Ozone: Integrating Structured and Semistructured Data

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.

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

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.

Introduces the concept of representative objects, which uncover the inherent schema(s) in semi-structured, hierarchical data sources and provide a concise description of the structure of the data. Semi-structured data, unlike data stored in typical relational or object-oriented databases, does not have a fixed schema that is known in advance and stored separately from the data. With the rapid growth of the World Wide Web, semi-structured hierarchical data sources are becoming widely available to the casual user. The lack of external schema information currently makes browsing and querying these data sources inefficient at best, and impossible at worst. We show how representative objects make schema discovery efficient and facilitate the generation of meaningful queries over the data.

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.

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.

This study aims to establish an XML design for the Collection of examination articles(科文選集) and explore the integration of this semi-structured data with the structured Gwashi(科試) Dataset, which is being developed by the Center for the Study of Civil Service Examinations in the Joseon Dynasty of Korea University Institute for Sinographic Literatures and Philology. To this end, Chapter 2 provides an overview of the material characteristics of the Collection of examination articles housed in national and public institutions, as well as the collections held by Korea University, and confirms its complementary relationship with information based on Bangmok. Chapter 3 systematically reflects the formal and content-related characteristics of the Collection of examination articles into an XML document structure by designing a schema in three levels: ① Metadata, ② Formatdata, and ③ Contentdata, along with detailed classifications and technical principles for each element. Chapter 4 presents a practical method for extracting exam-related data from the Collection of examination articles XML documents using Python code and converting it into structured data, thus transforming semi-structured data into tabular format. Ultimately, this work contributes to enhancing the academic utility and potential of the Collection of examination articles by enabling a more multidimensional management approach, moving beyond bibliographic-centric management.

How Do Job Vacancy Rates Predict Firm Performance? A Web Crawling Massive Data Perspective

As semi-structured data is used widely in several fields, the importance of structured data mining is increasing recently. Although mining frequent patterns in structured data is one of the most fundamental tasks, frequent pattern miners often discover huge number of patterns. To overcome this problem, two major approaches, condensed representation mining and constraint-based mining, have been proposed. In this paper, as a technique for integrating these two approaches, we propose three algorithms, RCLOCOT, posCLOCOT, and negCLOCOT, for discovering closed ordered subtrees under anti-monotone constraints about the structure of patterns to be discovered. The proposed algorithms discover closed constrained subtrees efficiently not by post-processing but by pruning and skipping the search space based on the occurrence matching and the patterns on the border.

Linked Data is a powerful technology for storing and publishing structure of data. It is helpful for web applications because of its usefulness through semantic query data. However, using Linked Data is not easy for normal users who lack of knowledge about the structure of data or query's syntax of Linked Data. From that problem, we propose a translator component that is used for translating RESTful/JSON request messages to SPARQL command based on ontology — a metadata that describes the structure of data. Clients do not need to care about the structure of stored data or SPARQL language, a kind of query language used for querying linked data, when they insert a new instance or query all instances of any specific class. In addition, the translator component has the search function that can find a set of data from multiple classes based on finding the shortest paths between the target classes and target classes. This translator component will be applied for any dynamic ontological structure as well as automatically generate SPARQL command based on user request message.

Systems for managing and querying semistructured-data sources often store data in proprietary object repositories or in a tagged-text format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistructured data instance is given, a STORED mapping can be generated automatically using data-mining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a document-type-descriptor (DTD), when present, can be exploited to further improve performance.

An object wrapper [3,4] is a piece of middleware that provides an object view on top of a legacy database management system. But legacy database management systems such as relational database systems (RDS) and multidimensional database systems (MDS) [5,6,7] are different in many aspects from an Object-oriented Data Model. Among these aspects are semantic management, data structures and data types supported in these models. As a result, it is crucial to understand these data models in order to design and implement an efficient piece of middleware. In this paper, a short discussion of the real-world data modelling requirements is presented. Then, this is followed by a discussion on an Object View’s requirements. The third section of the paper presents the architecture of the Object-to-Multidimensional Mapping Engine (O-MME) [10,11,12,13,14,15,16,17,18,19]. A detail discussion on major interfaces and classes in the Object-to-Multidimensional mapping algorithm are presented next. Issues such as data type conversion and database language statements generation are discussed in this paper also. Finally, some performance issues are presented and future possible researches are outlined.KeywordsStructure Query LanguageMapping EngineCache ManagerBusiness ClassisRelational Database SystemThese 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.