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Database Systems for Advanced Applications

Overview of attention for book
Cover of 'Database Systems for Advanced Applications'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A General Fine-Grained Truth Discovery Approach for Crowdsourced Data Aggregation
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    Chapter 2 Learning the Structures of Online Asynchronous Conversations
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    Chapter 3 A Question Routing Technique Using Deep Neural Network for Communities of Question Answering
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    Chapter 4 Category-Level Transfer Learning from Knowledge Base to Microblog Stream for Accurate Event Detection
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    Chapter 5 AngleCut: A Ring-Based Hashing Scheme for Distributed Metadata Management
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    Chapter 6 An Efficient Bulk Loading Approach of Secondary Index in Distributed Log-Structured Data Stores
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    Chapter 7 Performance Comparison of Distributed Processing of Large Volume of Data on Top of Xen and Docker-Based Virtual Clusters
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    Chapter 8 An Adaptive Data Partitioning Scheme for Accelerating Exploratory Spark SQL Queries
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    Chapter 9 Semi-Supervised Network Embedding
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    Chapter 10 CirE: Circular Embeddings of Knowledge Graphs
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    Chapter 11 PPNE: Property Preserving Network Embedding
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    Chapter 12 HINE: Heterogeneous Information Network Embedding
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    Chapter 13 DT-KST: Distributed Top-k Similarity Query on Big Trajectory Streams
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    Chapter 14 A Distributed Multi-level Composite Index for KNN Processing on Long Time Series
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    Chapter 15 Outlier Trajectory Detection: A Trajectory Analytics Based Approach
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    Chapter 16 Clustering Time Series Utilizing a Dimension Hierarchical Decomposition Approach
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    Chapter 17 Fast Extended One-Versus-Rest Multi-label SVM Classification Algorithm Based on Approximate Extreme Points
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    Chapter 18 Efficiently Discovering Most-Specific Mixed Patterns from Large Data Trees
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    Chapter 19 Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment
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    Chapter 20 An Intelligent Field-Aware Factorization Machine Model
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    Chapter 21 Beyond Skylines: Explicit Preferences
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    Chapter 22 Optimizing Window Aggregate Functions in Relational Database Systems
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    Chapter 23 Query Optimization on Hybrid Storage
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    Chapter 24 Efficient Batch Grouping in Relational Datasets
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    Chapter 25 Memory-Enhanced Latent Semantic Model: Short Text Understanding for Sentiment Analysis
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    Chapter 26 Supervised Intensive Topic Models for Emotion Detection over Short Text
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    Chapter 27 Leveraging Pattern Associations for Word Embedding Models
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    Chapter 28 Multi-Granularity Neural Sentence Model for Measuring Short Text Similarity
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    Chapter 29 Leveraging Kernel Incorporated Matrix Factorization for Smartphone Application Recommendation
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    Chapter 30 Preference Integration in Context-Aware Recommendation
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    Chapter 31 Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation
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    Chapter 32 Location-Aware News Recommendation Using Deep Localized Semantic Analysis
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    Chapter 33 Review-Based Cross-Domain Recommendation Through Joint Tensor Factorization
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    Chapter 34 A Local-Clustering-Based Personalized Differential Privacy Framework for User-Based Collaborative Filtering
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    Chapter 35 Fast Multi-dimensional Range Queries on Encrypted Cloud Databases
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    Chapter 36 When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System
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    Chapter 37 Supporting Cost-Efficient Multi-tenant Database Services with Service Level Objectives (SLOs)
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    Chapter 38 Recovering Missing Values from Corrupted Spatio-Temporal Sensory Data via Robust Low-Rank Tensor Completion
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    Chapter 39 Group-Level Influence Maximization with Budget Constraint
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    Chapter 40 Correlating Stressor Events for Social Network Based Adolescent Stress Prediction
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    Chapter 41 Emotion Detection in Online Social Network Based on Multi-label Learning
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

2 X users
1 patent

Readers on

17 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Database Systems for Advanced Applications
Published by
Lecture notes in computer science, January 2017
DOI 10.1007/978-3-319-55753-3
978-3-31-955752-6, 978-3-31-955753-3

Selçuk Candan, Lei Chen, Torben Bach Pedersen, Lijun Chang, Wen Hua

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 24%
Student > Ph. D. Student 3 18%
Student > Doctoral Student 1 6%
Researcher 1 6%
Student > Postgraduate 1 6%
Other 0 0%
Unknown 7 41%
Readers by discipline Count As %
Computer Science 8 47%
Engineering 1 6%
Unknown 8 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 20 June 2019.
All research outputs
of 23,509,982 outputs
Outputs from Lecture notes in computer science
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Outputs of similar age
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Outputs of similar age from Lecture notes in computer science
of 150 outputs
Altmetric has tracked 23,509,982 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,137 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 75% of its peers.
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 423,671 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.