Architectural, Spatial, and Navigational Metaphors as Design

Points for Collaboration

John “Boz” Handy-Bosma, Ph.D.Chief Architect for Collaboration, IBM Office of the CIO

For KM Chicago, May 8, 2012• May 8, 2012

Credit:Ogivly

Credit: Fellowship of the Rich, Flickr

MOSFET Architecture, scaling recipes, and Moore’s Law

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Dimensions

Voltages

Doping levels

K Circuit delay:

Power/circuit:

Power delay product:

K

K

K

Decreased by:If scaled by Constant

Recipes in Dennard's Scaling Theory

Factors maintained in constant ratio

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Consistent inputs in proportion Predictable outcomes on figures of merit

Figure of merit: a quantity characterizing performance

Used for benchmarking and comparisons

e.g.; clock speed in CPU, wicking factor in fabrics

Consistent measurement

Related figures held to constant performance, not degraded

Not classical power laws

Multiple factors

Relationships among factors

No claims about development in relation to time

How to achieve ratios is not addressed

Where to look for scaling principles?

(Answer: where things are clogged or crowded)

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An approach Identify practical recipes for improving Collaboration and Search. Use these as input to decisions on architecture and design.

Factors maintained in constant ratio(roughly)

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Consistent improvement in specific factors

- in proportion - Predictable outcomes in figures of merit

Recipes enable balanced improvement in search, collaboration, and metrics

•Precision and recall

•Content and metadata

•Adoption and Use

Experimentation allows for measurement and improvement on key measures – but it is important to identify potential trade-offs in figures of merit resulting from technical and social factors:

Serial navigation (similar to Fitt's Law)

Impact of follower models on signal-to-noise ratio of communication

Multiple factors:

• Wayfinding (e.g.; navigating, searching, sorting, filtering)

• Information production (e.g.; quality and quantity of authoring, tagging, publishing)

• Bidirectional (e.g.; reciprocal networking among participants)

Relationships: mutually reinforcing, mutually impinging, exponential

Factors are expressed via specific solutions as used in the field

Wayfinding and Isovist: How is search relevance measured?

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Key Term Definition

Relevance A subjective measure of whether a document in a search result answers a query

Precision A measure of the percentage of documents in a result list that answer a query

Recall A measure of the percentage of documents in a result list relevative to all documents in a collection

Pertinence A subjective measure of whether a document in a search result answers a query (in light of previous knowledge or experience)

Aboutness The subjects and topics conveyed by a document or query

Isovist Pertinent items visible | not visible at any given point in a navigational sequence

Test for performance using known corpora and results (e.g.; Trec)

Typically uses a single query and response, rather than a series of interactions between users and search engine

Geared toward top of results list

But traditional approaches are not sufficient to measure relevance of results, where relevance is determined by social interaction and collaboration outcomes!

Example: What aspects of metadata facilitate collaboration?

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Collaboration capability Metadata features

Integrating disparate bodies of content from multiple sources / communities

- Incorporate global and local extensions to vocabulary-- Query modification to allow lateral navigation-- Matching on shared interests

Team Coordination - Content previews, review and approval, collaborative workflow- Tagging at group level- Metadata suggestions

Positive network effects from sharing in social channels

- Social Tagging and Bookmarking- Rankings and ratings- Clickstream analysis for ranking

Knowledge Elicitation - Query expansion a) Conditional metadata, b) Did you mean?- Tag notifications

Facilitate collaboration among disparate language comunities

- Unique and mapped display values; e.g.; Social Authority

Example: when is metadata search helpful to collaboration?

When metadata search?✔ Multiple set membership for searchables✔ Sufficient completeness and quality of

classification scheme✔ Adequate accuracy of categorization✔ Leads to improved effective precision and

time to find

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When not metadata search?

✗ Precise results can be obtained without metadata

✗ When metadata leads to undesirable phenomena such as conjunction search, serial navigation, or error propagation

Often assumed, but questionable:

? That a single large corpus is to be searched

? That metadata require hierarchical taxonomy with many classifiers

? That agreement on taxonomy is needed

? That searches are for documents (as opposed to collections of documents, parts of documents, people, facts, etc.)

? That metadata operations only involve “anding” on attributes to find instances

Measuring effective precision of metadata search

• Log sequence of user actions in a search session (queries, metadata selections, links)

• Work backward from a known result (document click, download, print, tag, bookmark, notify, rate, exit)

• Establish influence of each step in sequence on ranking of document(s) that elicited that result (via rankings in results list)

• Query by segments of interest using aggregated data

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Privacy-preserving cookies

Clickstream data

Searchqueries

Clickstream repository

Segmentation database

Sequence

Survey and ratings repositories

Analysis

Survey and ratings info

Example: Is stemming improving the search results? Method: A-B tests using stemming, sample measures of search precision

Optimization Cycle2. Observe

practice

6. Measure

Outcomes

7. Transition Variables to

Constants

4. Propose New

Variables

5. Build new

configuration

3. Evaluate

Bottlenecks

1. Configure

Practices

and Tools