating views over the data source to elements of the extracted ontology. To represent the extracted conceptual model we use an ontology language, rather that a graphical notation, in order to provide precise formal semantics. Our approach uses heuristics based on ideas of standard relational schema design and normalization. Under this we formally prove that our technique preserves the semantics of constraints in the database. Therefore, there is no data loss, and the extracted model constitutes a faithful wrapper of the relational database.

Knowledge of the dependencies and correlations which exist between data units in a relational database is of paramount importance when designing a distributed database since discovering such correlations would be crucial for reaching an optimal data distribution. The knowledge may also be used to improve the performance of a centralized database through a reorganization process. In this paper, we propose a data mining approach for identifying groups of correlated tables in a relational database schema by considering the work profile of the database. We analyze the query history to find sets of tables that are frequently accessed together. The sets of tables that are found overlap in general and we therefore turn each set of tables into a fuzzy set and determine the degree of membership of each table in each of the fuzzy sets. Our approach offers a high degree of flexibility and can be easily customized to produce result with desired amount of detail. The outcome will be highly valuable for guiding a database administrator in producing a better allocation plan. It also guides the database designer in deriving a well optimized fragmentation plan. Our experiment shows the viability and power of our approach. The proposed approach can also be extended to find correlations between smaller data units such as fragments, rows or columns.

This paper presents formal mappings between semantic, nested relational, and relational database schemes and instances. It is shown that the corresponding schemes are equivalent w.r.t. the notion of information capacity [Hul 86], i.e., the mappings between the corresponding sets of instances are bijections. The examinations are done in the framework of the database model EXTREM (EXTended RElational Model) which integrates four types of database models: semantic (the IFO model [AbH

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.

This chapter discusses architectural aspects of Distributed Knowledge Base Management Systems. We investigate how research results on knowledge representation from the artificial intelligence side and on massive data management from the database side may be effectively combined in the design and development of a future distributed knowledge base management system architecture. In particular we address the incorporation of preexisting, autonomously created and administered, heterogeneous database systems as components in a Distributed Knowledge Base Management System and we illustrate how a relational data base schema may be translated into a semantic data model using the Temporal Hierarchic Model [FN85, SCHI83b] as the representation tool.KeywordsKnowledge RepresentationDatabase AdministratorDatabase SchemaLocal DatabaseData DictionaryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Mapping relational database schema to ontology is a critical task for ontology-based heterogeneous database integration. This paper proposes a hybrid approach combining element-level and structure-level techniques to discover the correspondences between relational database schema and ontology. In the process of element-level matching, string-based and Word Net sense-based techniques are combined to calculate the name similarity between elements. This paper analyzes different characteristics exhibited by matching element pairs of various types and describes how to use these characteristics to calculate the structure similarity. A series of experiments indicate that our approach performs better than the previous ones.

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

Relational model is the most essential database model for decades and is examined in various ways. A very important concept in the relational modeling is the concept of dependencies. Particularly a set of functional dependencies and its equivalent minimal cover are discussed here for synthesizing third normal form relations. A traditional synthesis algorithm have been used to reduce the set of functional dependencies, F, into an equivalent minimal cover. This cover is then used to develop a relational schema; however, a given set of functional dependencies may have more than one minimal covers. In turn, different minimal covers may give rise to different relational schema. Database designers had already been agreed upon the use of popular inference rules of Armstrong to obtain minimal covers from the set F of functional dependencies. Despite the frequent use of the traditional synthesis algorithm, there is still a lack of formality and simplicity support in that algorithm to produce a minimum cover, especially; the step(s) of the algorithm in which the set of functional dependencies of F are used to make left-irreducible remain informal. In this paper, an extension is proposed to the synthesis algorithm that aids in to produce an optimal minimal cover more systematically from the given set of functional dependencies. The optimal minimal cover obtained by this extension would be appropriate for synthesizing third normal form relational schema from the universal relation scheme.

Context. The complexity of understanding the concept of multivalued dependencies (when setting out their theoretical foundations, an axiomatic approach is used) in relational (table) databases leads to problems of their modeling, which in turn can cause a violation of data integrity. A partial solution to these issues is facilitated by the search for equivalent axiomatic systems of multivalued dependencies that are minimal in terms of the number and power of their components. This, in turn, requires establishing the equivalence of these axiomatic systems and proving the independence of their elements. The object of the study is the axiomatisations of multivalued dependencies.The purpose of the work is to establish the equivalence of two axiomatic systems of multivalued dependencies, one of which is minimal in terms of the number and power of components, and to prove the independence of specified axiomatics. Method. Set-theoretic and logical-algebraic methods are used in the construction of proofs. The equivalence of two axiomatic systems of multivalued dependencies is established classically: the relation of syntactic inference is considered and sequences of multivalued dependencies are constructed that are proofs of the rules of each axiomatics from the components of the other. To build proof of independence of the axiomatic system, models of relational schemes are proposed, such that for an axiom or rule whose independence is being proved, it is impossible to construct proof from set of other axioms or rules.Results. Equivalence of the axiomatisation of multivalued dependencies proposed by a group of scientists Beeri, Fagin and Howard and the Biskup’s axiomatisation of multivalued dependencies has been established. The independence of the Biskup’s axiomatic system is proved in the sense that without loss of completeness it is impossible to remove neither the only axiom nor none of the inference rules.Conclusions. The independence of the Biskup’s axiomatisation of multivalued dependencies, which has no more in number and power of components than other axiomatic systems of multivalued dependencies, is proved. Using such axiomatic system has advantages in the development of CASE-tools (Computer-Aided Software Engineering Tools), which include the implementation of reducing the relational database schema to the fourth normal form, and also, when manually designing a logical model of a relational database.