Usability and Integration

1
Usability and Integration

• H. V. Jagadish

2
Many Sources of Data

• Text
• XML/semi-structured
• Experimental measurements
• Public databases
• Some data may have time/space variation
• Need to make sense of this big mess

3
Find Patterns in Data

• Conventional data mining seeks patterns that can
  be mathematically specified over (usually) global
  extents.
• Typically assume simple data structure.
• Need new approaches to find patterns in messy
  data.

4
Human in the Loop

• Hard for a machine to tell an interesting pattern
  apart from one that is not.
• Problem exacerbated when we seek
  smaller/localized patterns, or work with large
  vocabularies of possible patterns.
• Need human in the loop to make this judgment.

5
Computer-Assisted (Human) Analytics

• Patterns found by human and not by computer.
• Job of computer is to make patterns easy to find.
• So computer system must effectively support
  queries and display results.
• Eg.Visual Analytics

6
Organize Data for Analysis

• Join multiple complex temporal data streams into
  a windowed model suitable for efficient
  analysis. Manish Singh
• Permit organic change to schema as information
  needs evolve. Eric Qian
• Provide a spreadsheet interface for direct
  manipulation of complex and large data. Choose
  small sets of representatives effectively. Ben
  Liu

7
Access Data for Analysis

• Under-specified queries, particularly keyword
  queries. Derive qunit as response unit, mined
  from observed query logs. Arnab Nandi
• Visual manipulation algebra for analyzing large
  time-varying graphs with data on nodes and edges.
  Anna Shaverdian

8
Scientific Data Analysis

• Explain analysis results in terms of source data,
  even when the source may have been updated since.
  Jing Zhang
• Analyze gene expression microarray data, and
  electronic health record data, in light of known
  biomedical knowledge. Fernando Farfan