Informatics Publishing Limited

V. Sitha Ramulu ¹, B. Raveendra Babu ²

Affiliations
1 Department of Computer Science and Engineering, SBIT, Khammam, IN
2 Department of CSE, Dean-Administration and Finance, VNRVJIET, Hyderabad, IN

Abstract

Background and Objectives: Relational Databases have got such a great importance in the current global enterprises, such that they are widely and extensively used for storing and managing data. The objective includes the comparison techniques with older approaches and present approach depicts best accessibility to read/write access to the data. The data semantics are not directly imposed on the relational schema. In fact, they are direct towards the application level. Methods/Statistical Analysis: There are several mapping techniques involved by many semantic web technologies and several ontologies that enhance data utilization among many applications. The techniques also embed data reusability for many number of times across community boundaries, large enterprises etc. The current strategies depend on the Read only access either through Linked data or SPARQL. The implemented approach underwent the test cases with various instances using interfaces. The interfaces falling under semantic web frameworks for accessing of data by these approaches include RDF2GO: ChangeSet, Sesame and Jena. Findings: The feasibility factors for conversion of relational data schema to the RDF do not give satisfactory remarks. Hence, to increase the feasibility scope for conversions, we used and adopted an approach called "Mediation Approach" for converting RDF's to RDB's. In the current context we present an extensible platform called RDB-to-RDF Ingression (R2RI) that supports Read/write access to the data residing in the relational schema. The entitled platform uses the technique of "encapsulation" as the translation logic during translation process in the core layer. This translation logic proves itself to be the foundation for the rest of the interfaces existing in the interface layer. At later stages, we exposed the general concepts about the architectural patterns, mappings of RDB-to-RDF and emphasizing special focus on our mediation platform "R2RI" duly enhancing a protocol to provide parsing feedback protocol to the client. We also present the prototype implementation process followed by its performance calculations.

Keywords

Relational Databases, Resource Description Framework (RDF), Semantic Web.

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V. Sitha Ramulu ¹, B. Raveendra Babu ²

Affiliations
1 Department of Computer Science and Engineering, SBIT, Khammam - 507002, Telangana, IN
2 Department of CSE, VNRVJIET, Hyderabad - 500090, Telangana, IN

Abstract

Background/Objectives: Software engineering is a state of art providing enormous solutions to the clients. Software engineering methodologies keeps on changing from day to day. This changing nature of software gave place for development of new languages, technologies, new tools in the software applications and many frame works that gained speed in replacing older systems. Hence, the main objective in presenting the paper defines in "bridging" the older software to the latest version new technology software. This enables data to meet accurate requirements in data mining from the data bases. Methods/Statistical Analysis: In the storage of databases, the relational databases are used utmost for storing data in very large amount which should be with good and high integrated security to safe guard the data semantics. In order to support the objective, we have introduced an R2R Ingression approach - a methodology to bridge old systems to new technology software in RDB and RDF with various mappings and architectural patterns that duly define the bridging process for RDB and RDF. Findings: A clear and contrast elaboration have been extracted between the two distinguished example softwares as presented in our context. We stated the relations of old systems and new systems softwares with the example mappings using R2RI, so that legacy softwares can be easily transparent to the new technology softwares. This kind of bridging is so essential to enhance the usage of old legacy systems at the current latest technologies. A Semi automation tool is apprehended and transformed to a complete automation tool to give additional benefits to the user as well as the administrator. Applications/Improvements: This paper covers the development of a new methodology called R2RI which is an RDB-to-RDF mapping with integration approach that contain architecture and mapping which enables the RDF-based Read/Write access to enrich the various characteristics of RDB. So the R2R ingression mapping helps to align the RDB and RDF.

Keywords

Relational Database, Resource Description Framework.

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Ch. N. Santhosh Kumar ¹, V. Sitha Ramulu ¹, K. Sudheer Reddy ², Suresh Kotha ¹, Ch. Mohan Kumar ¹

Affiliations
1 Dept. of CSE, Swarna Bharathi Inst. of Sc. & Tech, Khammam, A.P., IN
2 Dept. of CSE, Acharya Nagarjuna University, Guntur, A.P., IN

Abstract

Data mining, which is refers to as Knowledge Discovery in Databases(KDD), means a process of nontrivial exaction of implicit, previously useful and unknown information such as knowledge rules, descriptions, regularities, and major trends from large databases. Data mining is evolved in a multidisciplinary field , including database technology, machine learning, artificial intelligence, neural network, information retrieval, and so on. In principle data mining should be applicable to the different kind of data and databases used in many different applications, including relational databases, transactional databases, data warehouses, object-oriented databases, and special application-oriented databases such as spatial databases, temporal databases, multimedia databases, and time-series databases. Spatial data mining, also called spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are the data that have spatial or location component, and they show the information, which is more complex than classical data. A spatial database stores spatial data represents by spatial data types and spatial relationships and among data. Spatial data mining encompasses various tasks. These include spatial classification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules, trend detection. This paper presents how spatial data mining is achieved using clustering.

Keywords

Clustering, Database, Data Mining, Spatial Data.

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