TOWARDS A UNIFIED PRINCIPLE FOR REASONING ABOUT HETEROGENEOUS DATA: A FUZZY LOGIC FRAMEWORK

2012 ◽  
Vol 20 (02) ◽  
pp. 281-302 ◽  
Author(s):  
LYAMINE HEDJAZI ◽  
JOSEPH AGUILAR-MARTIN ◽  
MARIE-VERONIQUE LE LANN ◽  
TATIANA KEMPOWSKY
Keyword(s):  
Fuzzy Logic ◽  
Heterogeneous Data ◽  
Learning Problems ◽  
Characteristic Functions ◽  
Partial Knowledge ◽  
Process Variables ◽  
Automatic Reasoning ◽  
Multiple Data ◽  
Monitoring Process ◽  
Data Representations

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

A Smart Grid Data Global Placement Strategy Based on Cloud Computing

2014 ◽  
Vol 644-650 ◽  
pp. 3256-3259
Author(s):  
Yu Kai Li ◽  
Di Xin ◽  
Hong Gang Liu
Keyword(s):  
Cloud Computing ◽  
Smart Grid ◽  
Heterogeneous Data ◽  
Simulation Software ◽  
Network Computing ◽  
Grid Data ◽  
Data Intensive ◽  
Multiple Data ◽  
Rational Distribution ◽  
Global Placement

Cloud computing is a typical network computing model.Large-scale network applications based on cloud computing presents the characteristics and trends of distributed, heterogeneous data-intensive.Smart grid is a complex system running a real-time online.It is a data-intensive applications.How to effectively integrate multiple data centers in the smart grid and let them work together in the cloud computing environment is a question.And how to make rational distribution of data in the smart grid is also a question.Therefore, we propose a global placement strategy based on genetic algorithm.And we give the data placement scheme for solving on data-intensive applications.Through simulation software CloudSim, we conducted simulation experiments and analyzed the effectiveness of the program.

scholarly journals Influence of process variables on quality attributes of jaggery prepared by vacuum pan evaporation technology

2020 ◽  
Vol 13 (2) ◽  
pp. 188-194
Author(s):  
Eresh Kumar Kuruba ◽  
P.V.K. Jagannadha Rao ◽  
D. Khokhar ◽  
S. Patel
Keyword(s):  
Fuzzy Logic ◽  
Moisture Content ◽  
Variable Temperature ◽  
Quality Attributes ◽  
Cane Sugar ◽  
Sugarcane Juice ◽  
Vacuum Pressure ◽  
Pan Evaporation ◽  
Process Variables ◽  
Fuzzy Logic Method

Jaggery is a solid unrefined, non- centrifugal cane sugar (NCS) with unique colour, flavor and aroma obtained from crushing of cane and evaporating of sugarcane juice. In this paper vacuum pan evaporation method were used sugarcane juice boiling at vacuum pressure (Vp : 500-700 mm of Hg), time (t:60-90 min) and temperature (T:100-1200 C). The quality attributes of jaggery developed from vacuum pan evaporator were investigated at different process variables. The developed jaggery were analyzed for physiochemical. Results showed that TSS (0 Brix) and Hardness (Hd) increased with increase in vacuum pressure and time, whereas moisture content percentage (%) and water activity (aw) decreased with increase in vacuum pressure and time at variable temperature of 1100 C. Fuzzy logic method was used to evaluate the sensory characteristic of prepared jaggery.

Integrating Heterogeneous Data for Big Data Analysis

2014 ◽  
pp. 263-288 ◽  
Author(s):  
Richard Millham
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
New Paradigm ◽  
Heterogeneous Data Integration ◽  
Multiple Data ◽  
Global Schema ◽  
Optimal Resource ◽  
Data Virtualization

Data is an integral part of most business-critical applications. As business data increases in volume and in variety due to technological, business, and other factors, managing this diverse volume of data becomes more difficult. A new paradigm, data virtualization, is used for data management. Although a lot of research has been conducted on developing techniques to accurately store huge amounts of data and to process this data with optimal resource utilization, research remains on how to handle divergent data from multiple data sources. In this chapter, the authors first look at the emerging problem of “big data” with a brief introduction to the emergence of data virtualization and at an existing system that implements data virtualization. Because data virtualization requires techniques to integrate data, the authors look at the problems of divergent data in terms of value, syntax, semantic, and structural differences. Some proposed methods to help resolve these differences are examined in order to enable the mapping of this divergent data into a homogeneous global schema that can more easily be used for big data analysis. Finally, some tools and industrial examples are given in order to demonstrate different approaches of heterogeneous data integration.

scholarly journals Locally Linear Embedding Orthogonal Projection to Latent Structure

2022 ◽  
pp. 189-209
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Time Delay ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Latent Structure ◽  
Economic Losses ◽  
Locally Linear Embedding ◽  
Process Variables ◽  
Partial Least Squares Analysis ◽  
Monitoring Process ◽  
Linear Embedding

AbstractQuality variables are measured much less frequently and usually with a significant time delay by comparison with the measurement of process variables. Monitoring process variables and their associated quality variables is essential undertaking as it can lead to potential hazards that may cause system shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least squares analysis (PLS) (Kruger et al. 2001; Song et al. 2004; Li et al. 2010; Hu et al. 2013; Zhang et al. 2015).

scholarly journals Category Classification of Tree States Using Visual Data Based on Fuzzy Logic

2020 ◽  
pp. 2-11
Author(s):  
Aleksey Pyataev ◽  
Margarita Favorskaya
Keyword(s):  
Fuzzy Logic ◽  
Intelligent System ◽  
Objective Assessment ◽  
Forest Monitoring ◽  
Characteristic Functions ◽  
Linguistic Variables ◽  
Visual Data ◽  
Forest Protection ◽  
Crown Density ◽  
Tree Condition

Introduction: A reliable assessment of tree condition directly affects the planning of economic indicators for the use of forest resources and ecological actions for forest protection. Therefore, the correct evaluation of the sanitary state of forest is very important. At present, the decisions that forest pathologists make about classifying trees or forest areas are based on visual inspection and their subjective knowledge about the tree features. Purpose: Development of a method for classifying the condition of a tree in terms of its crown density degree and other features, based on fuzzy logic with characteristic functions for linguistic variables such as “Crown density”, “Annual branch growth”, “Bark falling off” or “Shrinking branches”. Results: The proposed method classifies the tree condition using pine as an example. The method consists in preliminary image processing, including the removal of background objects, extraction of texture features as extended binary patterns, and application of a specially designed controller based on fuzzy logic. We propose four types of linguistic variables, with their respective terms. For these variables, characteristic functions are specified in tabular form and then approximated by smooth functions. A fuzzy logic controller allows you to obtain an objective assessment of the tree crown condition. Experimental studies confirm the effectiveness of the developed method. Practical relevance: The intelligent system of classifying the tree condition according to visual data can provide a significant support to plantation survey specialists. The proposed method allows you to improve the quality of forest monitoring, minimize the influence of human factor, and organize the forest protection in the best possible way.

Importance of Big Data

2015 ◽  
pp. 1-19 ◽  
Author(s):  
Seema Ansari ◽  
Radha Mohanlal ◽  
Javier Poncela ◽  
Adeel Ansari ◽  
Komal Mohanlal
Keyword(s):  
Big Data ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Data Types ◽  
It Industry ◽  
Management Tools ◽  
Location Data ◽  
Processing Power ◽  
Multiple Data ◽  
Big Data Applications

Combining vast amounts of heterogeneous data and increasing the processing power of existing database management tools is no doubt the emerging need of IT industry in coming years. The complexity and size of data sets that need to be acquired, analyzed, stored, sorted or transferred has spiked in the recent years. Due to the tremendously increasing volume of multiple data types, creating Big Data applications that can extract the valuable trends and relationships required for further processes or deriving useful results is quite challenging task. Companies, corporate organizations or be it government agencies, all need to analyze and execute Big Data implementation to pave new paths of productivity and innovation. This chapter discusses the emerging technology of modern era: Big Data with detailed description of the three V's (Variety, Velocity and Volume). Further chapters will enable to understand the concepts of data mining and big data analysis, Potentials of Big Data in five domains i.e. Healthcare, Public sector, Retail, Manufacturing and Personal location Data.

Rehabilitation Management System Oriented Cloud Services Integration Technology

2013 ◽  
Vol 415 ◽  
pp. 371-376
Author(s):  
Yun Long Ma ◽  
Xiao Hua Chen ◽  
Bo Liu ◽  
Guo Feng Zhang
Keyword(s):  
Data Exchange ◽  
Heterogeneous Data ◽  
Cloud Services ◽  
Rehabilitation Management ◽  
Integration Technology ◽  
Application Systems ◽  
Multiple Data ◽  
Services Integration ◽  
Integrated Architecture ◽  
Platform System

As the informationization of the medicare sector in China grows up and the sources of the rehabilitation data are distributed and multi-leveled from different centers, it is urgently needed to integrate the resources of various application systems, implement unified data exchange of the distributed and heterogeneous multiple data sources and encapsulate the data resources into various kinds of services. For this purpose, this paper puts forward the idea of SOA-based cloud services encapsulation and integrated architecture. At first, the multi-source and heterogeneity of data and the integration of information in the rehabilitation management are analyzed. Then, the cloud services encapsulation and integration technology is study to find out how to encapsulate data resources into various kinds of services, build a SOA-based cloud services platform, find a solution for the integration of multi-sourced and heterogeneous data, and meet the function extension, encapsulation and release of cloud services arising out of changing demands. At last, A case of the cloud platform system is presented to show the feasibility and effectiveness of the architecture platform.

scholarly journals Processing on Structural Data Faultage in Data Fusion

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Fan Chen ◽  
Ruoqi Hu ◽  
Jiaoxiong Xia ◽  
Jie Tao
Keyword(s):  
Data Fusion ◽  
Rapid Development ◽  
Structural Data ◽  
Information Management System ◽  
Heterogeneous Data ◽  
Data Errors ◽  
Inaccurate Data ◽  
Multiple Data ◽  
Data Tables ◽  
New Perspective

With the rapid development of information technology, the development of information management system leads to the generation of heterogeneous data. The process of data fusion will inevitably lead to such problems as missing data, data conflict, data inconsistency and so on. We provide a new perspective that combines the theory in geology to conclude such kind of data errors as structural data faultage. Structural data faultages after data integration often lead to inconsistent data resources and inaccurate data information. In order to solve such problems, this article starts from the attributes of data. We come up with a new solution to process structural data faultages based on attribute similarity. We use the relation of similarity to define three new operations: Attribute cementation, Attribute addition, and Isomorphous homonuclear. Isomorphous homonuclear uses digraph to combine attributes. These three operations are mainly used to handle multiple data errors caused by data faultages, so that the redundancy of data can be reduced, and the consistency of data after integration can be ensured. Finally, it can eliminate the structural data faultage in data fusion. The experiment uses the data of doctoral dissertation in Shanghai University. Three types of dissertation data tables are fused. In addition, the structural data faultages after fusion are processed by the new method proposed by us. Through the statistical analysis of the experiment results and compare with the existing algorithm, we verify the validity and accuracy of this method to process structural data faultages.

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