The purpose of the proposed paper is that analysis of uncertainty and impression handling in fuzzy Relational Data Base by defining the fuzzy relationships between the existing database model to Fuzzy Vague Relational Database Model (FRDBMS) with the help of fuzzy membership function for analysis of the degree of uncertain information of existing data base. Some theoretical properties of the mo...

1. Introduction Given the fundamental role played by joins in querying relational databases, it is not surprising that stream join has also been the focus of much research on streams. Recall that relational (theta) join between two non-streaming relations R1 and R2, denoted RlweR2, returns thesetofallpairs (rl, r2), whererl E R1, 7-2 E R2, and the join condition 8(rl, r2) evaluates to true. A s...

Mining frequent patterns from traditional database is an important research topic in data mining and researchers achieved tremendous progress in this field. However, with high volumes of uncertain data generated in distributed environments in many of biological, medical and life science application in the past ten years, researchers have proposed different solutions in extending the conventiona...

Uncertainty in data is caused by various reasons including data itself, data mapping, and data policy. For data itself, data are uncertain because of various reasons. For example, data from a sensor network, Internet of Things or Radio Frequency Identification is often inaccurate and uncertain because of devices or environmental factors. For data mapping, integrated data from various heterogono...

Despite the recent surge of research in query processing over data streams, little attention has been devoted to defining precise semantics for continuous queries over streams. We first present an abstract semantics based on several building blocks: formal definitions for streams and relations, mappings among them, and any relational query language. From these basics we define a precise interpr...

Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. We give here an introduction to the StreamQRE language, which has recently been proposed for the purpose of simplifying the task of programming the desired logic in such stream processing applications. StreamQRE provides natura...

A major goal for database research has been the incorporation of additional semantics into database models. It is recognized that the relational database model has semantic and structured drawbacks when it comes to modeling some emerging applications such as computer aided design (CAD), geographical information systems (GIS), and artificial intelligence. In response to this problem, some attemp...

In this paper we examine the possibilities to extend the relational data model with the mechanisms that can handle imprecise, uncertain and inconsistent attribute values using fuzzy logic and fuzzy sets. We present a fuzzy relational data model which we use for fuzzy knowledge representation in relational databases that guarantees the model in 3 rd normal form. We also describe the CASE tool fo...

Orion is a state-of-the-art uncertain database management system that extends the relational model to include probabilistic uncertain data as first call data types. This demonstration presents an implementation of this system as an extension of PostgreSQL. The Orion model is capable of supporting both attribute and tuple uncertainty with arbitrary correlations. Both discrete and continuous pdfs...