Results 51 to 60 of about 1,095,657 (311)
Data exchange and incomplete information [PDF]
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2006 Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target, and answering queries over target instances in a way that is semantically consistent with the information in the source.
openaire +1 more source
Examining the Robustness of Evaluation Metrics for Patent Retrieval with Incomplete Relevance Judgements [PDF]
, 2010 Recent years have seen a growing interest in research into patent retrieval. One of the key issues in conducting information retrieval (IR) research is meaningful evaluation of the effectiveness of the retrieval techniques applied to task under ...
Magdy, Walid +3 more
core +1 more source
FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
Chengshi guidao jiaotong yanjiu[Objective] To address the issues of prediction delay and accuracy degradation caused by incomplete deformation monitoring data in metro foundation pits, a prediction method based on a CNN-GRU (convolutional neural network-gated recurrent unit) neural ...
ZHOU Yi +5 more
doaj +1 more source
K-Means Clustering With Incomplete Data
IEEE Access, 2019 Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with ...
Siwei Wang +6 more
doaj +1 more source
Effective Density-Based Clustering Algorithms for Incomplete Data
Big Data Mining and Analytics, 2021 Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for ...
Zhonghao Xue, Hongzhi Wang
doaj +1 more source
Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study [PDF]
, 2010 Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the ...
Holder, Roger +12 more
core +1 more source
Calpain small subunit homodimerization is robust and calcium‐independent
FEBS Letters, EarlyView.Calpains dimerize via penta‐EF‐hand (PEF) domains. Using single‐molecule force spectroscopy, we measured the strength and kinetics of PEF–PEF homodimer binding. The interaction is robust, shows a transient conformational step before dissociation, and remains largely insensitive to Ca2+.
Nesha May O. Andoy +4 more
wiley +1 more source
GrImp: Granular Imputation of Missing Data for Interpretable Fuzzy Models
AxiomsData incompleteness is a common problem in real-life datasets. This is caused by acquisition problems, sensor failures, human errors, and so on. Missing values and their subsequent imputation can significantly affect the performance of data-driven models
Krzysztof Siminski, Konrad Wnuk
doaj +1 more source
Finding Top- $k$ Dominance on Incomplete Big Data Using MapReduce Framework
IEEE Access, 2018 Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes large.
Payam Ezatpoor +3 more
doaj +1 more source