data fusion

By O. Vergara

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Fast Learning from Distributed Datasets without Entity Matching (2016, methodology): “Common issues in fusion, such as conflicts and heterogeneity [Bleiholder and Naumann, 2008], are not considered in this work.”
A survey of data fusion in smart city applications (2019, background): “In order to address such issue, Huang et al. [126] have provided a solution by integrating two types of cameras for seafood freshness inspection.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Prior studies in the field of data fusion have developed numerous approaches for modelling the correctness of information supplied by multiple sources in the presence of possible data conflicts (see [143] and [144] for recent surveys).”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Another example is data transformation [120], which is typically used to handle distributed data sources with heterogeneous schema and is particularly useful for business datasets.”
Data Matching (2012, background): “The third task, known as data fusion [38], is the process of merging pairs or groups of records that have been classified as matches (i.”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “2 shows the classification of different record fusing strategies [16].”
Data fusion in intelligent transportation systems: Progress and challenges - A survey (2011, unknown): “Recognizing that, Sohn and Lee [70] use data fusion and ensemble algorithms to increase the accuracy.”
A taxonomy of privacy-preserving record linkage techniques (2013, background): “The three major tasks in data integration are schema matching [9], record linkage [6,10], and data fusion [11,12].”
Big data integration (2013, background): “Data fusion refers to resolving conflicts from different sources and finding the truth that reflects the real world [2], [11].”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, background): “This problem has been extensively studied in both database and natural language processing areas [7,9].”
A Survey on Truth Discovery (2015, background): “Truth discovery is motivated by the strong need to resolve conﬂicts among multi-source noisy information, since con-ﬂicts are commonly observed in database [8,9,17], the Web [29, 52], crowdsourced data [41, 42, 59], etc.”
A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014, background): “As an important aspect of this area, research on resolving conflicts from multiple sources [4,5,10,22] arise various ways to handle conflicts in data integration.”
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, background): “We point the reader to a survey by Bleiholder and Naumann [6] that describes how common schema matching and duplicate detection techniques can be used to address these two problems.”
Truth Finding on the Deep Web: Is the Problem Solved? (2012, background): “Existing works on data fusion ([3, 8] as surveys and [10, 13, 14, 17, 19, 20] as recent works) have experimented on data collected from the Web in domains such as book, restaurant and sports.”
A self-training approach for resolving object coreference on the semantic web (2011, background): “…the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which .nds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between property-value pairs, which is not always accurate enough.”
Quality assessment for Linked Data: A Survey (2015, background): “However, if data integration leads to duplication of instances, it may lead to contradictory values thus leading to inconsistency [8].”