Systematic review methodsData extraction forms link systematic reviews with primary research and provide the foundation for appraising, analysing, summarising and interpreting a body of evidence. This makes their development, pilot testing and use a crucial part of the systematic reviews process. Several...
ObjectiveThis study assessed the impact of systematic review and data extraction experience on the accuracy and efficiency of data extraction in systematic reviews.Study Design and SettingWe conducted a prospective cross-sectional study from October to December 2006. Participants were classified as having...
Feature extraction can be done using a bag of words (i.e., simply count occurrences of tokens without considering word order nor normalizing counters), n-grams (i.e., extract the contiguous sequence of n tokens such as bi-gram which indicates the extraction of token pairs), and collocations...
This systematic review is not without bias. Firstly, there is a risk of bias in the review process as only one reviewer screening the literature where the subjectivity of the inclusion and exclusion criteria may affect the selection of relevant publications. Moreover, the year range was not spec...
systematic literature review. To reduce the risk of bias in study identification and selection, the study selection process was performed independently by J.P. and J.D. and subsequently cross-checked. The data extraction process was performed by J.P., E.D., and J.D. and then cross-...
processing can be classified either as stream processing (e.g., filtering, annotation) or batch processing (e.g., cleaning, combining and replication). For further processing, depending on the requirements of the system, information extraction, data integration, in-memory processing, anddata ingesti...
26th European Symposium on Computer Aided Process Engineering Jun Yow Yong, ... Jiří Jaromír Klemeš, in Computer Aided Chemical Engineering, 2016 Abstract Data reconciliation is a key step of data extraction from existing plants. While there are many publications on data reconciliation generally...
The idea of streaming analytics is that each of the received data tuples is processed in the data processing node. Such processing includes removing duplicates, filling missing data, data normalization, parsing, feature extraction, which are typically done in a single pass due to the high data ...
Fog computing: A taxonomy, systematic review, current trends and research challenges JagdeepSingh, ...Sukhpal SinghGill, inJournal of Parallel and Distributed Computing, 2021 3.7Data extraction AppendixBlaid out the strategy for data extraction from all the 154 research articles considered in this SLR...
(72.6%) owing to the predominant use of feature extraction techniques to generate structured data; however, missing data handling techniques were used in 23.6% of datasets most of which involved variable exclusion. Outlier detection was performed for datasets of three models (2.8%) and most ...