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An Overview on Data Mining and Data Fusion
Indian Journal of Data Mining, 2023 Strong adoption of Internet and Communication technologies across industries in the last two decades has led to large-scale digitization of business processes. While this has helped in the instant availability of information, over the period, the source and amount of this information have increased multi-fold giving rise to Big Data.
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A Review of Data Fusion Techniques [PDF]
The Scientific World Journal, 2013 The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed.
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, 2010 We consider the problem of specifying data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies; a novel fuse operation on relational indexes specifies where the underlying physical data structure
Hawkins, Peter +4 more
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City Data Fusion: Sensor Data Fusion in the Internet of Things
, 2015 Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor ...
Wang, Meisong +5 more
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, 2021 The recent resurgence of Artificial Intelligence (AI), specifically in the context of applications like healthcare, security and defense, IoT, and other areas that have a big impact on human life, has led to a demand for eXplainable AI (XAI). The production of explanations is argued to be a key aspect of achieving goals like trustworthiness and ...
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Probabilistic Methods for Data Fusion [PDF]
, 1998 The main object of this paper is to show how we can use classical probabilistic methods such as Maximum Entropy (ME), maximum likelihood (ML) and/or Bayesian (BAYES) approaches to do microscopic and macroscopic data fusion. Actually ME can be used to assign a probability law to an unknown quantity when we have macroscopic data (expectations) on it.
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Data fusion in the internet of data [PDF]
Concurrency and Computation: Practice and Experience, 2018 Piccialli F., Jung J. J.
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Data assimilation and data fusion in a regional simulation
, 2012 An Ensemble Kalman filter [Ballabrera-Poy et al., 2009] has been used to assimilate Sea Surface Temperature (SST) and Argo data into a regional configuration of the NEMO-OPA ocean model. Our validation of the data assimilation experiments include the comparison against a random ensemble of Argo profilers previously set aside (cross-validation), where ...
Hoareau, Nina +4 more
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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
Felix Naumann, Jens Bleiholder
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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
Felix Naumann, Jens Bleiholder
openaire +2 more sources