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Data Warehousing and Knowledge Discovery

Overview of attention for book
Data Warehousing and Knowledge Discovery
Springer Science & Business Media

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Logic Programming for Data Warehouse Conceptual Schema Validation.
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    Chapter 2 A Model-Driven Heuristic Approach for Detecting Multidimensional Facts in Relational Data Sources
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    Chapter 3 Physical Design and Implementation of Spatial Data Warehouses Supporting Continuous Fields
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    Chapter 4 Benchmarking Spatial Data Warehouses
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    Chapter 5 Discovering Community-Oriented Roles of Nodes in a Social Network
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    Chapter 6 A Graph-Based Clustering Scheme for Identifying Related Tags in Folksonomies
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    Chapter 7 Frequent Sub-graph Mining on Edge Weighted Graphs
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    Chapter 8 $\mathcal{F}$ & $\mathcal{A}$ : A Methodology for Effectively and Efficiently Designing Parallel Relational Data Warehouses on Heterogenous Database Clusters
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    Chapter 9 Yet Another Algorithms for Selecting Bitmap Join Indexes
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    Chapter 10 Speeding Up Queries in Column Stores
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    Chapter 11 Mining Non-redundant Information-Theoretic Dependencies between Itemsets
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    Chapter 12 Discovery and Application of Functional Dependencies in Conjunctive Query Mining
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    Chapter 13 Using Transitivity to Increase the Accuracy of Sample-Based Pearson Correlation Coefficients
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    Chapter 14 The NOX Framework: Native Language Queries for Business Intelligence Applications
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    Chapter 15 Experience in Extending Query Engine for Continuous Analytics
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    Chapter 16 Development of a Business Intelligence Environment for e-Gov Using Open Source Technologies
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    Chapter 17 A Fast Randomized Method for Local Density-Based Outlier Detection in High Dimensional Data
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    Chapter 18 Specialty Mining
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    Chapter 19 Region of Interest Based Image Categorization
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    Chapter 20 Meta-learning for Post-processing of Association Rules
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    Chapter 21 A Relational Approach for Discovering Frequent Patterns with Disjunctions
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    Chapter 22 An Occurrence Based Approach to Mine Emerging Sequences
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    Chapter 23 Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
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    Chapter 24 XML Data Fusion
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    Chapter 25 An Efficient Duplicate Record Detection Using q-Grams Array Inverted Index
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    Chapter 26 Modelling Complex Data by Learning Which Variable to Construct
Overall attention for this book and its chapters
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164 Mendeley
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Title
Data Warehousing and Knowledge Discovery
Published by
ADS, August 2010
DOI 10.1007/978-3-642-15105-7
ISBNs
978-3-64-215104-0, 978-3-64-215105-7
Editors

Bach Pedersen, Torben, Mohania, Mukesh K., Tjoa, A Min

Timeline

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 164 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Unknown 163 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 2%
Professor > Associate Professor 2 1%
Student > Bachelor 1 <1%
Unspecified 1 <1%
Unknown 157 96%
Readers by discipline Count As %
Computer Science 3 2%
Unspecified 2 1%
Business, Management and Accounting 1 <1%
Mathematics 1 <1%
Unknown 157 96%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 June 2015.
All research outputs
#20,447,499
of 23,002,898 outputs
Outputs from ADS
#34,078
of 37,435 outputs
Outputs of similar age
#90,521
of 95,267 outputs
Outputs of similar age from ADS
#380
of 397 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 37,435 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 95,267 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 397 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.