Fuzzy Object-oriented Database Research Articles

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.

Uncertainty Modeling of Object-Oriented Biomedical Information in HBase

While object-oriented databases (OODBs) are known to be rich in functionality, HBase database, which is a distributed and scalable big data store, as well as uncertain databases have recently gained a lot of attention in the database community. This paper presents a methodology for handling an important step of knowledge integrations and migrations. In particular, a formal approach for reengineering fuzzy object-oriented databases in HBase is firstly developed. The reengineering approach is based on the technique of rule-based schema mapping, which defines a set of transformation rules involved in the process of schema transformations for mapping a fuzzy object-oriented database schema into a fuzzy HBase database schema. In addition, a formal approach to map the fuzzy object-oriented algebra into fuzzy HBase algebra is proposed. On this basis, we complement the work with a comprehensive set of experiments to show the efficiency of our proposed approach in terms of query time and scalability metrics.

Mapping fuzzy RDF(S) into fuzzy object‐oriented databases

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object-oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

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)

Reverse engineering of Fuzzy OWL 2 ontologies to object-oriented database models

With the increase in demand of complex information modeling, object-oriented database models are put on the agenda. But information imperfection is inherent in the real-world applications. To deal with these complex imprecise and uncertain information, fuzzy object-oriented database (FOOD) and fuzzy OWL 2 ontology modeling are recently received more attention. But construction fuzzy ontology is a time-consuming and laborious task from scratch, reusing existing fuzzy ontology is an effective method of ontology construction. For the sake of reusing fuzzy OWL 2 ontologies, this paper proposes a reverse engineering approach for transforming fuzzyOWL2 Ontologies into FOOD models. And reverse engineering can shorten development cycles of ontology and various database models. On this basis, we propose formal definition of FOOD models and fuzzy OWL 2 ontologies. Furthermore, we give transformation rules and explain how to transform fuzzy OWL 2 ontologies into fuzzy FOOD models with an example in detail. The correctness of this transformation approach is proved. The advantage of reengineering fuzzy ontologies into FOOD models is the reusability of domain knowledge on the Web.

Reengineering Object-Oriented Fuzzy Spatiotemporal Data into XML

With the rapid development of the Internet, XML has become the defacto standard for integrating and exchanging data. Since more and more business data are stored in object-oriented database, we study the methodology of modeling fuzzy spatiotemporal data and transforming fuzzy spatiotemporal data from object-oriented databases to XML as well. In order to allow for better and platform independent sharing of fuzzy spatiotemporal data stored in an object-oriented format, we devise a fuzzy spatiotemporal data model in object-oriented database to capture the semantics of spatiotemporal features. In particular, XML schema best describes the existing fuzzy object-oriented schema, and we investigate the transforming rules of spatiotemporal data from fuzzy object-oriented database to XML. Furthermore, an instance demonstrates the validation of our approach. Such approach of transformation can provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from object-oriented databases to XML.

An intelligent multimedia information system for multimodal content extraction and querying

This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused to increase the accuracy of the object extraction process. The semantic contents that are extracted using the information fusion are stored in an intelligent and fuzzy object-oriented database system. In order to answer user queries efficiently, a multidimensional indexing mechanism that combines the extracted high-level semantic information with the low-level video features is developed. The proposed multimedia information system is implemented as a prototype and its performance is evaluated using news video datasets for answering content and concept-based queries considering all these modalities and their fused data. The performance results show that the developed multimedia information system is robust and scalable for large scale multimedia applications.

Definition membership function based on approach to hedge algebras

In this paper, we describe a hedge algebras based approach to modeling uncertainty in fuzzy object-oriented databases. Membership value reflects the degree of fuzziness existing in the data value and uncertainty is extended to the class definition level and is the basis for the determination of the membership of an object in a class. On this basis, we recommend methods of determining the membership degree on characteristics of fuzzy attributes, object/class, class/superclass and in addition, multiple inheritance was discussed and analyzed.

F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

In the literature, several studies have focused on introducing fuzzy extensions to the relational and/or object database models in order to store the imprecision. Indeed, on one hand, fuzzy EER and fuzzy UML are both applied for fuzzy object-oriented database modelling. On the other hand, Fuzzy ER is adapted for fuzzy relational database models. All these previous fuzzy conceptual modelling methods are not adapted to fuzzy spatiotemporal data. In this paper, we propose an approach for modelling imprecise data in object and relational databases based on the representation of data using connected and normalised fuzzy sets stored via α-cuts. The approach is applied to geographical information systems in order to handle imprecise spatiotemporal data.

Formalizing fuzzy object-oriented database models using fuzzy ontologies

Full implementation of the Semantic Web requires widespread availability of (fuzzy) ontologies. In this paper, we develop a complete approach and an automated tool for formalizing fuzzy object-oriented database (FOOD) models using fuzzy ontologies (i.e., constructing fuzzy ontologies from FOOD models), and investigate how to apply the constructed fuzzy ontologies to reason on FOOD models. Firstly, we propose a complete formal definition of FOOD models, which considers most features of FOOD models. On this basis, we propose an approach for constructing fuzzy ontologies from FOOD models. The approach can transform FOOD models (including schema and object instance information) into fuzzy ontologies. We also give full proof of correctness of the construction approach, and provide a full construction example. Furthermore, we implement a prototype tool called FOOD2FOnto, which can automatically construct fuzzy ontologies from FOOD models. Some tests demonstrate that our approach actually works. Finally, the constructed fuzzy ontologies are used to reason on the FOOD models, and some reasoning rules and a reasoning example are provided.

Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application

Spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems and environmental data management systems. For database researchers, modeling and designing a database of fuzzy spatiotemporal data and querying such a database efficiently have been challenging issues due to complex spatial features and uncertainty involved. This paper presents an integrated approach to modeling, indexing, and efficiently querying spatiotemporal data related to fuzzy spatial and complex objects and spatial relations. As our case study, we design and implement a meteorological database application that involves fuzzy spatial and complex objects, and a spatiotemporal index structure, and supports various types of spatial queries including fuzzy spatiotemporal queries. Our implementation is based on an intelligent database system architecture that combines a fuzzy object-oriented database with a fuzzy knowledge base.

Fuzzy Object Oriented Database versus FRDB for Uncertainty Management

It is impossible to capture all of the semantics of real world information and to model it in a perfect way. Uncertain information being lacking information, individual interpretation of knowledge is taken into account. Most widely used relational model faced much difficulty to handle complex data requirements. The size of databases and their changing composition such as graphics, video and sound, as well as numbers and text invited a reorganization of existing information systems. Object oriented database draw their strength from graphical user interfaces, advanced data management capabilities and powerful modeling techniques. So in order to deal with inexactness fuzzy techniques have been extensively integrated with more advanced object oriented database system which is capable to represent and manipulate the complex objects as well as complicated and uncertain relationship exiting among them. In this paper uncertainty using Fuzzy Object Oriented Database and Fuzzy Relational Data Base is illustrated.

Formal transformation from fuzzy object-oriented databases to fuzzy XML

XML has become the standard for publishing and exchanging data on the Web. Since most of the business data nowadays are stored in structured databases including relational and object-oriented databases (OODB), it is of significance to automate the transformation process and generate the XML data containing information from existing databases. At the same time, information imprecision and uncertainty exist in many practical applications, and for this reason, fuzzy data modeling has been extensively investigated in various data models. As such, there is an increasing need to effectively publish fuzzy structured data as fuzzy XML documents for Web-based applications. In this paper, we take a significant step in a fundamental consolidation of fuzzy XML. In particular, we are interested in finding an XML schema that best describes the existing fuzzy object-oriented schema. To accomplish this, we first offer mapping formalisms to capture the semantics of fuzzy XML Schema and fuzzy object-oriented schema. To allow for better and platform independent sharing of data stored in an object-oriented format, we investigate the formal transformation from fuzzy OODB to fuzzy XML and develop a set of rules to assist in the transformation process.

Fuzzy object-oriented databases model with hedge algebra based semantics.

In recent times, fuzzy object-oriented databases model has been studied in many different approaches such as fuzzy set theory, possibility theory, ... However, the mentioned approach are still limited in data performance and matching. In this paper, we propose a new approach to overcome the limitations of the approach that is based on hedge algebra structure for building fuzzy object-oriented databases model. Some operators fuzzy object-oriented relation algebra are proposed corresponding with the model. Finally,we propose a method of fuzzy object-oriented query processing flexibly.

Formal approach for reengineering fuzzy XML in fuzzy object-oriented databases

Since semi-structured documents (e.g., XML) could benefit greatly from database support and more specifically from object-oriented (OO) database management systems, we study the methodology of reengineering XML to object-oriented databases when database migration occurs in this paper. In particular, considering the need of processing the imprecise and uncertain information existing in practical applications, we investigate the problem of migrating fuzzy XML to fuzzy object-oriented databases. To find the object-oriented schema that best describes the existing fuzzy XML schema (DTD), we devise a comprehensive approach centering on a set of mapping rules. Such reengineering practices could not only provide a significant consolidation of the interoperability between fuzzy OO and fuzzy XML modeling techniques, but also develop the practical design methodology for fuzzy OO databases.

Algebraic operations in fuzzy object-oriented databases

The fuzzy object-oriented databases have been proposed to meet the need of dealing with fuzzy as well as complex objects. In this paper, we present a formal fuzzy object-oriented database model. Based on the semantic measure of fuzzy data, we first identify two kinds of fuzzy object redundancies, which are inclusion redundancy and equivalence redundancy, and then define three kinds of merging operation for redundancy removal. On the basis, we define some fuzzy algebraic operations for fuzzy classes and fuzzy objects. Finally, in the paper, we discuss fuzzy querying strategies and give the form of SQL-like fuzzy querying for the fuzzy object-oriented databases.

Comparison of entity with fuzzy data types in fuzzy object-oriented databases

Aiming at distinguishing entities, the concept of entity comparison plays an important role in object management in object-oriented databases as well as object-oriented knowledge bases. In order to model uncertain data and complex-valued attributes a

Modeling fuzzy information in UML class diagrams and object-oriented database models

Conceptual data modeling has become essential for non-traditional application areas. Some conceptual data models have been proposed as tools for database design and object-oriented database modeling. Information in real-world applications is often vague or ambiguous. Currently, a little research is underway on modeling the imprecision and uncertainty in conceptual data modeling and the conceptual design of fuzzy databases. The unified modeling language (UML) is a set of object-oriented modeling notations and a standard of the object management group (OMG) with applications to many areas of software engineering and knowledge engineering, increasingly including data modeling. This paper introduces different levels of fuzziness into the class of UML and presents the corresponding graphical representations, with the result that UML class diagrams may model fuzzy information. The fuzzy UML data model is also formally mapped into the fuzzy object-oriented database model.

A description logic approach for representing and reasoning on fuzzy object-oriented database models

With the wide utilization of description logics ( DLs), researches on applications of DLs have become the important research lines in the Semantic Web. In particular, DLs have been deeply studied in database modeling. This paper proposes a description logic approach to represent and reason on fuzzy object-oriented database (FOOD) models. After recalling some basic notions of FOOD models and a fuzzy DL called f-ALCIQ( D), we propose a kind of formal definition and semantics of FOOD models. On this basis, representation and reasoning of FOOD models with f-ALCIQ( D) is studied. We first translate a FOOD model and its corresponding database instance into an f-ALCIQ( D) knowledge base (i.e., TBox and ABox), and further implement a prototype translation tool called FOOD2DL. Then, based on the translated f-ALCIQ( D) knowledge bases, we investigate how to reason on FOOD models (e.g., consistency, subsumption, and redundancy) through the reasoning mechanism of f-ALCIQ( D). The formalization in f-ALCIQ( D) of FOOD models makes it possible to improve the ability of reasoning on database models by means of the inference services offered by fuzzy DLs.

An intelligent fuzzy object-oriented database framework for video database applications

Video database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge-based technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this study, firstly, we introduce a fuzzy conceptual data model to represent the semantic content of video data. For that purpose, UML (unified modeling language) is utilized and extended to represent uncertain information along with video specific properties. Secondly, we present an intelligent fuzzy object-oriented database framework for video database applications. The introduced fuzzy conceptual model is used in this framework, which provides modeling of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it supports various flexible queries including (fuzzy) semantic, temporal and (fuzzy) spatial queries, based on the video data model. We think that the presented conceptual data model and the framework can be used for any video database application.