2013 IEEE International Conference on Big Data 2013
DOI: 10.1109/bigdata.2013.6691635
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Large Scale predictive analytics for real-time energy management

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Cited by 27 publications
(21 citation statements)
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“…With multidimensional range queries, analysts define the subspaces in R d of interest within the overall data space. High quality cardinality prediction in such subspaces then becomes important for data mining, data exploration, time series analysis, and big data visualization tasks [9,12] of data (sub)spaces of interest.…”
Section: Definition 1 (Range Querymentioning
confidence: 99%
See 1 more Smart Citation
“…With multidimensional range queries, analysts define the subspaces in R d of interest within the overall data space. High quality cardinality prediction in such subspaces then becomes important for data mining, data exploration, time series analysis, and big data visualization tasks [9,12] of data (sub)spaces of interest.…”
Section: Definition 1 (Range Querymentioning
confidence: 99%
“…Frequently, data analysts, data scientists, and statisticians are in search of approximate answers to such queries over unknown data subspaces, which supports knowledge discovery and underlying data function estimation. Imagine exploratory and predictive analytics [9] based on a stream of such aggregation operators over data subspaces being issued, until the scientists/analysts extract sufficient statistics or fit local function estimators, e.g., coefficient of determination, product-moment correlation coefficient, and multivariate local linear approximation over the subspaces of interest.…”
Section: Introductionmentioning
confidence: 99%
“…With m-d range queries, analysts define the subspaces of interest within the overall data space. SCP in such subspaces then becomes important for data mining, query-driven data exploration, time series analysis, and big data visualization tasks [8], [4] of data (sub)spaces of interest. In exploratory and predictive analytics, a data scientist routinely defines specific regions of a large dataset that are worth exploring and wishes to derive and predict statistics over the populations of these regions -which amounts to SCP of the corresponding range queries.…”
Section: Introductionmentioning
confidence: 99%
“…Frequently, data analysts and statisticians are in search of (approximate and/or partial) answers to such queries over unknown data subspaces (knowledge discovery). Imagine exploratory and predictive analytics [4] based on a stream of such aggregation operators over data subspaces being issued, until the scientist extracts sufficient statistics or learns local statistical characteristics, e.g., coefficient of determination and product-moment correlation coefficient, of the subspaces of interest.…”
Section: Introductionmentioning
confidence: 99%
“…However, these rule-inference methods do not guarantee optimality. Other contributions exploit data mining approaches to predict the building energy performance: Fan et al [21] use ensemble models for predicting next-day energy consumption and peak power, whereas Balac et al [22] develop a highly scalable framework capable of analysing and predicting the building behaviour considering alternative energy sources and smart grid constraints in real-time.…”
Section: Related Workmentioning
confidence: 99%