15.2 Five-Stage Search FrameworkIn designing the advanced .docx

15.2 Five-Stage Search Framework
In designing the advanced search interface, a five-stage search
framework may help to coordinate
design practices and satisfy the needs of first-time, intermittent,
and frequent users. The five
stages of action, illustrated more fully in Box 15.1 , are:
1. Formulation: Expressing the search
2. Initiation of action: Launching the search
3. Review of results: Reading messages and outcomes
4. Refinement: Formulating the next step
5. Use: Compiling or disseminating insight
Information seeking is an iterative process, so the five-stages
can be repeated many times until
users’ needs are met. Users may not see all the components of
the five stages, but if they are
unsatisfied with the results, they should be able to have
additional options and change their
queries easily.
PRINTED BY: Fran Blow <[email protected]>. Printing is for
personal, private use only. No part of this book may be
reproduced or transmitted without publisher's prior permission.
Violators
will be prosecuted.
2/19/2018http://e.pub/r6kac6j9uer7lvx1pcjc.vbk/OPS/xhtml/file
P7001012430000000000000000002...

Box 15.1 Five-stage framework to clarify search user interfaces
1. Formulation
◾ Use simple and advanced search.
◾ Limit the search using structured fields such as year, media,
or location.
◾ Recognize phrases to allow entry of names, such as “George
Washington.”
◾ Permit variants to allow relaxation of search constraints (e.g.,
phonetic variations).
◾ Control the size of the initial result set.
◾ Use scoping of source carefully.
◾ Provide suggestions, hints, and common sources.
2. Initiation of action
◾ Explicit actions are initiated by buttons with consistent labels
(such as “Search”).
◾ Implicit actions are initiated by changes to a parameter and
update results
immediately.
◾ Guide users to successful or past queries with auto-complete.
3. Review of results
◾ Keep search terms and constraints visible.
◾ Provide an overview of the results (e.g., total number).
◾ Categorize results using metadata (by attribute value, topics,
etc.).
◾ Provide descriptive previews of each result item.
◾ Highlight search terms in results.
◾ Allow examination of selected items.
◾ Provide visualizations when appropriate (e.g., maps or
timelines).
◾ Allow adjustment of the size of the result set and which
fields are displayed.

◾ Allow change of sequencing (alphabetical, chronological,
relevance ranked, etc.).
4. Refinement
◾ Guide users in progressive refinement with meaningful
messages.
◾ Make changing of search parameters convenient.
◾ Provide related searches.
◾ Provide suggestions for error correction (without forcing
correction).
5. Use
◾ Imbed actions in results when possible.
◾ Allow queries, settings, and results to be saved, annotated,
and sent to other
applications.
◾ Explore collecting explicit feedback (ratings, reviews, like,
etc.).
Violators
will be prosecuted.
P7001012430000000000000000002...
15.2.1 Formulation
The formulation stage includes identifying the source of the
information (i.e., where to search).
Users may want to limit their searches for flights to certain

travel sites, or limit a text search to help
documents and not the entire web (Fig. 15.3 ), or search only
women’s clothing on a shopping
website (e.g., women’s shirts). Also called scoping, this
limitation of the source can lead to better
results but also lead to failures when the constraint remains
active and users forget about it.
Clearly displaying the source of information is important.
Even if it is technically feasible, searching all libraries or the
entire web may not always be the
best approach. Users often prefer to limit the search to a
specific library or collection in a library
(e.g., within the manuscript collections of the Library of
Congress or within Wikipedia). Keywords
or phrases can be specified, and structured fields such as year of
publication, volume number, or
language can be used to further limit the search scope. A text
box and a few menus may be
enough in most cases, but form fill-in (Section 8.6 ) allows
users to specify more detailed
searches in databases (e.g., search for nonstop flights between
three local airports and New
Orleans across a range of possible dates).
In database searches (e.g., Fig. 15.2 ), users often seek items
that contain meaningful short
phrases (“Civil War,” “Environmental Protection Agency,”
“carbon monoxide”), and multiple-entry
fields can be provided to allow for multiple phrases. Searches
on phrases have proven to be more
accurate than searches on individual words. Phrases also
facilitate searching for names (for
example, a search on “George Washington” should not turn up
“George Bush” or “Washington,
DC”). If Boolean operations, proximity restrictions, or other

combining strategies
Figure 15.3
The Yahoo! help search box has two buttons of different colors
to search two different sources of
information: purple for searching the help information and blue
for searching the web. Pressing the
purple button “scopes” the results to the help information only
and shows results below a purple
banner. Searching the web jumps to a different page (the normal
search) that reuses the blue
button color, helping users keep track of which source of
information they are searching.
Violators
will be prosecuted.
P7001012430000000000000000002...
are specifiable, users should also be able to express them. Users
or service providers should
additionally have control over stop lists (which typically filter
out from the search terms common
words, single letters, and obscenities).
When users are unsure of the exact value of the field (the terms
to be searched for or the spelling
or capitalization of a name), the search constraints can be
relaxed by allowing variants to be
accepted. In a textual-document search, advanced search

interfaces may give users control over
variant capitalization (case sensitivity), stemmed versions (the
keyword “teach” retrieves words
with variant suffixes such as “teacher,” “teaching,” and
“teaches”), partial matches (the keyword
“biology” retrieves “sociobiology” and “astrobiology”),
phonetic variants from soundex methods
(the keyword “Johnson” retrieves “Jonson,” “Jansen,” and
“Johnsson”), synonyms (the keyword
“cancer” retrieves “malignant neoplasm”), abbreviations (the
keyword “IBM” retrieves “International
Business Machines,” and vice versa), and broader or narrower
terms from a thesaurus (the
keyphrase “New England” retrieves “Vermont,” “Maine,”
“Rhode Island,” “New Hampshire,”
“Massachusetts,” and “Connecticut”).
When searching in a simple list of items (e.g., searching a name
in a list of contacts), the result list
can be displayed as users type. The list shrinks rapidly and
users can select the wanted item
without finishing typing the name. When the collection is large,
auto-completion can be applied to
the search term instead, revealing most common search phrases
that match the text already typed
(Fig. 15.4 ). The
Figure 15.4
Auto-complete suggestions can speed data entry and guide users
toward successful queries.
a. In a mobile phone address book, typing one character filters
the list to all names that
contain that character, and the list is updated continuously as
users type.

b. Typing “helm” in Amazon’s search box shows suggestions
for “helmet light” or “welding
helmet” but also suggestions to narrow the scope of the search
to relevant departments.
c. On the Adobe website, suggestions include products (e.g.,
typing the beginning of the word
“video” suggests several video-editing tools).
auto-complete list is updated as users continue typing, which
helps them recall terms of interest,
limits misspelling, and speeds up the query initiation process.
Violators
will be prosecuted.
P7001012430000000000000000002...
Mobile applications may use context information such as
location to narrow down the auto-
completion suggestions. For example, searching on a map can
narrow the history of previous
searches to the ones relevant to the current location. This may
allows users to find the exact
location of a doctor’s office for a follow-up visit even if they
don’t remember the name or exact
address of the doctor—an impressive combined use of location,
history and auto-completion.
For regular users who want additional control, advanced

command languages can be offered to
search databases (e.g., SQL; see Section 15.4 ). On the other
hand, new users will benefit
from the reading of typical phrases (which can be placed next to
the search box), direct links to
often searched items (e.g., sales or popular topics), and
carefully designed tips. The seamless
integration of search with navigation and browsing will allow
users to switch to menus of choices
when they are not able to come up with search phrases or to
review sample materials to better
understand what is available even before they start composing
their query (see also Section
15.3 and Fig. 12.1 of NASA’s Earthdata search interface).
15.2.2 Initiation of action
The second stage is the initiation of action, which may be
explicit or implicit. Many systems have a
search button for explicit initiation. A magnifier glass has
become the de facto standard icon for
search when space is limited, but pressing the Enter key on a
keyboard or pausing during spoken
interaction may be the only thing needed to initiate the search.
An appealing alternative is implicit initiation, in which each
change to any component of the
formulation stage immediately produces a new set of search
results. Dynamic queries in which
users adjust query widgets to produce continuous updates
(Shneiderman, 1994) led the way in
demonstrating the benefits of implicit initiation, and they have
been widely adopted in faceted
browsing (Section 15.3 ).

Violators
will be prosecuted.
P7001012430000000000000000002...
15.2.3 Review of results
The third stage is the review of results, in which users review
results in textual lists (Fig. 15.5 )
or on geographical maps (Fig. 15.6 ), timelines (see HIPMUNK
in Fig. 1.7 ), or other
specialized visual overviews of results. If no items are found,
that failure should be indicated
clearly. When messages are worded carefully (see error
messages in Section 12.8 ) and
useful suggestions are provided, users are less likely to abandon
their search (e.g., leave a
shopping website to never come back).
When results are presented in a list, it is common practice to
return only about 20 results, but
larger initial sets are preferable for those with high bandwidth
and large displays. Previews
consisting of carefully selected text samples (or snippets; see
Fig. 15.5 ), human-generated
abstracts, photos, or automatically generated summaries help
users select a subset of the results
for use and can

Violators
will be prosecuted.
P7001012430000000000000000002...
Figure 15.5
A Google Search result list. A summary is provided at the top
(the total number of results). Each
result includes preview information (or a snippet). Search terms
are highlighted, including “Human-
Computer Interaction Lab,” which is the expanded variant of the
search term “HCIL.” The name of
the top-level organization was added (here “National Center for
Biotechnology Information”) to
help users judge the trustiness of the information.
Violators
will be prosecuted.
P7001012430000000000000000002...
Figure 15.6
Searching for Annapolis on the real estate website Zillow
returns a list of houses and dots
displayed on a map. The two windows are coordinated; when
the cursor hovers over a house in

the result list, the location of the house is indicated on the map.
A click on the house would bring
all the details displayed in an overlapping window.
help them define more productive queries as they learn about
the contents of the items (Greene
et al., 2000). Translations may also be proposed. Allowing users
to control how results are
sequenced (e.g., alphabetical, chronological, relevance ranked,
or by popularity) also contributes
to more effective outcomes. If users have control over the result
set size and which fields are
displayed, they can better accommodate their information-
seeking needs.
Highlighting the search terms in the snippet or other preview
helps users gauge the value of
results. Previous visits are noted. For websites, the URL is
partially visible, and the name of the
organization is provided—when available—helping users gauge
the trustiness of the information.
In database search, the preview information might indicate
which collection the item belongs to or
include a photo and important attributes (Fig. 15.6 ).
Additional preview and overview surrogates for items and
collections can be created to facilitate
browsing of results. Graphical overviews can indicate scope,
size, or structure and help gauge the
relevance of collections (e.g., with maps, timelines, or
diagrams). Previews consisting of samples
from collections entice users and help them define productive
queries (Greene et al., 2000).
When the number of results is very large and metadata is
available, a useful strategy is to provide

an overview of the number of items in available categories (see
also faceted search in Section
15.3 ). For example, when searching a library catalog, the
number of books, journal articles, or
news articles can be indicated (Fig. 15.7 ) and allow users to
filter the results. When no
metadata is available, one strategy is to automatically cluster
the results based on content
analysis (see, for
Violators
will be prosecuted.
P7001012430000000000000000002...
Figure 15.7
A search for “human computer interaction” powered by
Summon for a university library catalog
returns a very large number of results. On the left, users can see
the number of results for
categories organized by content type, subject terms, or
publication date. The box provides an
overview of the results, reveals how the search was done (e.g.,
here the default search does not
returns dissertations), and facilitates further refinement of the
search. The menu at the upper right
allows users to sort results by relevance or by date. Help is
available with a “Chat now” button,
which allows users to chat with a librarian

(http://www.lib.ncsu.edu).
example, Yippy at http://www.yippy.com). This allows users to
navigate a tree of hierarchically
organized topics, but the quality and appropriate labeling of the
clusters are often problematic, so
this technique is losing proponents.
To help users identify items of interest, access to the full
document is usually necessary, with
highlighting of the terms used in the search. For large
documents, automatic scrolling to the first
occurrence of the keyword is helpful, as are markers placed
along the scrollbar to indicate the
™
Violators
will be prosecuted.
P7001012430000000000000000002...
locations of other occurrences (see Fig. 9.8 ). A common issue
when reviewing results within a
document is to have the search box hide the location of the
terms found in a document.
15.2.4 Refinement
The fourth stage is refinement. Search interfaces can provide

meaningful messages to explain
search outcomes and to support progressive refinement. For
example, corrections can be
proposed, such as asking, “Did you mean fibromyalgia?” when a
term is misspelled. If multiple
phrases were used, items containing all phrases should be shown
first and identified, followed by
items containing subsets. Progressive refinement, in which the
results of a search are refined by
changing the search parameters (e.g., search phrases but also
time range, location, etc.), should
be made convenient by leaving the search terms active—along
with an easy way to clear
them—instead of asking users to start from scratch every time.
Violators
will be prosecuted.
P7001012430000000000000000002...
15.2.5 Use
The final stage, use of the results, is where the payoff comes.
Results may be merged and saved,
disseminated by e-mail, or shared in social media. Users may
want to feed the results to a
bibliographic tool or be notified when new results become
available. Sometimes direct answers or
actions can be embedded directly in the result lists (Fig. 15.8 ),
but most often search is only

one of many components of a more complex analysis tool. For
example, powerful environments
are available for lawyers to review previous lawsuits and
assemble supporting materials for their
cases. Intelligence analysts might use tools such as nSPace from
Uncharted Software (Fig.
15.9 ) to prepare evidence-based reports. Multiple searches can
be specified at once, and
names, dates, places, and organizations are
Figure 15.8
When possible (and important), provide information or simple
actions without requiring users to
leave the search results page. On the left, Google Search users
get the answer to their safety-
critical question at the top of the result list. On the right,
Peapod shoppers looking for groceries
can specify quantity and buy directly from the list of results
after a search on “grapes.”
Violators
will be prosecuted.
P7001012430000000000000000002...
Figure 15.9
nSpace TRIST from Uncharted Software is used by analysts to
produce evidence-based reports.
For this (fictitious) criminal investigation, a user is reviewing a
collection of documents (shown as

icons at the top). The search history on the left shows three
searches. Names, places, and
organizations have been automatically extracted. Here the
search term “Laboratory” and the
person “John Panni” are selected. Snippets are shown on the
bottom left. Analysts use TRIST to
define dimensions of interest and to quickly identify documents
of interest for use in a Sandbox
(Fig. 15.10 ) (http://uncharted.software/nspace; Chien et al.,
2008).
extracted automatically. Analysts review documents and export
information into a Sandbox (Fig.
15.10 ) to organize the evidence they found, mark it as
supporting or refuting hypotheses, and
then generate reports.
Many searches are related to searches done in the past. Users
often need to find the same
information again or to continue a search started the previous
day. A search history, bookmarking,
tagging, and indication of past visits in the results (e.g., “You
have visited this page 2 times, Last
visit 1/6/2016”) all contribute to helping users re-find
information. Keeping the visited links visually
distinct reminds users of where they have been.
Designers can apply the five-stage framework to make the
search process more visible,
comprehensible, and controllable by users. The five stages are
often repeated many times until
users needs are met.

Violators
will be prosecuted.
P7001012430000000000000000002...
Figure 15.10
nSpace Sandbox from Uncharted Software™ allows multiple
analysts to organize and present
the evidence gathered from research. A variety of tools such as
node and link diagramming,
automatic source attribution, recursive evidence marshaling,
and timeline construction provide
support for analysis and reporting.
®
Violators
will be prosecuted.
P7001012430000000000000000002...
Week 10 Discussion 1
"Characterizing Data Types" Please respond to the following:
1. Justify characterizing data types according to task taxonomy.
Support your response.
2. Assess the value to an interface designer, of being familiar
with the seven basic tasks and create an argument for which

three of the seven basic tasks are the most important to
incorporate in a design.
Week 10 Discussion 2
"Information Visualization" Please respond to the following:
1. Despite increases in computing power and network
bandwidth, many user interfaces are still largely text oriented,
with a few icons and illustrations. Discuss at least three reasons
why text-oriented interfaces are still the most common.
2. Use the Internet to locate an article on information
visualization tools and identify the tool you would be most
likely to use in a design project and explain why you selected it.
3. Bottom of Form
Bottom of Form
1. Bottom of Form
Bottom of Form
Week 9 Discussion 1
"Searching Textual Documents" Please respond to the
following:
1. Your company has been hired to design a product that will
provide searches of textual documents and database querying.
Your design team has not developed a product like this before.
Examine the challenges associated with searching in textual
documents and database querying. Describe how the five-stage
search framework shared in Chapter 15 could be used to
overcome those challenges.
2. Assess current multimedia document search techniques and
suggest three techniques to improve multimedia search
experiences. Explain why you chose to recommend each of the
techniques you suggested.
Week 9 Discussion 2
"Advanced Filtering" Please respond to the following:
1. Describe the most effective advanced filtering and search
interface. Argue why the interface you chose is more effective
than others, and describe who benefits most from the interface

you are describing.
2. From the e-Activity, Read the article titled “History of
Search Engines: From 1945 to Google Today” about the history
of Internet search engines, located at
http://www.searchenginehistory.com/. Google has been the
dominant market leader for search engines for the past several
years, despite a fairly basic interface and competition from
competitors like Microsoft and Yahoo. Discuss reasons why
Google has been able to maintain such a high market share.
Also, suggest some ideas for a search engine that would be able
to take market share away from Google.
3. Bottom of Form
4. Bottom of Form

15.2 Five-Stage Search FrameworkIn designing the advanced .docx

Recommended

Recommended

More Related Content

Similar to 15.2 Five-Stage Search FrameworkIn designing the advanced .docx

Similar to 15.2 Five-Stage Search FrameworkIn designing the advanced .docx (20)

More from felicidaddinwoodie

More from felicidaddinwoodie (20)

Recently uploaded

Recently uploaded (20)

15.2 Five-Stage Search FrameworkIn designing the advanced .docx