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Data Fusion

Meier, Ch. · elib (German Aerospace Center) · 2002

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Data fusion

Jens Bleiholder, Felix Naumann · ACM Computing Surveys · 2009

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation. This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Cited by 1,825

Data Fusion

Amy Braverman · Encyclopedia of Quantitative Risk Analysis and Assessment · 2008

Abstract We define data fusion as the process of combining statistically heterogeneous samples from marginal distributions to construct a new sample that can be regarded as having come from the unobserved joint distribution of interest. This problem has been addressed in the literature on statistical matching, which also appeals to the missing data paradigm of Little and Rubin. Though less obvious, spatial statistics also offers techniques that facilitate rigorous data fusion by inferring unobserved values in multiple data sets at specific locations. We touch just briefly on the topic of copulas. Copulas have yet to be exploited in this context, but the link is clear given our definition of data fusion. Finally, we consider some challenges posed by data fusion, and maybe more importantly by the absence of data fusion.

Cited by 3

Data fusion

Veronika Janíková · Digital Repository (National Repository of Grey Literature) · 2014

This bachelor's thesis deals with a data fusion, which is a one of the possible solutions to the common problem of data availability in praxis. In the first part, practical use of data fusion, especially in marketing, fundamental algorithms and data fusion problems are discussed. The main part of this thesis deals with the so-called "unconstrained statistical fusion". Firstly, one of the possible processes of this type of fusion is described theoretically in detail. This process involves branching into four different types of data fusion. Next, a method of theoretical evaluating the quality of the general fusion model is designed using statistical indicators. The practical part of the thesis contents processes of four types of unconstrained statistical fusion and their evaluation which both are programmed in statistical program R. Furthermore, the fusion is applied to our artificially generated database and also to a real data collected in praxis by Czech public opinion research company Median. In the very last part of the thesis, the results of fusions applied to these two databases are interpreted, evaluated and discussed.

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Data Fusion

Fay Huang, Reinhard Klette, Karsten Scheibe · 2008

This chapter contains sections titled: Determination of Camera Image Coordinates Texture Mapping High Resolution Orthophotos Fusion of Panoramic Images and Airborne Data Exercises Further Reading

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Data fusion

Albert, Veres, Aba, Erôss · CERN Document Server (European Organization for Nuclear Research) · 2017

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Data Fusion

Beatrice Campanella, Stefano Legnaioli · 2022

This chapter offers a brief overview of the current literature regarding the combination of chemometric algorithms and data fusion techniques involving LIBS and Raman spectroscopies. The two techniques allow complementary information on both the molecular structure and elemental composition of a sample under study. Furthermore, when measured simultaneously, they consent to improve characterization of complex matrixes. After a brief introduction where the concept of “data fusion” is explained, including the main data treatments used, some examples are reported illustrating how LIBS + Raman data fusion strategies allow to improve the classification of samples of different origin.

Cited by 1

Data fusion

2012

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Data Fusion

Harvey B. Mitchell · 2026

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Data Fusion

Oscar Ricardo Vergara · 2007

INTRODUCTION Data fusion is a generic word referring to several techniques of digital image processing which are used to integrate data from different spatial and spectral resolution in order to obtain higher-quality synthetic images, which facilitate the process of thematic data extraction by means of using visual interpretation (Vergara et al., 1996). The aim of data fusion is to fit the characteristics of spatial and spectral high resolution presented separately by the original images together in just one image. From the eighties onwards, several researchers have tested different techniques to generate synthetic images with the aforementioned properties. Henceforth a brief comment upon some papers presented in this meantime shall be made. These papers will be then grouped according to the methodology employed. The purpose of this comment is to provide the reader with references to research carried out in this field (both in Brazil and abroad) so as to orient the user in relation

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DATA FUSION

Edward J. Wegman · Elsevier eBooks · 1982

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Data fusion

Alamgir Hossain, Hard Combination · 2012

This paper presents a study of hard combination data fusion for cooperative spectrum sensing in Cognitive Radio (CR). We evaluated the performance of cooperative spectrum sensing with the hard combination OR, AND and MAJORITY rules. Energy detection technique is used to sense the presence of primary user (PU) signal. Simulation result shows that cooperative spectrum sensing with OR rule is the best among hard combination data fusion in Cognitive Radio and gives the better performance than AND and MAJORITY rules. Keyword:

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Data Mining, Data Fusion

Katherine Chiang · eCommons (Cornell University) · 2010

Data mining and data fusion are two complementary approaches to processing dynamic, large and/or heterogeneous data. They are already used in various research disciplines and real?world applications and that use will grow. This presentation describes and defines these approaches using examples, and suggests possible services and applications by libraries.

Cited by 2

Key words: image fusion, data fusion, information fusion, multisensor fusion.

Prof Dr, Yaonan Wang, Prof Dr. -ing et al. · 2008

Abstract: Multisensor image fusion is a process of combining or amalgamating information from multiple sensors. It has been applied to a wide variety of fields such as navigation, military surveillance, remote sensing, medical diagnosis, industrial process control and measurement, intelligent robot, and law enforcement. In this paper, the basic concept, advantage, general structure, methods, applications, and performance evaluation of multisensor image fusion are presented.

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From Data Fusion to Knowledge Fusion

Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz et al. · arXiv (Cornell University) · 2015

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.

Cited by 3Open access

Multisensor Data Fusion

Hugh Durrant‐Whyte, Thomas C. Henderson · Springer handbooks · 2016

Cited by 190

Multisensor data fusion

Barshan, Billur · Bilkent University Institutional Repository (Bilkent University) · 2015

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Guided Data Fusion

Romila Pradhan · Purdue e-Pubs (Purdue University System) · 2018

While the volume and variety of data furnished by disparate data sources has rocketed over the years, often there is little to no restraint over the quality of data available on the Internet; data sources often provide conflicting information for the same data item (a real-world entity or event). Recent years have witnessed a number of data fusion systems that propose solutions to consolidate multiple instances of a data item, distinguish correct from incorrect information and present a unified, consistent and meaningful record to users. Most of these fusion systems are focused on automatically identifying correct information for data items. Despite their remarkable effectiveness in resolving conflicts, these fusion systems are not error-free and incorrect interpretation on certain data items quickly propagate as false judgement on other items. This dissertation studies techniques to incorporate user feedback and capitalize on the knowledge of relationships among claims of data items to improve the effectiveness of conflict resolution. In particular, the dissertation addresses two key challenges toward guided data fusion. The first challenge relates to integrating feedback from users to rapidly resolve conflicts. The objective is to effectively and efficiently integrate user feedback for maximum benefit to data fusion. For this purpose, we develop a novel framework built on the principles of decision theory and active learning to reason about the order in which claims should be validated by users. We propose approaches that exploit the structure of interactions between data items and sources and offer interactive validation time for users of a data fusion system. The second challenge relates to leveraging relations between claims of data items to identify multiple related correct claims. The objective is to recognize existing entity-relationships among claims and integrate them with data fusion systems that are agnostic to data relationships. Toward this goal, we leverage knowledge representations that encapsulate a wide range of relationship semantics and introduce mechanisms to integrate the knowledge representation with data fusion models to retrieve multiple correct claims that are consistent with each other. Our experimental evaluations using real-world and synthetic datasets demonstrate the effectiveness and efficiency of our proposed approaches to improve conflict resolution of data integrated from multiple sources.

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Multisensor Data Fusion

A. Nejat İnce, Ercan Topuz, Erdal Panayırcı et al. · 1998

Cited by 38

Multisensor Data Fusion

David L. Hall, James Llinas · 2001

The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut

Cited by 370