Cover Image

Big data integration /

Bibliographic Details
Main Authors: Dong, Xin Luna (Author), Srivastava, Divesh (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2015.
Series:Synthesis lectures on data management ; # 40.
Subjects:
Big data.
Data integration (Computer science)
big data integration
data fusion
record linkage
schema alignment
variety
velocity
veracity
volume
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 1. Motivation: challenges and opportunities for BDI
  • 1.1 Traditional data integration
  • 1.1.1 The flights example: data sources
  • 1.1.2 The flights example: data integration
  • 1.1.3 Data integration: architecture & three major steps
  • 1.2 BDI: challenges
  • 1.2.1 The "V" dimensions
  • 1.2.2 Case study: quantity of deep web data
  • 1.2.3 Case study: extracted domain-specific data
  • 1.2.4 Case study: quality of deep web data
  • 1.2.5 Case study: surface web structured data
  • 1.2.6 Case study: extracted knowledge triples
  • 1.3 BDI: opportunities
  • 1.3.1 Data redundancy
  • 1.3.2 Long data
  • 1.3.3 Big data platforms
  • 1.4 Outline of book
  • 2. Schema alignment
  • 2.1 Traditional schema alignment: a quick tour
  • 2.1.1 Mediated schema
  • 2.1.2 Attribute matching
  • 2.1.3 Schema mapping
  • 2.1.4 Query answering
  • 2.2 Addressing the variety and velocity challenges
  • 2.2.1 Probabilistic schema alignment
  • 2.2.2 Pay-as-you-go user feedback
  • 2.3 Addressing the variety and volume challenges
  • 2.3.1 Integrating deep web data
  • 2.3.2 Integrating web tables
  • 3. Record linkage
  • 3.1 Traditional record linkage: a quick tour
  • 3.1.1 Pairwise matching
  • 3.1.2 Clustering
  • 3.1.3 Blocking
  • 3.2 Addressing the volume challenge
  • 3.2.1 Using MapReduce to parallelize blocking
  • 3.2.2 Meta-blocking: pruning pairwise matchings
  • 3.3 Addressing the velocity challenge
  • 3.3.1 Incremental record linkage
  • 3.4 Addressing the variety challenge
  • 3.4.1 Linking text snippets to structured data
  • 3.5 Addressing the veracity challenge
  • 3.5.1 Temporal record linkage
  • 3.5.2 Record linkage with uniqueness constraints
  • 4. BDI: data fusion
  • 4.1 Traditional data fusion: a quick tour
  • 4.2 Addressing the veracity challenge
  • 4.2.1 Accuracy of a source
  • 4.2.2 Probability of a value being true
  • 4.2.3 Copying between sources
  • 4.2.4 The end-to-end solution
  • 4.2.5 Extensions and alternatives
  • 4.3 Addressing the volume challenge
  • 4.3.1 A MapReduce-based framework for offline fusion
  • 4.3.2 Online data fusion
  • 4.4 Addressing the velocity challenge
  • 4.5 Addressing the variety challenge
  • 5. BDI: emerging topics
  • 5.1 Role of crowdsourcing
  • 5.1.1 Leveraging transitive relations
  • 5.1.2 Crowdsourcing the end-to-end workflow
  • 5.1.3 Future work
  • 5.2 Source selection
  • 5.2.1 Static sources
  • 5.2.2 Dynamic sources
  • 5.2.3 Future work
  • 5.3 Source profiling
  • 5.3.1 The Bellman system
  • 5.3.2 Summarizing sources
  • 5.3.3 Future work
  • 6. Conclusions
  • Bibliography
  • Authors' biographies
  • Index.