Using an object-oriented database to encapsulate heterogeneous scientific data sources

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

Entity alignment is the problem of identifying which entities in a data source refer to the same real-world entity in the others. Identifying entities across heterogeneous data sources is paramount to many research fields, such as data cleaning, data integration, information retrieval and machine learning. The aligning process is not only overwhelmingly expensive for large data sources since it involves all tuples from two or more data sources, but also need to handle heterogeneous entity attributes. In this paper, we propose an unsupervised approach, called EnAli, to match entities across two or more heterogeneous data sources. EnAli employs a generative probabilistic model to incorporate the heterogeneous entity attributes via employing exponential family, handle missing values, and also utilize the locality sensitive hashing schema to reduce the candidate tuples and speed up the aligning process. EnAli is highly accurate and efficient even without any ground-truth tuples. We illustrate the performance of EnAli on re-identifying entities from the same data source, as well as aligning entities across three real data sources. Our experimental results manifest that our proposed approach outperforms the comparable baseline.

An object-oriented anthropometric database for developing a man model

This paper describes our research toward the development a knowledge-based multimedia database system for bone age research. The prototype system is designed to allow users to query for information based on patient demographic profile, patient medical history, evolution constraints of the developing hand, image content, and fuzzy high-level terms and concepts. The major highlights of this work include: 1) object-oriented database management system, 2) graphical user interface for query formulation, 3) object-oriented data model which supports both traditional “Where-type” predicates and new object evolutionary and temporal predicates, and 4) cooperative query answering capabilities for intelligent query modification in the event of a null solution to the original query.

RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.

Management of large quantities of complex data is essential in many advanced application areas. Object-oriented (OO) database management system have been developed to effectively model and process the complex domain knowledge. They have been shown to outperform some existing relational systems. The existing implementations of OO database management systems attempt to improve the efficiency of OO queries by explicitly capturing the relationships among objects. However, the execution of complex queries involving the retrieval of objects from many classes and relationships among them causes the existing system to operate inefficiently. In this paper, we present parallel algorithms for the processing of queries against a large OO database. The algorithms are based on a closed model of query processing pattern-based access instead of the conventional value-based access. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the explicitly stored object associations. Generation of large quantities of temporary data is avoided by marking objects using their identifiers and by employing a two-phase query processing strategy. A query is processed by concurrent multiple waves, thereby improving parallelism avoiding the complexities introduced in their sequential implementation. The correctness and the performance of the parallel algorithms have been tested and analyzed by running parallel programs on a 32-node transputer based parallel machine designed and developed at the IBM Research Center at Yorktown Heights, New York. Benchmark queries of different semantic complexities are generated, and their performance is analyzed for various data and query parameters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Results and observations gathered while implementing the ANSI T1M1.5 GNM-T1.214 standard within an object-oriented database management system (ODBMS) framework are discussed. The generic network model (GNM) proposes a dynamic network modeling methodology that encompasses a wide spectrum of managed telecommunication network resources. Within the GNM, managed telecommunication resources are modeled from building blocks of lower-level resource elements (logical or physical). The complex relationships between GNM resource elements are described either by specialization/generalization or user/containment; therefore, an ODBMS is well suited for implementation of the GNM as compared to conventional database management systems. The ODBMS framework described also allows the database model to evolve with the GNM. It is demonstrated that a proposed refinement of the GNM into subclasses results in a functional database design that can model a large variety of network resources in accord with a standardized network view. >

• Offers a new vision on automated data harmonization in Dataspace (DS) systems. • Introduces LLM-based methods for scalable DS ingestion of heterogeneous datasources. • Presents a system with Harmonizer, Transformer, Evaluator components for ingestion. • Demonstrated an automated data ingestion prototype using LLM agents. • Validates the system with healthcare use case harmonizing heterogeneous data sources. Dataspaces (DS) enable stakeholders to collaborate on innovative, data-driven services by integrating data across domains. However, the realization and adoption of DS remain challenging due to domain-specific heterogeneity at the system, service, and data levels. While system and service-level heterogeneity can often be addressed through standards, data-level heterogeneity, namely data structures and semantics variations, remains challenging. To effectively ingest data into the DS, two communication endpoints must correctly interpret each other’s data models, therefore, DS ecosystems rely on “harmonization”, the process of generating a unified target data model from heterogeneous sources and transforming incoming data accordingly. Currently, harmonization and transformation are performed manually whenever new data sources are integrated. This is time-consuming, costly, and difficult to scale, posing a critical barrier to the realization and adoption of DS in practice. This study proposes a novel methodology for automated data harmonization during ingestion into DS ecosystems. The approach integrates harmonization, transformation, and human-in-the-loop evaluation within an automated system powered by modern LLM-based AI agents. These agents address data-level heterogeneity and generate harmonized target data models, representing a substantial departure from current manually-handled data harmonization. The system is validated through a healthcare use case, demonstrating its practical feasibility for harmonization during data ingestion into the DS. Overall, this work provides a foundational step toward seamless, efficient, and scalable data integration in DS. By automating data harmonization, it delivers substantial value to industry digital solutions as well as domains where data heterogeneity persists, including IoT or Big Data platforms.

Support for modeling relationships in object-oriented databases

Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.

The analysis of modern production environments requires a clear and understandable uniform visualization of the information from all levels of the production. This visualization has to be on the one hand light weight to avoid cognitive overload of the users and on the other hand rich enough to provide information about all production information sources. To solve this issue we propose to use a unified component-based visualization where single items can be exchanged to fulfill the informational needs of the users from heterogeneous data sources.

ObjectiveBiomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method.Materials and methodsWe developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures.ResultsOur unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.ConclusionsWe present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

The V3 Video Server is a demonstration showing a multimedia application developed on top of the VODAK database management system. VODAK is a prototype of an object-oriented and distributed database management system (DBMS) developed at GMD-IPSI. The V3 Video Server allows a user to interactively store, retrieve, manipulate, and present analog and short digital video clips. A video clip consists of a sequence of pictures and corresponding sound. Several attributes like author, title, and a set of keywords are annotated. The highlights of the demonstration are as follows. (1) It is shown that an object-oriented database management systems is very useful for the development of multimedia applications. (2) The video server gives valuable hints for the development of an object-oriented database management system in direction to a multimedia database management system.

Purpose of ReviewThe complexity of the data collected for sleep research is increasing, and the focal point of sleep research is dependent on a higher number of data sources. Data collected for sleep studies often includes both subjective and objective measurements of sleep quality and is gathered over a more extended period, e.g., for weeks, months, or even years. However, this variety and volume of data make it challenging and time-consuming for researchers to utilize. Therefore, sophisticated data structures are necessary to utilize data in sleep research.Recent FindingsThis paper explores how heterogeneous data sources can be represented in a homogeneous database design. The following research questions drove our work: (i) How can we represent sleep data from heterogeneous sources in a homogenous digital platform database? and (ii) How can a data source pipeline transform various data sources into a homogeneous data format?SummaryThis paper’s main contributions are conceptualizing the design and development of a homogeneous database and digital platform architecture and a data source pipeline that fits well for sleep research in particular and healthcare research in general.

We describe the implementation of a version manager on an object-oriented database management system. The version manager supports various version semantics and version derivation graphs. Especially, it supports the version propagation to effectively manage versions of design objects and the efficient storage management for versions. All functionalities of the presented version manager are integrated with an object-oriented database programming language seamlessly. Therefore, designers can simply develop their applications which manipulate design objects.