XML을 활용한 科文選集의 디지털화 방안 연구

Applications have an increasing need to manage semistructured data (such as data encoded in XML) along with conventional structured data. We extend the structured object database model ODMG and its query language OQL with the ability to handle semistructured data based on the OEM model and Lorel language, and we implement our extensions in a system called Ozone. In our approach, structured data may contain entry points to semistructured data, and vice-versa. The unified representation and querying of such “hybrid” data is the main contribution of our work. We retain strong typing and access to all properties of structured portions of the data while allowing exible navigation of semistructured data without requiring full knowledge of structure. Ozone also enhances both ODMG/OQL and OEM/Lorel by virtue of their combination. For instance, Ozone allows OEM semantics to be applied to ODMG data, thus supporting semistructured-style navigation of structured data. Ozone also enables ODMG views of OEM data, allowing standard ODMG applications to access semistructured data without losing the benefits of structure. Ozone is implemented on top of the ODMG-compliant O2 database system, and it fully supports our extensions to the ODMG model and OQL.KeywordsQuery LanguageHybrid DataPath ExpressionSpeci CationSemistructured DataThese 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.

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

Most of datasets in open data portals are mainly in tabular format in spreadsheet, e.g. CSV and XLS. To increase the value and reusability of these datasets, the datasets should be made available in RDF format that can support better data querying and data integration. In this paper, we present a semi-automatic framework for generating and publishing RDF dataset from existing datasets in tabular format. This framework provides automatic schema detection functions, i.e. data type and nominal type detection. In addition, user can create RDF dataset from existing dataset step-by-step without required knowledge about RDF and OWL. Evaluation of the schema detection using some datasets from data.go.th shows that the technique can achieve high precision in most datasets and high recall for the datasets with small data input errors.

XML is a markup language specifically designed for the Internet, and it has become the standard for represent and exchange the data on the Internet. With the development of the Internet and day by day, large amount of XML documents are being added to the network continuously in various application domains. Thus, it is become necessary to work out how to create a way to store and manage XML data efficiently inside the databases. There are two methods to represent XML in the databases: (1) XML-Enabled databases which designed for structured data (XML is semi-structured data). And (2) Native XML databases or which designed specifically to store any XML document (structured and semi-structured data). The difference between the nature of the database structure and XML data structure became an important research topic, and this led to emergence Native XML database. This paper presents the indexing architecture of eXist, an Open Source native XML database system.

This paper studies from a machine learning viewpoint the problem of extracting tuples of a target n-ary relation from tree structured data like XML or XHTML documents. Our system can extract, without any post-processing, tuples for all data structures including nested, rotated and cross tables. The wrapper induction algorithm we propose is based on two main ideas. It is incremental: partial tuples are extracted by increasing length. It is based on a representation-enrichment procedure: partial tuples of length i are encoded with the knowledge of extracted tuples of length i-1. The algorithm is then set in a friendly interactive wrapper induction system for Web documents. We evaluate our system on several information extraction tasks over corporate Web sites. It achieves state-of-the-art results on simple data structures and succeeds on complex data structures where previous approaches fail. Experiments also show that our interactive framework significantly reduces the number of user interactions needed to build a wrapper

PurposeUpon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.Design/methodology/approachTo develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.FindingsLegislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.Research limitations/implicationsAlthough this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.Practical implicationsThis study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.Originality/valueThe findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

This study takes the Danjiangkou reservoir basin, which is the water source area of the South-to-North Water Diversion Project, one of the largest water diversion projects in the world, as the research area. Three different types of evapotranspiration (ET) datasets are adopted, including the Global Land Evaporation Amsterdam Model (GLEAM), European Centre for Medium-Range Weather Forecasts ERA5—Land Component (ERA5Land), and Complementary Relationship (CR) datasets. These datasets are analyzed for spatiotemporal evolution and data fusion using Mann–Kendall analysis, Sen’s Slope analysis, and Extended Triple Collocation (ETC). The aim is to improve the accuracy of evapotranspiration estimation in the watershed of the water source area. The results show the following: (1) All three sets of evapotranspiration data indicate an increasing trend in the watershed, with rates of 0.78 mm/year, 0.14 mm/year, and 2.56 mm/year, respectively. Additionally, the seasonal variation in evapotranspiration is significant, with the rate of change being summer > spring > autumn > winter. (2) The data fusion results indicate that ERA5Land performs best in the water source area watershed, with the smallest root mean square error (RMSE) value. In the fused data, ERA5Land’s evapotranspiration data account for the largest proportion at 59.93%, GLEAM ET data account for 39.96%, and CR’s evapotranspiration data account for the smallest proportion at only 0.11%. (3) The spatial distribution shows that the fused data fully exploits the advantages of different evapotranspiration data, inherits the advantages of ERA5Land and GLEAM ET products, and achieves effective fusion of multi-source data, thereby forming a more accurate dataset. These research findings provide scientific references for the construction of digital twin watersheds, intelligent water resource allocation, and effective responses to climate change in the water source area of the South-to-North Water Diversion Project.

With the development of information technology in Colleges and universities, the traditional data integration scheme has been unable to meet the needs of colleges and universities in dealing with massive unstructured and semi-structured data and various types of structured data integration and fusion analysis. Based on this, this paper uses MPP and Hadoop to build university data platform, which can not only meet the needs of deep analysis, complex query and rapid response of structured data, but also meet the performance requirements of massive unstructured data in storage, loading, conversion and other aspects. At last, the paper selects the log data of a university’s online behavior, carries out data storage, preprocessing and analysis query, verifies the performance advantages of the platform in massive data processing, and provides a new idea and method for the construction of university data platform.KeywordsHadoopMPP databaseHybrid architectureData analysis

Often interesting structured or semistructured data is not in database systems but in HTML pages, text files, or on paper. The data in these formats is not usable by standard query processing engines and hence users need a way of extracting data from these sources into a DBMS or of writing wrappers around the sources. This paper describes NoDoSE, the Northwestern Document Structure Extractor, which is an interactive tool for semi-automatically determining the structure of such documents and then extracting their data. Using a GUI, the user hierarchically decomposes the file, outlining its interesting regions and then describing their semantics. This task is expedited by a mining component that attempts to infer the grammar of the file from the information the user has input so far. Once the format of a document has been determined, its data can be extracted into a number of useful forms. This paper describes both the NoDoSE architecture, which can be used as a test bed for structure mining algorithms in general, and the mining algorithms that have been developed by the author. The prototype, which is written in Java, is described and experiences parsing a variety of documents are reported.

Data security is highly vital and crucial for every organization, regardless of size of organization—whether it it is a small scale organization, medium scale organization, or large scale organization. This applies to the both inside and outside the organization. The essential prerequisite for anyone's confidence and trust in any organization is data security. In day to day life human generates various data in different formats like text data, graphical data, voice data, video data etc. using various medium like youtube, twitter, facebook, instragram, emails etc. These data may be a structural data, may be a semi structural data or may be an unstructured data. In today’s life all the data which are generated is generally huge in size which we termed as big data. To handle this big data is very difficult task. Cloud computing is one of the technique which can handle this very well. But the main fact is that there are various challenges to both of these technologies. One of the biggest challenges is to provide security to this kind of data. There are various research has been done and still going on to address this issue effectively. In this paper we have also tried to address this issue after doing exhaustive literature work then we provide our own AVJ security algorithm which can provide security to text data. The writers' names—A for Mr. Ankur Shah, V for Dr. Vishal Dahiya, and J for Dr. Jay A Dave—led to the creation of the moniker AVJ***Throughout this paper we have used term base paper algorithm meaning that we are using reference paper [5] algorithm of Mohammad Anwar Hossain and et al.

Business intelligence (BI) technologies are being successfully applied in data analytics, including in the use of spatial data. The advent of the semantic web brought semantically rich and semi-structured data formats, such as resource description framework (RDF), which are external to the BI infrastructure. The integration of both conventional and semantic web data into a BI system results in a new category of analytical tools called exploratory OLAP. We extend exploratory OLAP with spatial capabilities, providing an exploratory SOLAP, called ExpSOLAP. ExpSOLAP is the first analytical tool that integrates semantic, spatial, semi-structured data with traditional spatial, structured data sources. Additionally, SOLAP queries can be posed in both data sources.

At present, enterprises have been developing more and more quickly in China. In order to strengthen the systems of enterprise management, manufacturing execution, equipment and so on in each link of management, many enterprises have started to use data integration to construct an Informationization management system. Based on the needs of the development of enterprises, this paper analyzes the pattern formation of data integration and its advantages and explores its role in enterprise informationization construction.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

This article proposes a data warehouse integration technique that combines data and documents from different underlying documents and database design approaches. The well-defined and structured data such as relational, object-oriented and object relational data, semi-structured data such as XML, and unstructured data such as HTML documents are integrated into a Web data warehouse system. The user specified requirements and data sources are combined to assist with the definitions of the hierarchical structures, which serve specific requirements and represent a certain type of data semantics using object-oriented features including inheritance, aggregation, association, and collection. A conceptual integrated data warehouse model is then specified based on a combination of user requirements and data source structure, which creates the need for a logical integrated data warehouse model. A case study is then developed into a prototype in a Web-based environment that enables the evaluation. The evaluation of the proposed integration Web data warehouse methodology includes the verification of correctness of the integrated data, and the overall benefits of utilizing this proposed integration technique.

This article proposes a data warehouse integration technique that combines data and documents from different underlying documents and database design approaches. The well-defined and structured data such as relational, object-oriented and object relational data, semi-structured data such as XML, and unstructured data such as HTML documents are integrated into a Web data warehouse system. The user specified requirements and data sources are combined to assist with the definitions of the hierarchical structures, which serve specific requirements and represent a certain type of data semantics using object-oriented features including inheritance, aggregation, association, and collection. A conceptual integrated data warehouse model is then specified based on a combination of user requirements and data source structure, which creates the need for a logical integrated data warehouse model. A case study is then developed into a prototype in a Web-based environment that enables the evaluation. The evaluation of the proposed integration Web data warehouse methodology includes the verification of correctness of the integrated data, and the overall benefits of utilizing this proposed integration technique.