Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

Fuzzy grey relational analysis for software effort estimation

Abstract: Accurate and credible software effort estimation is a challenge for academic research and software industry From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA) Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k th continuous feature $$ \left( {\left| {\left( {{x_o}(k) - {x_i}(k)} \right} \right|} \right) $$ GRA is a problem solving method that is used to assess the similarity between two tuples with M features Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features Both techniques are suitable when relationship between effort and other effort drivers is complex Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods

Efficient fuzzy match for evaluating data records

Abstract: To help ensure high data quality, data warehouses validate and clean, if needed incoming data tuples from external sources. In many situations, input tuples or portions of input tuples must match acceptable tuples in a reference table. For example, product name and description fields in a sales record from a distributor must match the pre-recorded name and description fields in a product reference relation. A disclosed system implements an efficient and accurate approximate or fuzzy match operation that can effectively clean an incoming tuple if it fails to match exactly with any of the multiple tuples in the reference relation. A disclosed similarity function that utilizes token substrings referred to as q-grams overcomes limitations of prior art similarity functions while efficiently performing a fuzzy match process.

Reactive Tuple Spaces for Mobile Agent Coordination

Abstract: The paper surveys several coordination models for mobile agent applications and outlines the advantages of uncoupled coordination models based on reactive blackboards. On this base, the paper presents the design and the implementation of the MARS system, a coordination tool for Java-based mobile agents. MARS defines Linda-like tuple spaces that can be programmed to react with specific actions to the accesses made by mobile agents.

Object Distinction: Distinguishing Objects with Identical Names

Abstract: Different people or objects may share identical names in the real world, which causes confusion in many applications. It is a nontrivial task to distinguish those objects, especially when there is only very limited information associated with each of them. In this paper, we develop a general object distinction methodology called DISTINCT, which combines two complementary measures for relational similarity: set resemblance of neighbor tuples and random walk probability, and uses SVM to weigh different types of linkages without manually labeled training data. Experiments show that DISTINCT can accurately distinguish different objects with identical names in real databases.

Intelligent Rollups in Multidimensional OLAP Data

Abstract: In this paper we propose a new operator for advanced exploration of large multidimensional databases. The proposed operator can automatically generalize from a specific problem case in detailed data and return the broadest context in which the problem occurs. Such a functionality would be useful to an analyst who after observing a problem case, say a drop in sales for a product in a store, would like to find the exact scope of the problem. With existing tools he would have to manually search around the problem tuple trying to draw a pattern. This process is both tedious and imprecise. Our proposed operator can automate these manual steps and return in a single step a compact and easy-to-interpret summary of all possible maximal generalizations along various roll-up paths around the case. We present a fle xible cost-based framework that can generalize various kinds of behaviour (not simply drops) while requiring little additional customization from the user. We design an algorithm that can work efficiently on large multidimensional hierarchical data cubes so as to be usable in an interactive setting.