Interactive Dynamic Aggregate Queries

Kenneth A. Ross

Junyan Ding

Columbia University

MediatorMediator

Data RequestData Request

UnifiedUnifiedResultsResults

UserUser Web

TraditionalDBMS

......

Scenario Outline

Graphical User Graphical User InterfaceInterface

Dynamic QueryDynamic Query

Data Filese.g., PUMS

Dynamic Query Dynamic Query EngineEngine

Engine Decoupled from Interface

Can use a variety of interfaces

Multiple connections to one server

Can “do one thing well”

Client/Server parallelism

Abstract interaction via API

Engine Performance Goals

Interactive data exploration

Millions of records

Thousands of columns (but look at ten or so at a time)

Aggregates and statistical measures

Fine adjustments at 30 answers/second.

Technical Details

Main Memory Implementation

Multidimensional tree structures

Cache consciousness

Branch Misprediction

SIMD

Asynchronous work

Internet

GetGloss

.gov url

Xml glossary info

ParseGloss Sensus

GlossIT

GlossIT

Automatic Ontologies from Web Pages

Judith L. KlavansPeter K. DavisSamuel Popper

Columbia University

Where are Glossaries?

Internet

GetGLOSSWeb Crawling to Find Glossaries

GetGloss

ParseGLOSSBuilding an Ontology

ParseGloss

Output forSENSUS Ontology

SENSUS

Data Users

Social Science Research Data Component

Electronic Data Service – Columbia Univ

Librarians and Data Specialists

Steady stream of different user groups

Collect user logs and interview users

Coordinated by Walter Bourne

DGRC User Interface Testbed

Menu presented as grid of alternating rows and columns– Top level items in left

column

Ontology entry shown in beam for selected item– Located as

near as possible

DGRC User Interface Testbed

Color coding shows parental and semantic relationships

DGRC User Interface Testbed

Fisheye magnification of region of interest

Magnified group laid out to avoid internal overlap

Optimize the effectiveness of the interface,

Identify usability problems,

Provide feedback on the overall functionality,

Anticipate changes in user need that might drive future development,

Validate the design,

Indicate the extent to which the interface improves on previous interfaces.

Goals of Evaluation

Methods of Evaluation

Interviews to Experts

Analysis of DataGate Interface

Design and Testing with Heuristic for

Database Interface

User and Task Analysis

Interview Findings

User Type Identification– Novice and Power/Expert Users

User GoalsKinds of QuestionsTypes of SearchesRelated Terms for Searches

– Difficulty of Use of Alternative Terms

Selecting the databaseLearning to Use the Interface

– Innovative Interface– Need Orientation and Time to Familiarize with the Interface

Interview Findings

Searching StylesFlexibility to Searching StylesHelping the User Define the Search

– Help users to Visualize the Context and Structure of Information

– Definition and Redefinition of Search

Standardization ProblemsSuggestions for the Design

• Variables– Hierarchical Structure– Massive Amount

• Terminology– Definitions Change– Obscure Terminology– Census Question

Change• Geographical References

– Boundaries Change– Unique Boundaries– Codes for Areas– Various Meanings for

Same Names

• Content Visualization– Display Information

Organization

• Dynamic Menu– Magnification on Selected

Items with Full Content

• Zoom In, Zoom Out– Manipulate the Level of

Magnification

• Searchlight– Multiple Layers of Display– Alternative Terms– Definition of Terms– Alternative Pathways

• Create Dynamic Maps

Census Characteristics and Interface PossibilitiesCensus Characteristics and Interface Possibilities