Fuzzy Object-Oriented Database for Big Data

Due to the huge amount of uncertain data collection, mankind facing a colossal amount and fast data having such a composite configurations. The data can be transact on the web and manually exchanges, interpersonal organizations and through our everyday life exercises. A very helpful result of designing an appropriate Big Data in different zones, for example, medical services, human services, executives and administrators. Managing the Big Data efficiently, new investigation perspective has been secured at this point the viewpoints about huge information (Big Data) requires extra-long stretch innovative interests. The Fuzzy Logic s have been implemented here for Big Data because of their capacities to handle the vagueness and uncertainties in the information. A couple of imaginative approach for Big Data processing is presented previously. To abbreviate the current duties and extant a point of view of further enhancements. We survey the different examinations and concentrated on that there are constrained Fuzzy systems have been adopted in large information (Big Data preparing. We also examine the various advantages of Fuzzy Logic s in large information (Big Data) issues. Therefore in this paper a Fuzzy object-oriented database is designed for Big Data and perform some Fuzzy queries to check the performance of the designed Fuzzy object-oriented database. We focused on the continuously propelled augmentations of Fuzzy sets and their blends in with various contraptions could offer a novel promising planning condition

A Narrative Review of Storing and Querying XML Documents Using Relational Database

Extensible Markup Language (XML) has become a common language for data interchange and data representation in the Web. The evolution of the big data environment and the large volume of data which is being represented by XML on the Web increase the challenges in effectively managing such data in terms of storing and querying. Numerous solutions have been introduced to store and query XML data, including the file systems, Object-Oriented Database (OODB), Native XML Database (NXD), and Relational Database (RDB). Previous research attempts indicate that RDB is the most powerful technology for managing XML data to date. Because of the structure variations of XML and RDB, the need to map XML data to an RDB scheme is increased. This growth has prompted numerous researchers and database vendors to propose different approaches to map XML documents to an RDB, translating different types of XPath queries to SQL queries and returning the results to an XML format. This paper aims to comprehensively review most cited and latest mapping approaches and database vendors that use RDB solution to store and query XML documents, in a narrative manner. The advantages and the drawbacks of each approach is discussed, particularly in terms of storing and querying. The paper also provides some insight into managing XML documents using RDB solution in terms of storing and querying and contributes to the XML community.

The computing and information system for research of prospective electric power grids expansion

The paper describes the software tool implemented by Melentiev Energy Systems Institute SB RAS, aimed to solve wide range of energy issues. In this article, the Computing and Information System (CIS) means a software tool that provides collection, transfer, processing, storage, geo-visualization, and output of digital technical and economic data of different energy/power entities. Besides, this tool is incorporated within a mathematical model for optimization of expansion and operating modes of power systems. The paper discusses the example of how data storage and data representation in object-oriented database assist to improve efficiency of research prospective electric power systems expansion and operation.

Clustering and Query Optimization in Fuzzy Object-Oriented Database

The purpose of the clustering method is to provide some meaningful partitioning of the data set. In general, finding separate clusters with similar members is essential. A problem in clustering is how to determine the number of optimal clusters that best fits the data set. Most clustering algorithms generate a partition based on input parameters (for example, cluster number, minimum density) which results in limiting the number of clusters. Therefore, the article proposes an improved EMC clustering algorithm that is more flexible in handling and manipulating those clusters, where input parameter values are assumed to be different clusters for different partitions of a data set. In addition, based on the above partitioning results, this article proposes a new approach to processing and optimizing fuzzy queries to improve efficiency in the manipulation and processing of specific data such as (less time consuming, less resource consuming)