Data In Relational Databases Research Articles

Preface to the special issue on concept lattices and their applications—CLA 2012

The Ninth International Conference “Concept Lattices and Applications (CLA 2012)” was held in Fuengirola (Malaga), Spain from October 11th until October 14th 2012. CLA is a conference series which was started in 2002 and which focuses on Concept Lattices, Formal Concept Analysis (FCA) and Galois Lattices. FCA was invented by Rudolf Wille in the 1980s as a mathematical modeling of hierarchies, data analysis and conceptual descriptions using mathematical lattice theory. A binary relation (for example between some objects and their attributes) is modeled as a formal context which is transformed into a concept lattice. Because binary relations can be extracted from any relational database or other data repository, FCA has applications in many disciplines, such as data and knowledge processing, information retrieval, data mining, reasoning and software engineering. Since the 1980s, FCA has grown into a reasonably large international research community with two annual international conference series (CLA and ICFCA). Concept lattices can be graphically represented which encourages the development of graphical interfaces for end users and poses interesting research questions relating to graph theory. In the context of “big data”, research questions focus on improving the efficiency of FCA algorithms and on decomposing formal contexts into meaningful smaller units. More recently there has been an increasing interest in Fuzzy FCA, which supports the application of FCA to non-discrete datasets and the factorization of binary matrices as a generalization of formal contexts. This special issue contains the 9 best papers from CLA 2012. In total 44 papers were submitted to CLA 2012 out of which 28 papers were accepted as full papers and published as Volume 972 in the CEUR series (ceur-ws.org). For the special issue, the authors

SQL-based semantics for path expressions over hierarchical data in relational databases

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

SURVEY ON SCALABLE CONTINUAL TOP-K KEYWORD SEARCH IN RELATIONAL DATABASES

Keyword search in relational database is a techniqu e that has higher relevance in the present world. E xtracting data from a large number of sets of database is very important .Becau se it reduces the usage of man power and time consu mption. Data extraction from a large database using the relevant keyword ba sed on the information needed is a very interactive and user friendly. Without knowing any database schemas or query languages lik e sql the user can get information. By using keywor d in relational database data extraction will be simpler. The user doesn’t w ant to know the query language for search. But the database content is always changing for real time application for example data base which store the data of publication data. When new publications arrive it should be added to database so the database content changes according to time. Because the database is updated frequently the result should change. In order to handle the databa se updation takes the top-k result from the current ly updated data for each search. Top-k keyword search means take greatest k results based on the relevance of document. Keyword search in relational database means to find structural information from tuples from the database. Two types of keyword sear ch are schema-based method and graph based approach. Using top-k keywor d search instead of executing all query results tak ing highest k queries. By handling database updation try to find the new resu lts and remove expired one.

A Novel Relational Database Data Mining Method Based on Artificial Neural Network

This study proposed a novel relational database data mining method based on the artificial neural network. It analyzed the disadvantages of the existed data mining methods and then introduced the novel algorithm. This algorithm discovered the implicit knowledge by training the data samples in the database. This study introduced the artificial neural network method training model and algorithm, and tested the method by an example.

Mapping Process of Relational Data to OWL Class Instances using XSLT

Ontologies are widely used in many domains and evolve as domains change [7]. From the other side, the relational database technology has ensured the best facilities for storing, updating and manipulating the information of problem domain. We propose in this paper a transformation process from existing data in relational databases into OWL class instances using XSLT. This process allows us to mapping some constraints on columns such as foreign key while maintaining semantic constraints. Our approach start by translate the relational data into adequate XML document conforming to a suitable XML schema. Since our process is based on XSLT Stylesheets, its transformation rules can be modified in a very flexible manner in order to consider different mapping strategies and future requirements.

Mining heterogeneous information networks

Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.

An intensional approach for periodic data in relational databases

Periodic data play a major role in many application domains, spanning from manufacturing to office automation, from scheduling to data broadcasting. In many of such domains, the huge number of repetitions make the goal of extesionally storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an intensional representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is an extension of the TSQL2 temporal relational data model. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct with respect to the traditional extesional approach. We also provide an experimental evaluation of our approach.

Storing and querying fuzzy XML data in relational databases

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data management has been extensively investigated in various database management systems. Currently, introducing native support for XML data in relational database management systems (RDBMs) has attracted considerable interest with a view to leveraging the powerful and reliable data management services provided by RDBMs. Although there is a rich literature on XML-to-relational storage, none of the existing solutions satisfactorily addresses the problem of storing fuzzy XML data in RDBMs. In this paper, we study the methodology of storing and querying fuzzy XML data in relational databases. In particular, we present an edge-based approach to shred fuzzy XML data into relational data. The unique feature of our approach is that no schema information is required for our data storage. On this basis, we present a generic approach to translate path expression queries into SQL for processing XML queries.

OpLiDaF - Open Linked Data Framework: una piattaforma per la creazione e la pubblicazione di linked data

ITACH@ Project, Innovative Technologies And Cultural Heritage Aggregation, intends to offer innovative tools for the development of the tourism industry and Italian culture. This paper analyzes the particular technological component defined OpLiDaF, Open Linked Data Framework, a platform aimed the creation, structuring and visualizing data in RDF/XML. The paper discusses also the different formats, with special attention to procedures and techniques of processing data in relational databases, according to the instructions provided by the W3C Working Group RDB2RDF. It is working on the development of standard languages for mapping relational data and relational database schemas into RDF and OWL. The paper aims to show the potential of a language mapping between relational database schemas and ontologies implemented in RDF(S) or OWL, used in the platform OpLiDaF: the R2O (Relational to Ontology), which enables the production of data set extensible semantics explicit and well recognized.

XML Data Storage and Query Optimization in Relational Database by XPath Processing Model

XML is de facto new standard for data representation and exchanging on the web. Along with the growth of XML data, traditional relational databases support XML data processing across-the-board. Consistent storage and efficient query for XML data is the chief problem in XML supported relational databases. This work presents mechanisms of Storage and query optimization for XML data in relational database. XML data are treated as a kind of data type in relational database, and XML tables are used to store native XML data in fixed schema. Structural summary index is built and maintained in relational database and an optimizing mechanism based on XPath model named Compressed XML Query Tree will also be presented in order to improve efficiency of XML data query by reducing superabundant join operations from ancestor- descendent axis. All strategies are appropriate for classical XML query algorithms. Algorithms for XML query will be performed in experiments on real XML datasets in relational database and query workloads to report the performance of our mechanism and show the efficiency compared with other mechanisms.

Different Approaches to Modeling the Trees Data in Relational Database

This paper presents a detailed study of the work to modeling the hierarchies and data trees in relational database area. Hierarchical structures are used in diverse domains, in engineering and in research. We give a comparison between the various models cited in literature. This study allowed us to synthesize the most interesting of these models to represents the data trees. General Terms Relational database, tree data.

A novel method for improving the efficiency of automatic construction of ontology from a relational database

With the development of the semantic web, ontology is playing an increasingly important role in many research areas such as semantic interoperability and knowledge base. However, constructing ontology manually is complicated and needs the supports of domain experts in knowledge acquisition as well, so it is time-consuming, error-prone and tedious-work. Learning ontology from existing resources is a good solution. We can use relational database for building ontology, because relational database is widely used for storing data. This paper proposes an approach of learning ontology web language (OWL) from data in relational database. Compared with existing methods, our approach can acquire ontology from relational database automatically. In addition, our proposed method, unlike other existing methods, all types of relationships between tables are considered. The proposed method is implemented using Jena and MySQL and is applied on a sample relational database (RDB). The resulting ontology was shown as an OWL file. The evaluation of the generated ontology will use FaCT + + and Pellet. Key words: Ontology building, OWL, Relational database, Semantic web, mapping rules.

An Exploratory Study of K-Means and Expectation Maximization Algorithms

In this paper, K-Means and Expectation-Maximization algorithms are part of the commonly employed methods in clustering of data in relational databases. Experiments conducted with both clustering algorithms revealed that both algorithms have been found to be characterized with shortcomings. The parameters considered in evaluating the results of findings are the number of iterations (no distinct convergence, 1), the computation time (not defined, 3.2s) and the memory space (not defined, 1.1MB) consumed at the point of convergence of both K-means and Expectation-Maximization algorithms respectively. The results obtained revealed that Expectation-Maximization algorithm’s quick and premature convergence cannot be said to have guaranteed optimality of results while K-means was found not to guarantee convergence. Though reasonable conclusion could be drawn from results obtained with Expectation-Maximization algorithm, its premature convergence may raise some questions of doubt with regards to reliability of results obtained.

An Ontology as a Tool for Representing Fuzzy Data in Relational Databases

Abstract Several applications to represent classical or fuzzy data in databases have been developed in the last two decades. However, these representations present some limitations specially related with the system portability and complexity. Ontologies provides a mechanism to represent data in an implementation-independent and web-accessible way. To get advantage of this, in this paper, an ontology, that represents fuzzy relational database model, has been redefined to communicate users or applications with fuzzy data stored in fuzzy databases. The communication channel established between the ontology and any Relational Database Management System (RDBMS) is analysed in depth throughout the text to justify some of the advantages of the system: expressiveness, portability and platform heterogeneity. Moreover, some tools have been developed to define and manage fuzzy and classical data in relational databases using this ontology. Even an application that performs fuzzy queries using the same technology is ...

Storing XML Data – The ExDB and CellStore Way in the Context of Current Approaches

In this paper we describe possible approaches how to store XML data, which is a key aspect for their further processing. One popular technique for managing XML data is to map the data to an existing database system, e.g., to the relational or object-relational database management system. We describe possible ways how to store XML data in relational databases, because relational systems are still widely used for various purposes, including XML data management. But XML data are trees, not tables, so the main focus of this article is oriented to native XML databases. We describe general properties of such kind of databases and, in particular, explain possible solutions on two experimental native XML database management systems – ExDB and CellStore. Both have been proposed, implemented and optimized in our research groups in recent years for experimental purposes.

Generating database migrations for evolving web applications

WebDSL is a domain-specific language for the implementation of dynamic web applications with a rich data model. It provides developers with object-oriented data modeling concepts but abstracts over implementation details for persisting application data in relational databases. When the underlying data model of an application evolves, persisted application data has to be migrated. While implementing migration at the database level breaks the abstractions provided by WebDSL, an implementation at the data model level requires to intermingle migration with application code. In this paper, we present a domain-specific language for the coupled evolution of data models and application data. It allows to specify data model evolution as a separate concern at the data model level and can be compiled to migration code at the database level. Its linguistic integration with WebDSL enables static checks for evolution validity and correctness.

Mining Implied Semantics of Database

To mine richer semantics from relational database data, method of mining was discussed. Aiming at specific type of semantics, thirteen rules were proposed. Applying these rules some implied semantics was found naturally. The research shows that these rules have good maneuverability and high efficiency.

An Efficient Middleware for Storing and Querying XML Data in Relational Database Management System

Problem statement: In this study, we propose a middleware that provides a transformation utility for storing and querying XML data in relational databases using model mapping method. Approach: To store XML documents in RDBMS, several mapping approaches can be used. We chose structure independent approach. In this middleware the model mapping method XParent and free of cost available technologies MYSQL, PhpMyAdmin and PHPclasses are used as examples. Results: This middleware stores XML tables and does not require a direct extension of SQL thus this middleware can be used with any relational databases management system with little changes in the middleware interface. The middleware offers two alternative methods -namely XParent and XReal- for storing XML in the database. Conclusion: The key to proposed middleware is to store XML document in a relational database through a user interface and with an XPath query processor. We present a comparative experimental study on the performance of insertion and retrieval of two types of XML documents with a set of XPath queries executed though the XPath. XML and Relational databases cannot be kept separately because XML is becoming the universal standard data format for the representation and exchanging the information whereas most existing data lies in RDBMS and their power of data capabilities cannot be degraded so the solution to this problem a middleware prototype is required. The proposed schema dependent solutions have a drawback that evens a small change in the logical structure of XML documents influence on the database schemas and several problems occur during the updating process. A new efficient data middleware is proposed in the study to face these issues.

Extracting Conceptual Data Specifications from Legacy Information Systems

This article discusses the process of extraction of conceptual data specifications from relational database DDL code and data and source code of legacy information systems. Problems of legacy information systems and main solutions for them are briefly described here. Method of extracting conceptual data specifications from legacy information systems is provided. Comparison with other currently existing database reverse engineering methods is presented.

Learning Semantics from Relational Database Schema and Data

Ontology has been proposed to resolve the issues of semantic heterogeneity in data interoperability and data sharing among heterogeneous systems because it provides an effective approach to represent the knowledge about data of a specific domain and has been applied in many fields, such as Semantic Web, e-commerce and information retrieval, etc. However, building ontology for information source manually is not only hard and error-prone, but also very personal if there is no common guideline. We propose a framework for data interoperability using semantic Web service to resolve the semantic heterogeneity in healthcare environment. We found that learning ontology from existing information resources is a good solution to explicitly express the semantics of information source, but some semantics may be missing during the ontology learning process. Since relational database is widely used for storing source data, in this paper a new approach of learning OWL ontology from a relational database is proposed. In order to acquire the complete conceptual information, a group of learning rules are used to obtain OWL ontology, including classes, properties, property characteristics, cardinalities and instances. The proposed learning rules are being implemented as a prototype. KeywordsOntology; Ontology Learning; OWL; Relational Database; Relational Model