111,670 research outputs found
Role-based Data Management
Database systems build an integral component of todayâs software systems and as such they are the central point for storing and sharing a software systemâs data while ensuring global data consistency at the same time. Introducing the primitives of roles and their accompanied metatype distinction in modeling and programming languages, results in a novel paradigm of designing, extending, and programming modern software systems. In detail, roles as modeling concept enable a separation of concerns within an entity. Along with its rigid core, an entity may acquire various roles in different contexts during its lifetime and thus, adapts its behavior and structure dynamically during runtime.
Unfortunately, database systems, as important component and global consistency provider of such systems, do not keep pace with this trend. The absence of a metatype distinction, in terms of an entityâs separation of concerns, in the database system results in various problems for the software system in general, for the application developers, and ïŹnally for the database system itself. In case of relational database systems, these problems are concentrated under the term role-relational impedance mismatch. In particular, the whole software system is designed by using different semantics on various layers. In case of role-based software systems in combination with relational database systems this gap in semantics between applications and the database system increases dramatically. Consequently, the database system cannot directly represent the richer semantics of roles as well as the accompanied consistency constraints. These constraints have to be ensured by the applications and the database system loses its single point of truth characteristic in the software system. As the applications are in charge of guaranteeing global consistency, their development requires more effort in data management. Moreover, the software systemâs data management is distributed over several layers, which results in an unstructured software system architecture.
To overcome the role-relational impedance mismatch and bring the database system back in its rightful position as single point of truth in a software system, this thesis introduces the novel and tripartite RSQL approach. It combines a novel database model that represents the metatype distinction as ïŹrst class citizen in a database system, an adapted query language on the database modelâs basis, and ïŹnally a proper result representation. Precisely, RSQLâs logical database model introduces Dynamic Data Types, to directly represent the separation of concerns within an entity type on the schema level. On the instance level, the database model deïŹnes the notion of a Dynamic Tuple that combines an entity with the notion of roles and thus, allows for dynamic structure adaptations during runtime without changing an entityâs overall type.
These deïŹnitions build the main data structures on which the database system operates. Moreover, formal operators connecting the query language statements with the database model data structures, complete the database model. The query language, as external database system interface, features an individual data deïŹnition, data manipulation, and data query language. Their statements directly represent the metatype distinction to address Dynamic Data Types and Dynamic Tuples, respectively. As a consequence of the novel data structures, the query processing of Dynamic Tuples is completely redesigned. As last piece for a complete database integration of a role-based notion and its accompanied metatype distinction, we specify the RSQL Result Net as result representation. It provides a novel result structure and features functionalities to navigate through query results. Finally, we evaluate all three RSQL components in comparison to a relational database system. This assessment clearly demonstrates the beneïŹts of the roles conceptâs full database integration
Knowledge Management Systems in Museums: the Next Generation for Assimilating Museum Information Resources in an Electronic Environment
This thesis focuses on knowledge management practices, tools, and systems and how it can play a vital role for managing collections in museums. The purpose of knowledge management would be to control information across disparate collections and departments within museums. The process of gathering. collecting and storing various data will help institutions achieve cost-effective solutions for a successful information management system. Implementing the concept and applications of knowledge management would create a culture that would encourage knowledge sharing among curators, registrars, directors of development and exhibition designers, to name a few. Further, it would establish museum-wide shared resources that would be available in one relational database for all to access, navigate, and contribute. However, facilitating this new museological concept presents many challenges and barriers. Advancements are being made through the development of knowledge tools, standards and other forms of technology. Overall, knowledge management would be beneficial in supporting the integration of museum informational resources (i.e, exhibition catalogs, press releases, memberships) in an electronic environment
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Generic unified modelling process for developing semantically rich, dynamic and temporal models
Models play a vital role in supporting a range of activities in numerous domains. We rely on models to support the design, visualisation, analysis and representation of parts of the world around us, and as such significant research effort has been invested into numerous areas of modelling; including support for model semantics, dynamic states and behaviour, temporal data storage and visualisation. Whilst these efforts have increased our capabilities and allowed us to create increasingly powerful software-based models, the process of developing models, supporting tools and /or data structures remains difficult, expensive and error-prone. In this paper we define from literature the key factors in assessing a modelâs quality and usefulness: semantic richness, support for dynamic states and object behaviour, temporal data storage and visualisation. We also identify a number of shortcomings in both existing modelling standards and model development processes and propose a unified generic process to guide users through the development of semantically rich, dynamic and temporal models
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of todayâs enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
ïŹrst a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
Moa and the multi-model architecture: a new perspective on XNF2
Advanced non-traditional application domains such as geographic information systems and digital library systems demand advanced data management support. In an effort to cope with this demand, we present the concept of a novel multi-model DBMS architecture which provides evaluation of queries on complexly structured data without sacrificing efficiency. A vital role in this architecture is played by the Moa language featuring a nested relational data model based on XNF2, in which we placed renewed interest. Furthermore, extensibility in Moa avoids optimization obstacles due to black-box treatment of ADTs. The combination of a mapping of queries on complexly structured data to an efficient physical algebra expression via a nested relational algebra, extensibility open to optimization, and the consequently better integration of domain-specific algorithms, makes that the Moa system can efficiently and effectively handle complex queries from non-traditional application domains
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