usability and integration h. v. jagadish. many sources of data text xml/semi-structured experimental...
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Usability and Integration
H. V. Jagadish
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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
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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.
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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.
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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
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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]
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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]
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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]