Logical Database Research Articles

Modeling and Teaching Techniques for Conceptual and Logical Relational Database Design

This paper proposes a series of techniques to be used in teaching database design. Common ERD notations are discussed. The authors developed an ERD notation, adapted from the Unified Modeling Language, which facilitates student learning of the database design process. The paper presents a specific step by step process for representing the ERD components as tables and for normalizing the resulting set of tables.

Engineering information modeling in databases: needs and constructions

PurposeTo provide a selective bibliography for researchers and practitioners interested in database modeling of engineering information with sources which can help them develop engineering information systems.Design/methodology/approachIdentifies the requirements for engineering information modeling and then investigates how current database models satisfy these requirements at two levels: conceptual data models and logical database models.FindingsPresents the relationships among the conceptual data models and the logical database models for engineering information modeling viewed from database conceptual design.Originality/valueCurrently few papers provide comprehensive discussions about how current engineering information modeling can be supported by database technologies. This paper fills this gap. The contribution of the paper is to identify the direction of database study viewed from engineering applications and provide a guidance of information modeling for engineering design, manufacturing, and production management.

A unified approach to structured and XML data modeling and manipulation

In this paper we propose an approach to defining logical database models, based on the instantiation of a general abstract model, and discuss its application to the management of mixed XML/relational data. Our abstract model is equipped with a parametric query algebra and relational-like algebraic equivalences, that do not have to be redefined when new models are generated. We present an instantiation of our model that unifies the main approaches to represent and manipulate relational and XML data, and in particular SQL, SQL/XML, XQuery, and Oracle’s XML Type. Additionally, our algebra can represent queries not expressible by other algebras taken from the literature. Among others, it can represent nested XQuery expressions with no constraints on nesting and on node constructors.

EDSIM: expert database system for inventory management

PurposeExpert database system (EDS), which is still in its infancy, is developed and demonstrated for a business function by coupling expert systems (ES) and database systems.Design/methodology/approachAn EDS for inventory management (EDSIM) using the fifth generation artificial intelligence (AI) language, Prolog, is developed. The convergence of logic programming and database techniques is focused in achieving the objective. EDSIM development is based on modular design and integration of several intelligent modules, with own knowledge base and shared dynamic database.FindingsEDSIM developed demonstrates the peculiarities of such an EDS over a system developed using conventional procedural language. It shows that development of such systems has great deployment potential in the next generation enterprise systems where embedded intelligence is inevitable.Research limitations/implicationsThough no attempt is made to demonstrate distributed processing and interaction among various intelligent autonomous modules, an initiative in that direction definitely can kindle reengineering of today's enterprise system.Practical implicationsThe language chosen for the development of EDSIM is Turbo Prolog™ and can be easily converted to a program in Visual Prolog™ which is the newer version of Turbo Prolog™. The advantage of such a conversion is that it will enable web readiness of the EDS there by facilitating e‐business in the web environment.Originality/valueThe benefits of the application of EDS, by the integration of ES and database systems in a fifth generation AI language, Prolog, paves way for capturing developments in AI and databases, simultaneously. This technology demonstrator has the potential to bring in an optimal mix of the above technologies in achieving major milestones while reengineering enterprise systems.

The deductive IDEF1X data model of a tooling system

IDEF1X is a tool widely used to depict data models. However, there have been few works that discussed the IDEFIX foundation, much less its logic semantics. F-logic is a logic database language based on object semantics. It is powerful in expressing object-oriented features. Thus we aim at constructing sufficient formality to allow formal analysis and to verify the properties of the IDEF1X data model. The IDEF1X data model is transformed into F-logic language first by transformation rules. Next, a logic interpretation structure of this data model is presented. The transformation processes and results are illustrated using an example of a tooling system. Finally, the significance of this approach is discussed and summarized.

Effects of Query Complexity and Learning on Novice User Query Performance With Conceptual and Logical Database Interfaces

Users see the database interface as the database system. A good interface enables them to formulate queries better. The semantics communicated through the interface can be classified according to abstraction levels, such as the conceptual and logical levels. With the conceptual interface, interaction is in terms of real-world concepts such as entities, objects and relationships. Current user-database interaction is mainly based on the logical interface, where interaction is in terms of abstract database concepts such as relations and joins. Many researchers argue that end users will perform better with the conceptual interface. This research tested this claim, as well as the effects of query complexity and learning, on the visual query performance of users. The experiment involved three tests: an initial test, a retention test and a relearning test. The results showed that, for complex queries, conceptual interface users achieved higher accuracy, were more confident in their answers, and spent less time on the queries. This is persistent across retention and relearning tests.

Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web

The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database—directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently—in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.

Unveiling the Hidden Bride: Deep Annotation for Mapping and Migrating Legacy Data to the Semantic Web

The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps and, thus, prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because, (i), dynamic web pages generated from databases outnumber static web pages, (ii), deep annotation may be a very intuitive way to create semantic data from a database and, (iii), data from databases should remain where it can be handled most efficiently in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.

Generic design of Web-based clinical databases.

BackgroundThe complexity and the rapid evolution and expansion of the domain of clinical information make development and maintenance of clinical databases difficult. Whenever new data types are introduced or existing types are modified in a conventional relational database system, the physical design of the database must be changed accordingly. For this reason, it is desirable that a clinical database be flexible and allow for modifications and for addition of new types of data without having to change the physical database schema. The ideal clinical database would therefore implement a highly-detailed logical database schema in a completely-generic physical schema that stores the wide variety of clinical data in a small and constant number of tables.ObjectiveThe objective was to review the medical literature regarding generic design of clinical databases.MethodsA search strategy was devised for PubMed and Google to get the best match of peer-reviewed articles and free Web resources on the subject.ResultsEight peer reviewed articles and a Web tutorial were found. All the resources described the so-called Entity-Attribute-Value (EAV) design as a means of simplifying the physical layout of data tables in a clinical database. In Entity-Attribute-Value design all data can be stored in a single generic table with conceptually 3 columns: 1 for entity (eg, patient identification), 1 for attribute (eg, name), and 1 for value (eg, "Jens Hansen"). To add more descriptive fields to the entity class, all that is necessary is to add attribute values to be stored in the attribute field. The main advantages of the Entity-Attribute-Value design are flexibility and effective entity-centered data retrieval. The main disadvantages are complicated front-end programming needed to display data in a conventional layout that the user understands and less-efficient attribute-centered queries. The Internet offers unique opportunities for database deployment, eliminating problems of user-interface deployment. Furthermore, Web forms may be generated in a completely-generic fashion during run time from metadata describing the semantic structure of clinical information stored in the database.ConclusionsThe Entity-Attribute-Value model is useful for generic design of clinical databases. Depending on the specific requirements of the application, more or less complex metadata models may be applied.

Analysis of existing databases at the logical level

Whereas physical database tuning has received a lot of attention over the last decade, logical database tuning seems to be under-studied. We have developed a project called DBA Companion devoted to the understanding of logical database constraints from which logical database tuning can be achieved.In this setting, two main data mining issues need to be addressed: the first one is the design of efficient algorithms for functional dependencies and inclusion dependencies inference and the second one is about the interestingness of the discovered knowledge. In this paper, we point out some relationships between database analysis and data mining. In this setting, we sketch the underlying themes of our approach. Some database applications that could benefit from our project are also described, including logical database tuning.

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.

Accumulation of the knowledge by a casting defect data base

We have developed a knowledge storing system for casting defects and a knowledge sharing system that can be linked to it. As a result of subjecting these systems to trials in several businesses, we have been able to confirm that they are useful for purposes such as compiling logical databases from technical information on defect countermeasures and sharing knowledge that can be expected to improve techniques in the future.

Knowledge Discovery with Second-Order Relations

This paper presents an induction technique that discovers a set of classification rules, from a set of examples, using second-order relations as a representational model. Second-order relations are database relations in which tuples have sets of atomic values as components. Using sets of values, which are interpreted as disjunctions, provides compact representations that facilitate efficient management and enhance comprehensibility. The second-order relational framework is based on theoretical foundations that link relational database theory, machine learning, and logic synthesis. The rule induction technique can be viewed as a second-order relation compression problem in which the original relation, representing training data, is transformed into a second-order relation with fewer tuples by merging tuples in ways that preserve consistency with the training data. This problem is closely related to two-level Boolean function minimization in logic synthesis. We describe a rule-mining system, SORCER, and compare its performance to two state-of-the-art classification systems: C4.5 and CBA. Experimental results based on the average of error rates ove 26 data sets show that SORCER, using a simple compression scheme, outperforms C4.5 and is competitive to CBA. Using a slightly more sophisticated compression scheme, SORCER outperforms both C4.5 and CBA.

Discovering interesting inclusion dependencies: application to logical database tuning

Inclusion dependencies together with functional dependencies form the most important data dependencies used in practice. Inclusion dependencies are important for various database applications such as database design and maintenance, semantic query optimization and efficient view maintenance of data warehouse. Existing approaches for discovering inclusion dependencies consist in producing the whole set of inclusion dependencies holding in a database, leaving the task of selecting the interesting ones to an expert user.In this paper, we take another look at the problem of discovering inclusion dependencies. We exploit the logical navigation, inherently available in relational databases through workloads of SQL statements, as a guess to automatically find out only interesting inclusion dependencies. This assumption leads us to devise a tractable algorithm for discovering interesting inclusion dependencies. Within this framework, approximate dependencies, i.e. inclusion dependencies which almost hold, are also considered.As an example, we present a novel application, namely self-tuning the logical database design, where the discovered inclusion dependencies can be used effectively.

MEER—An EER model enhanced with structure methods

Entity relationship (ER) schemas include cardinality constraints, that restrict the dependencies among entities within a relationship type. The cardinality constraints have direct impact on the application maintenance, since insertions or deletions of entities or relationships might affect related entities. Indeed, maintenance of a system or of a database can be strengthened to enforce consistency with respect to the cardinality constraints in a schema. Yet, once an ER schema is translated into a logical database schema, or translated within a system, the direct correlation between the cardinality constraints and maintenance transactions is lost, since the components of the ER schema might be decomposed among those of the logical database schema or the target system. In this paper, a full solution to the enforcement of cardinality constraints in EER schemas is given. We extend the enhanced ER (EER) data model with structure-based update methods that are fully defined by the cardinality constraints. The structure methods are provably terminating and cardinality faithful, i.e., they do not insert new inconsistencies and can only decrease existing ones. A refined approach towards measuring the cardinality consistency of a database is introduced. The contribution of this paper is in the automatic creation of update methods, and in building the formal basis for proving their correctness.

Regular database update logics

We study regular first-order update logic (FUL), which is a variant of regular dynamic logic in which updates to function symbols as well as to predicate symbols are possible. We first study FUL without making assumptions about atomic updates. Second, we look at relational algebra update logic ( RAUL), which can be viewed as an extension of relational algebra with assignment. RAUL is an instantiation of FUL. Third, we study dynamic database logic ( DDL), which is another version of FUL, in which the atomic updates can be “bulk updates” of predicates and updates of updateable functions. In all three cases, we define syntax, declarative semantics, axiomatizations, and operational semantics of the logic. All axiom systems are shown to be sound. Assuming the domain closure and unique naming assumptions, we also give a proof sketch of completeness of the axiomatization of DDL. The operational semantics presented in the paper are shown to be equivalent to the declarative semantics for certain classes of databases. We give examples of correctness proofs in RAUL and in DDL. Finally, we compare our approach to that of others and show how DDL can be used as a logic in which to specify and reason about updates to an object-oriented database system.

Fuzzy logic techniques in multimedia database querying: a preliminary investigation of the potentials

Fuzzy logic is known for providing a convenient tool for interfacing linguistic categories with numerical data and for expressing user's preference in a gradual and qualitative way. Fuzzy set methods have been already applied to the representation of flexible queries and to the modeling of uncertain pieces of information in databases systems, as well as in information retrieval. This methodology seems to be even more promising in multimedia databases which have a complex structure and from which documents have to be retrieved and selected not only from their contents, but also from "the idea" the user has of their appearance, through queries specified in terms of user's criteria. This paper provides a preliminary investigation of the potential applications of fuzzy logic in multimedia databases. The problem of comparing semistructured documents is first discussed. Querying issues are then more particularly emphasized. We distinguish two types of request, namely, those which can be handled within some extended version of an SQL-like language and those for which one has to elicit user's preference through examples.

Maximal state independent approximations to minimal real change

This paper is devoted to the problem of consistency enforcement for logical databases. The enforcement methods we propose compute the minimal real change in a given DB state, which is sufficient to accomplish the input update and preserve the integrity constraints (IC). For IC expressed in the form of a generalized logic program, this problem is proven to be hard. Meanwhile, we show that it is solvable in linear time under partial interpretations. We propose a method of computing DB state independent correct expansions of the input update and simultaneous optimization of IC with respect to this expansion. We show that under partial interpretations, optimal pairs (greatest correct update expansion/simplest equivalent IC) always exist and can be incrementally computed in square time. This partial solution being correct with respect to the total interpretations, we use it as an approximation in the total case. Moreover, for the class of IC without negation in clause bodies, we prove that this approximation constitutes the optimal pair (greatest correct update expansion/simplest equivalent IC).

A survey of languages for specifying dynamics: a knowledge engineering perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machines.

Incorporating Database Design in Warnier Method

The Warnier method, a highly prescriptive program design approach for file-oriented solutions, has been criticized for its lack of a database design component. This paper addresses this weakness by incorporating a logical database design step in Warnier method. Specifically, the paper presents rules for transforming the information in a Warnier diagram into a set of relations. With this extension the Wamier method complements the entity-relationship approach for data analysis and logical database design.