2013
DOI: 10.1002/spe.2192
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A heuristic approach to selecting views for materialization

Abstract: SUMMARYXML data warehouses are becoming more popular as data is harvested from the web or as output from web services. As these warehouses tend to grow significantly over time, various techniques for expediting queries have been developed. One such technique is to materialize some or all of the queries in advance of query processing. These views are then subject to change either when underlying data changes or view definitions themselves are modified by users. The work in this paper focuses on changes to view … Show more

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Cited by 12 publications
(10 citation statements)
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“…Instead, this forms part of current research building upon the optimization strategies presented in Liu and colleagues. 27,28 While only a brief description of the In-MINDD ontology was discussed here, it has evolved from the initial ontology in Roantree et al 14 and is now stable to the point where it is used as a foundation for the profiler and support forum cloud-based apps. This forms part of a second stream of current research 13 where cloud-based medical data are anonymized.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Instead, this forms part of current research building upon the optimization strategies presented in Liu and colleagues. 27,28 While only a brief description of the In-MINDD ontology was discussed here, it has evolved from the initial ontology in Roantree et al 14 and is now stable to the point where it is used as a foundation for the profiler and support forum cloud-based apps. This forms part of a second stream of current research 13 where cloud-based medical data are anonymized.…”
Section: Resultsmentioning
confidence: 99%
“…While our ontology also provides an opportunity for interoperability across clinical studies and for an XMLbased integration with online clinical data, we do not present a discussion here. Instead this forms part of current research building upon the optimization strategies presented in [14,15]. While only a brief description of the In-MINDD Ontology was discussed here, it has evolved from the initial ontology in [28] and is now stable to the point where it is used as a foundation for the Profiler and Support Forum Cloud-based apps.…”
Section: Discussionmentioning
confidence: 99%
“…These processing tasks include acquisition and interpretation, transformation, integration, and analysis or machine learning of data from sensors, devices, vehicles etc. There have been a number of approaches to building smart city applications [5] and to cluster or integrate query graphs on smart city data [10,12]. Decision makers who use smart city applications require OLAP type services to generate the datasets from a warehouse upon which they make their decision.…”
Section: Introductionmentioning
confidence: 99%
“…Enhancing data warehouses with web generated XML data was presented in [15] but again, the schema design (facts and dimensions) was driven by existing enterprise databases. In [8], the authors provide an efficient cube maintenance system based on relational data and this was extended for XML data in [17]. While this work can potentially integrate multiple XML cubes, it is based on a static cube and query set while our approach is to manage new and updating datasets coming from smart city sources.…”
Section: Introductionmentioning
confidence: 99%