Alexander Schukin, Nikolai Scerbakov, Eugenia Rezedinova · AIP conference proceedings · 2024
Database Management Systems (DBMS) are software systems that implement different data structuring methods in practice. Formally, a Data Model defines data structuring facilities and operations that can be applied to such structured data. Potentially, databases can operate with well-structured data or semi-structured data. In the case of well-structured data, database structure can be separated from actual information content. Such a generic database structure is defined as a so-called database schema. Thus, the database can be seen as just instances of data types predefined in the database schema. A typical sample of well-structured data is the relational data model. Semi-structured data are kept as software repositories that contain both data structures and actual content. A typical sample of such semi-structured data repository is a collection of WWW documents along with navigable links. This paper proposes a method of structuring data repositories suitable for the development of semi-structured databases. The method is based on the concept of so-called structured collection (S-Collections). An S-Collection is an object that encapsulates other data objects and relationships between them. Database may be seen as a set of S-Collections. S-Collections may contain other S-Collections even recursively. Developing a database can be seen as creating individual data objects, combining the objects into S-Collections and inclusion of S-Collections into other S-Collections. Editing operations are always applied to a particular data object (S-collection), what presents a mechanism for supporting the logical integrity of complex semi-structured databases.
Cited by 1Open access
Juneyoung Park, Mun Yong Yi · 2016
The continuous need to process semi-structured data in the more connected and semantic web requires a retrieval model that can truly reflect the user's intention and capture a user's understanding. As a semantic network shows great potential in representing the inherent structure of information in a document, recent studies have attempted to apply semantic networks into information retrieval. While many of the recent works on semi-structured data retrieval focused on the use of field structure within the data. Solely relying on the field structure is insufficient to portray the user's understanding, which is represented through the use of specific query terms. In this study, we seek to overcome this limitation by utilizing a semantic network to model semi-structured data and apply a graph-based semi-structured data retrieval model. Using both a popular testing environment and a real-life query data, we compare the performance of the suggested model with various competitive state-of-the-art retrieval models. The study's findings demonstrate the strength of the proposed model while providing intriguing opportunities for further application of the model.
Cited by 1
Ludovic Denoyer, Patrick Gallinari · Information Processing & Management · 2004
Cited by 114
THIAGO RIBEIRO NUNES · 2017
[pt] Tarefas de exploração de informação são reconhecidas por possuir características tais como alta complexidade, falta de conhecimento do usuário sobre o domínio da tarefa e incertezas sobre as estratégias de solução. O estado-da-arte em exploração de dados inclui uma variedade de modelos e ferramentas baseadas em diferentes paradigmas de interação, como por exemplo, busca por palavras-chave, busca facetada e orientação-a-conjuntos. Não obstante os muitos avanços das últimas décadas, a falta de uma abordagem formal do processo de exploração, juntamente com a falta de uma adoção mais pragmática do princípio de separação-de-responsabilidades no design dessas ferramentas são a causa de muitas limitações. Dentre as limitações, essa tese aborda a falta de expressividade, caracterizada por restrições na gama de estratégias de solução possíveis, e dificuldades de análise e comparação entre as ferramentas propostas. A partir desta observação, o presente trabalho propõe um modelo formal de ações e processos de exploração, uma nova abordagem para o projeto de ferramentas de exploração e uma ferramenta que generaliza o estado-da-arte em exploração de informação. As avaliações do modelo, realizadas por meio de estudos de caso, análises e comparações o estado-da-arte, corroboram a utilidade da abordagem.
Cited by 0Open access
Wiktor Zegadlo, Giacomo Bergami · OSF Preprints (OSF Preprints) · 2023
Cited by 0
Karen Pinel-Sauvagnat, CHRISMENT, Claude, BOUGHANEM, Mohand · OpenGrey (Institut de l'Information Scientifique et Technique) · 2005
Cited by 0
Özgün Erensoy, Jörg Siekmann, Sergey Sosnovsky · MPG.PuRe (Max Planck Society) · 2015
Cited by 0
Djedjiga Belhadj, Abdel Belaı̈d, Yolande Belaïd · Lecture notes in computer science · 2023
Cited by 0
Xiaoxiao Sun, Yunzhong Hou, Hongdong Li et al. · arXiv (Cornell University) · 2021
Label-free model evaluation, or AutoEval, estimates model accuracy on unlabeled test sets, and is critical for understanding model behaviors in various unseen environments. In the absence of image labels, based on dataset representations, we estimate model performance for AutoEval with regression. On the one hand, image feature is a straightforward choice for such representations, but it hampers regression learning due to being unstructured (\ie no specific meanings for component at certain location) and of large-scale. On the other hand, previous methods adopt simple structured representations (like average confidence or average feature), but insufficient to capture the data characteristics given their limited dimensions. In this work, we take the best of both worlds and propose a new semi-structured dataset representation that is manageable for regression learning while containing rich information for AutoEval. Based on image features, we integrate distribution shapes, clusters, and representative samples for a semi-structured dataset representation. Besides the structured overall description with distribution shapes, the unstructured description with clusters and representative samples include additional fine-grained information facilitating the AutoEval task. On three existing datasets and 25 newly introduced ones, we experimentally show that the proposed representation achieves competitive results. Code and dataset are available at https://github.com/sxzrt/Semi-Structured-Dataset-Representations.
Cited by 8Open access
Gillian Dobbie, Tok Wang Ling · Encyclopedia of Database Systems · 2009
Cited by 0
Eleni Petraki · WSEAS TRANSACTIONS ON COMPUTERS · 2025
The massive collection of data that has been taking place in recent years, in multiple ways and at an unimaginably fast pace, has led to the need to store and retrieve information from both structured and semi-structured or unstructured data. While the relational database model is widely used and provides efficient ways to organize structured data, manage it, and search for information through SQL commands, it has limitations and is not a suitable model for organizing and storing semi-structured data or data that lacks structure. This paper briefly presents the FDB model, a NoSQL database model that provides flexibility in defining any database schema, overcomes the limitations of the relational database model, while at the same time offering the possibility of multilingual conceptual searches in the database data, utilizing the information derived from multilingual thesauri. This paper presents the architecture of the FDB model, the thesaurus management and conceptual search algorithms, the process of converting a relational database schema to the corresponding FDB database schema, the advantages of the model, the ability to host semi-structured databases, as well as conclusions and future directions.
Cited by 0Open access
Ludovic Denoyer, Patrick Gallinari · 2004
Recently, a new community has started to emerge around the development of new information research methods for searching and analyzing semi-structured and XML like documents. The goal is to handle both content and structural information, and to deal with different types of information content (text, image, etc.). We consider here the task of structured document classification. We propose a generative model able to handle both structure and content which is based on Bayesian networks. We then show how to transform this generative model into a discriminant classifier using the method of Fisher kernel. The model is then extended for dealing with different types of content information (here text and images). The model was tested on three databases: the classical webKB corpus composed of HTML pages, the new INEX corpus which has become a reference in the field of ad-hoc retrieval for XML documents, and a multimedia corpus of Web pages.
Cited by 0
Danny Almeida Soares · Open Repository of the University of Porto (University of Porto) · 2020
Cited by 1Open access
2013
Cited by 0
Price, Heather E., Smith, Christian · Sage Journals Data · 2021
To identify the dominant cultural models among parents transmitting faith to their children, we find few methodological guidelines to guide coding and analysis of semi-structured interviews. We thus developed a three-phase procedure for our research team. Phase-one follows Campbell et al. by unitizing on meanings rather than words/pages, including creating decision rules documents, keyword lists, and summary memos. We provide empirical support for the reliability of those procedures and contribute by adding final validity checks into phase one and a new set of second-order coding procedures as phase two and phase three, as suggested by Miles and Huberman, to transform theme analysis into patterned findings. Phase two codes the latent patterns underlying the phase-one thematic codes. Phase three quantifies phase-two codes into matrices. Although time intensive, other researchers can apply these procedures to produce transparent, auditable findings.
Cited by 0Open access
Kiumars Badr, Q. Peter He, Jin Wang · IFAC-PapersOnLine · 2022
Methanotroph-photoautotroph (M-P) coculture has been demonstrated to be a highly promising biotechnology platform for biogas conversion. The metabolic coupling of methane oxidation and oxygenic photosynthesis within the coculture offers many benefits for the design of robust biotechnologies for biogas conversion. In addition, it has been postulated that the potential emergent interspecies interactions within the coculture could further enhance the growth of the coculture and play a pivotal role in determining the composition and function of the coculture. However, no knowledge on these emergent metabolic interactions is currently available. This is mainly due to the inherent complexity of the M-P coculture, and the lack of experimental tools to characterize the coculture. In this work, enabled by a novel experimental-computational protocol we developed recently to accurately characterize the M-P coculture in real time, we aim to elucidate the potential emergent metabolic interactions within a model coculture (Methylomicrobium buryatense – Arthrosipira platensis). Using designed experiments, we were able to confirm the existence of other interspecies metabolic interactions, in addition to the exchange of the in situ produced O2/CO2 within the model M-P coculture. Moreover, through semi-structured kinetic modeling, we were able to quantify the effect of these additional interspecies interactions, albeit unknown, on the growth of the model coculture. Finally, we developed the very first genome-scale model for the M-P coculture, which consistently predicts the top 8 metabolite being exchanged between the methanotroph and photoautotroph within the coculture, which contribute to the enhanced growth observed in the experiment.
Cited by 3Open access
Diptangshu Pandit, Nabendu Chaki, Samiran Chattopadhyay · Lecture notes in electrical engineering · 2014
Cited by 0
Gillian Dobbie, Wu Xiaoying, Tok Wang Ling et al. · 2000
Semi-structured data is becoming increasingly important with the introduction of XML and related languages and technologies. The recent shift from DTDs (document type definitions) to XML-Schema for XML data highlights the importance of a schema definition for semi-structured data applications.
Cited by 0
Shady Hamouda, Mona Abdelrahman · 2024
The vast influx of data has posed significant challenges for major corporations. This deluge has sparked numerous issues within big data, prompting a need for research and industrial solutions. Among the crucial challenges is the requirement for semi-structured data storage and handling to manage large volumes of data with adaptable schemas. Recently, researchers have proposed features and specifications for designing schemas tailored to semi-structured data formats. However, these proposals have not undergone evaluation based on semi-structured properties. Consequently, this study assesses these features within the context of semi-structured properties for big data applications. The findings indicate that semi-structured properties partially or fully support some of these features.
Cited by 0
Boris Gruschko · 2008
Abstract. Model Driven Software Development (MDSD) is increasingly gaining attention in the software development community. The success of the MDSD approach depends on the usage of artifact managing tools, due to the number of different artifacts produced during MDSD based project’s life-cycle. However, the available tools and methods concentrate on model authoring. This leads to the negligence of software logistic problems introduced by MDSD. This paper outlines the problem of Meta Model versioning and Model migration in scope of enterprise scale applications. 1
Cited by 0