EQUIREMENTS NALYSIS OCUMENT

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R
EQUIREMENTS


A
NALYSIS


D
OCUMENT




A
laska
D
epartment of
F
ish
&

G
ame

Salmon Data Management System



Axiom Consulting & Design



July
, 2009


Revision Sheet


Requirements Analysis Document


Page
i


Revision Sheet


Release No.

Date

Revision Description



























Requirements Analysis Document


Page
ii














The in
tent of this requirements analysis is to assist in the identification of ADF&G’s internal and
external needs for managing key salmon data and making it easily accessible to all that have an
interest in using or understanding it. By working together, nonpro
fits and government agencies
can leverage more resources to undertake important yet unfunded work that benefits managers
and stakeholders alike.





1.0 General Information


Functional Requirements Documen
t




FUNCTIONAL REQUIREMENTS DOCUMENT


TABLE OF CONTENTS


Page #


1.0 Background

................................
................................
................................
................................
....

1
-
1

1.0

GENERAL INFORMATION

................................
................................
................................
......

1
-
2

1.1

Purpose

................................
................................
................................
................................
....

1
-
2

1.3

Project Resources

................................
................................
................................
...................

1
-
2

1.4

Requirements Gathering Methodology

................................
................................
................

1
-
3

1.4.
1

Isolation of User Groups

................................
................................
................................
.....................
1
-
3

1.4.2

User Interviews

................................
................................
................................
................................
...
1
-
3

1.4.3

Workshops

................................
................................
................................
................................
..........
1
-
4

1.5

Points of Contact

................................
................................
................................
....................

1
-
5

1.5.1

Axiom Consulting & Design

................................
................................
................................
...............
1
-
5

1.5.2

Alaska Department of Fish & Game Division of Commercial Fisheries Area E Biological Staff

......
1
-
5

1.5.3

Alaska Department of Fish & Game Division of Commercial Fisheries Data Management Staff

......
1
-
5

1.5.4

State of the Salmon

................................
................................
................................
.............................
1
-
6

2.0

Statewide salmon information management

................................
................................
..............

2
-
1

2.1

Background

................................
................................
................................
.............................

2
-
1

2.2

Spatial Organization of Alaska

Statewide Salmon Data

................................
....................

2
-
1

2.3

Statewide

................................
................................
................................
................................
.

2
-
2

2.3.1

Existing Data Repositories

................................
................................
................................
..................
2
-
2

2.3.2

Deficiencies

................................
................................
................................
................................
........
2
-
2

2.4

Southeast (Region I)

................................
................................
................................
...............

2
-
3

2.4.1

Existing Data Repositories

................................
................................
................................
..................
2
-
3

2.4.2

Deficiencies

................................
................................
................................
................................
........
2
-
3

2.5

Central (Region II)

................................
................................
................................
.................

2
-
4

2.5.1

Existing Data Repositories

................................
................................
................................
..................
2
-
4

2.5.2

Deficiencies

................................
................................
................................
................................
........
2
-
4

2.6

Arctic Yukon Kuskokwim (Region III)

................................
................................
................

2
-
4

2.6.1

Existing Data Repositories

................................
................................
................................
..................
2
-
4

2.6.2

Deficiencies

................................
................................
................................
................................
........
2
-
4

2.7

Westward (Region IV)

................................
................................
................................
...........

2
-
5

2.7.1

Existing Data Repositories

................................
................................
................................
..................
2
-
5

2.7.2

Deficiencies

................................
................................
................................
................................
........
2
-
5

2.8

Current Efforts of the Computer Information Services Team (CIS)

...............................

2
-
5

3.0

PWS Salmon information management

................................
................................
.....................

3
-
1

3.1

Background

................................
................................
................................
.............................

3
-
1

3.2

Spatial Organization of PWS Salmon Data

................................
................................
.........

3
-
1


1.0 General Information


Functional Requirements Documen
t




3.3

Chum and Pink Salmon

Aerial Surveys

................................
................................
...............

3
-
3

3.3.1

Data Collection Protocols

................................
................................
................................
...................
3
-
3

3.3.2

Data Processing and Report generation

................................
................................
..............................
3
-
3

3.3.3

Status of Historical Data

................................
................................
................................
.....................
3
-
4

3.3.4

Deficiencies

................................
................................
................................
................................
........
3
-
5

3.4

Sockeye, Chinook, and Coho Aerial Surveys

................................
................................
.......

3
-
5

3.4.1

Data Collection Protocols

................................
................................
................................
...................
3
-
5

3.4.2

Status of Historical Data

................................
................................
................................
.....................
3
-
6

3.4.3

Deficiencies

................................
................................
................................
................................
........
3
-
6

3.5

Weirs, Towers and Miles Lake Sonar Escapement

................................
.............................

3
-
6

3.5.1

Data Sources

................................
................................
................................
................................
.......
3
-
6

3.5.2

In Season Management and Report generation

................................
................................
...................
3
-
6

3.5.3

Status of Historical Data

................................
................................
................................
.....................
3
-
7

3.5.4

Deficiencies

................................
................................
................................
................................
........
3
-
7

3.6

Age Sex Length Data

................................
................................
................................
..............

3
-
7

3.7

Commercial Harvest Data

................................
................................
................................
.....

3
-
8

4.0

INFORMATION SYSTEM VISION

................................
................................
...........................

4
-
1

4.1

Consolidation of User Needs into Measurable Goals

................................
..........................

4
-
1

4.1.1

Enable All Data to Be Spatially/Temporally Explicit at Multiple Scales
................................
............
4
-
3

4.1.2

Provide Granular Queriable Access to Raw Data for User Groups

................................
....................
4
-
1

4.1.3

Develop a Series of Modular Data Entry, Reporting and Data Access Tools

................................
.....
4
-
1

4.1.4

Provide Standard
ized Metadata for Datasets

................................
................................
......................
4
-
2

4.1.5

Develop Standard Operating Procedures and Data Processing Routines

................................
............
4
-
2

4.1.6

Ensure Alaska Department of Fish & Game Data Management Staff can Support, Modify
and
Improve Information System

................................
................................
................................
.............................
4
-
2

4.2

Use Cases

................................
................................
................................
................................
.

4
-
4

4.2.1

Use Case Actors

................................
................................
................................
................................
..
4
-
4

4.2.2

Use Case Diagrams and Actor Hierarchy

................................
................................
............................
4
-
5

4.2.3

Use

Case Index

................................
................................
................................
................................
...
4
-
6

4.2.4

Use Case Descriptions

................................
................................
................................
........................
4
-
6

5.0

OPERATIONAL PLAN

................................
................................
................................
...............

5
-
1

5.1

Salmon Data Consolidation Overview
................................
................................
..................

5
-
1

5.1.1

Summary

................................
................................
................................
................................
.............
5
-
1

5.1.2

Centralizing Data Storage for Salmon Data (Tier 1)

................................
................................
...........
5
-
1

5.1.3

Statewide Reporting Tools

................................
................................
................................
..................
5
-
2

5.1.4

User Interface

................................
................................
................................
................................
......
5
-
3

5.1.5

CIS Marine
r System Overhaul

................................
................................
................................
............
5
-
3

5.2

Process Flow for CIS Data Consolidation

................................
................................
............

5
-
4

5.2.1

Requirements Analysis

................................
................................
................................
........................
5
-
4

5.2.2

Educate and Enable ADF&G Staff

................................
................................
................................
.....
5
-
4

5.2.3

Generate Data Dictionary
................................
................................
................................
....................
5
-
4

5.2.4

Assessment of All Data Repositories

................................
................................
................................
..
5
-
4

5.2.5

Design ETL Process Logic/Add Value to Data

................................
................................
..................
5
-
5

5.2.6

Design Data Warehou
se Prototype Schemas

................................
................................
......................
5
-
5

5.2.7

Build Prototype ETL Processes

................................
................................
................................
..........
5
-
5

5.2.8

Assess Prototypes and Plan for Next Development Phase

................................
................................
..
5
-
5

5.2.9

Reiterate Steps 5.2.5
-
5.2.8

................................
................................
................................
..................
5
-
5

6.0

Technical framework

................................
................................
................................
...................

6
-
1


1.0 General Information


Functional Requirements Documen
t




6.1

Data Flow Diagram 1.0


Complete System

................................
................................
........

6
-
1

6.2

Data Flow Diagram 1.1
-

Data Entry System

................................
................................
......

6
-
4

6.3

Data
Flow Diagram 1.2
-

ETL Processes
................................
................................
..............

6
-
7

6.4

Data Flow Diagram 1.3
-

Interoperability Systems
................................
...........................

6
-
10

























1.0

GENERAL INFORMATION


1.0 General Information


Functional Requirements Document


Page
1
-
1



1.0

Background

In 2008 funding was awarded to the
State of the Salmon Program
-

a joint effort of the nonprofit
organizations Ecotrust and the Wild Salmon Center
-

and t
he
Alaska Department of Fish &
Game’s (ADF&G) Copper River and Prince William Sound Commercial Fisheries office in
Cordova to deepen ADF&
G’s capacity for managing their salmon population data.
The
Alaska
Department of Fish and Game Project (
SoS
-
ADF&G Project) is one of four components of th
e
SoS
-
Agency Partnership Initiative (API), a
working partnership

among three different fisheries
agenc
ies and the State of the Salmon Program

with shared
objectives

for improving salmon data
access

and interoperability.


The goal of the
SoS
-
ADF&G Project
is
:

Create web and database systems for ADF&G

staff in Cordova

in order to:



Make

it easier for ADF&G
staff to enter, edit, retrieve, and analyze escapement, age,
sex, size and harvest data.



Provide

public access to frequently requested data and information.


T
o meet the project goal, an explicit requirements analysis is needed

to assist in project
optimiz
ation from the very beginning. A Requirements Analysis can provide a framework for
project participants to assess progress at multiple points during the production process and help
evaluate whether mid
-
course corrections are warranted. It also assist
s

with

the discovery and
evaluation of similar database systems, web tools, and information development activities that
are being deployed or are under development elsewhere and which can be adopted for or should
be linked to this project. For instance, database

architecture, data dictionaries, and/or
programming developed for the
Arctic
-
Yukon
-
Kuskokwim Salmon Database Management
System
, the Bristol Bay Science and Research Institute’s Age, Weight, Le
ngth in
-
season data
reporting tool, or the
Integrated Status and Effectiveness Monitoring Program
’s data management
applications may be relevant. These kinds of knowl
edge and technical transfers will not only help
save time and money but also promote data compatibility, systems interoperability, and
coordination across jurisdictions.


The
original scope of work for the R
equirements
A
nalysis
included a
User Needs Assess
ment
; a
Data Flow Diagram
;
Measurable Goals
;
Use Case Descriptions
;
Software Requirements
Specification
; and Prototype Development. However, modifications to the R.A.’s scope
are
warranted in light of the efforts of the newly created
ADF&G Computer I
nforma
tion Services

(CIS)

team
.
The focus of the requirements analysis was to assess and plan a
salmon
data entry,
data access and reporting suite of web
based software tools for
user groups

within the Prince
William Sound (PWS) area
. Integration with existing A
DF&G data repositories and software
systems was a guiding principle for the effort.

I
n January 2009 the CIS team was formed
within

ADF&G to perform
analogous
tasks to
the SoS
-
ADF&G Project

except
at the

statewide
scale

and
for more

data types

and species
.



1.0 General Information


Functional Requirements Document


Page
1
-
2


As a result t
he
scope of this
Requirements Analysis

was
subsequently
broadened to the entire
state of Alaska

so as to put the ADF&G project appropriately within context of the CIS effort
. It
excludes software specifications and the development of a proto
type at this time. Its intent is to
provide information
adequate
to guide strategic project planning decisions

and
synchrony

of the
SoS
-
ADF&G Project and the CIS effort
.


1.0

GENERAL INFORMATION

1.1

Purpose

This
intent of this
document
is to
provide a broad a
ssessment of salmon data management practices at the
Alaska Department of Fish & Game
(ADF&G)
and some strategic guidance for improv
ing

those systems
to benefit
all
information
user
s
. Documenting system requirements from the perspective of user groups
prov
ides a solid base for the development of use cases, measurable goals, data flow diagrams and other
software design methodologies. These methodologies will enable software developers at ADF&G to
more effectively plan, design, code, implement
,
test
and maint
ain
the new salmon data management
system.


1.3

Project Re
sources

The following list of informational resources was
utilized

for the development of this requirements
analysis.




RA
Source
Diagrams

-

www.axiomalaska.com/CIS_SoS/RADiagrams.zip

The MS Visio source files for data flow diagrams and use cases.


PWS Historical Data Archive
-

www.axiomalaska.com/CIS_SoS/P
WSHistoricDataArchive.zip

File includes a compilation of PWS historic salmon metric data, Annual Management Reports and
internal data processing documentation. More specifically this archive contains tabular files for the
storage of historic weir data at

various sites in addition to the miles lake sonar escapement data.
This
archive also contains aerial s
urvey data for Coho, Chinook and
Sockeye species. Standard operating
procedures for the pink and chum aerial survey program and in season reporting sys
tems are described
within a section of the archive. The legacy SASPop escapement access database is also included.


PWS Salmon Data Matrix
-

www.axiomalaska.com/CIS_SoS/PWSDataMatrix.xls

Matrix of PWS salmon data sources,

metadata, reporting and visualization needs.



PWS
Pink and Chum Aerial Survey Data Preparation and Reporting Procedures
-

www.axi
omalaska.com/CIS_SoS/PWSChumPinkAerialSurveyDataProcessing.pdf

Operating procedures for data preparation and processing for in season reporting for pink and chum
aerial survey data. Example report output is also included in this resource.


PWS Salmon Dat
a Summary
-

www.axiomalaska.com/CIS_SoS/MoffitPWSSummary.pdf

Presentation put together by Steve Moffitt summarizing salmon data management in PWS by ADF&G.


CIS Workshop Materials
-

www.axiomalaska.com/CIS_SoS/CISDataWorkshop.zip


1.0 General Information


Functional Requirements Document


Page
1
-
3


Materials and presentations utilized during the ADF&G data management workshop at Axiom offices in
March. Also includes some products of th
e workshop that were further developed in the requirements
analysis.


CIS Workshop Audio
-

www.axiomalaska.com/CIS_SoS/CISDataAudio.zip

Partial audio recording of the discussions and pres
entation at the ADF&G data management workshop
held at Axiom offices in early March.


1.4

Requirements Gathering Methodology

1.4.1

Isolation of User Groups

Users of the Salmon Data Management System were sorted logically into two categories: Customers and

Stakeholders.
Customers

are defined as 'the primary beneficiaries of project outcomes,' while
Stakeholders

are much more on the periphery. Customers are directly impacted in their everyday
business operations by the capabilities of the information syste
m, and thus their success is tied intimately
to the overall success of the proposed system. Stakeholders are much less tied to the system but will
benefit from the added functionality that the new proposed information system will
provide
. The
information s
ystem must ultimately meet as many of the needs of its users as possible but be focused
primarily on meeting the core needs of the
c
ustomer group.

For the purpose of this analysis the
following groups were interviewed:


Customers




Copper River/PWS ADF&G C
ommercial Fisheries Staff



Statewide ADF&G
ADF&G Commercial Fisheries Staff



State of the Salmon Program



Ecotrust Copper River program


Stakeholders




Processors/buyers



Bioinformatics research community



Policy makers



Non Government Organizations (NGO
s
)



Commer
cial, recreational, subsistence and personal user
s



Funding Organizations


1.4.2

User Interviews


Th
e

initial step of the requirements analysis involved interview
ing

all potential user groups
and
documenting specific user needs. A concrete set of standard
i
zed

interview questions
was

drafted to
ensure consistent and meaningful responses from the pool of potential users. These interviews provided a
foundation for the subsequent steps of the requirements analysis.


Follow
-
up interviews were performed to reeng
age various user groups. These interviews were much less
structured and involved more granular discussions of issues. Additional interviews were conducted
primarily with the various components of the ADF&G
customer
group (Area E Biologists and Statewide
Programmers) and provided more specific information on an as
-
needed basis.


1.0 General Information


Functional Requirements Document


Page
1
-
4



1.4.3

Workshops


A workshop was held with
ADF&G

programming staff to isolate system requirements
according to

their
core needs
.
Both regional data man
agement staff and statewide CI
S team

members attended the
workshop.
This workshop included discussions of existing salmon data management systems and data
repositories
, regional user needs and data management efforts in progress
.
A
n overarching three
-
year
implementation plan was devel
oped as a
result

of this workshop. The three
-
year implementation plan is
further detailed in Section 4.0 INFORMATION SYSTEM VISION.



1.0 General Information


Functional Requirements Document


Page
1
-
5


1.5

Points of Contact


1.5.1

Axiom
Consulting & Design


Rob Bochenek

Information Architect

Anchorage
, Alaska


(907)

230
-
0
304

rob@axiomalaska.com



Shane StClair

Software Engineer

Anchorage
,

A
laska

(
360
)

450
-
3574

shane@axiomalaska.com


1.5.2

Alaska Department of Fish & Game
Division of
Com
mercial

Fisheries
Area
E Biological Staff



Steve Moffitt

PWS/CR Area Research Biologist

Cordova, Alaska

(907) 424
-
3212

steve.moffitt@alaska.gov


Glenn Hollowell

Area Management
Biologist

Cordova, Alaska

(907
) 424
-
3212

glenn.hollowell@alaska.gov


1.5.3

Alaska Department of Fish & Game
Division of Commercial Fisheries
Data
Management Staff


Kathleen Jones

Data Processing Manager II

Headquarters

Juneau, Alaska

(9
07) 465
-
4753

kathleen.jones@alaska.gov




1.0 General Information


Functional Requirements Document


Page
1
-
6


Tracy Olson

Analyst/Programmer IV

Headquarters

Juneau, Alaska

(907)465
-
6350

tracy.olson@alaska.gov


Holly Krenz

Analyst/
Programmer

IV

Region III

Anchorage, Alaska

(907)267
-
2418

holly.krenz@alaska.gov


Heath Kimball

Analyst
/
Programmer

III

Region II

Anchorage, Alaska

(907)267
-
2894

hea
th.kimball@alaska.gov


Ivan Show

Analyst/Programmer V

Region I & II

Juneau, Alaska

(907)465
-
6110

ivan.show@alaska.gov


Scott Johnson

Analyst/Programmer IV

Region I

Douglas, Alaska

(907)465
-
4242

scott.johnson@alaska.gov


1.5.
4

State of the Salmon


Cathy P. Kellon

Ecotrust

Portland, Oregon

(
503
)
467
-
0791

cathy@ecotrust.org


Rich Lincoln, Director

Wild Salmon Center

Portland, Or
egon

(
971
)
255
-
5575

rlincoln@wildsalmoncenter.org


1.0 General Information


Functional Requirements Document


Page
1
-
7



P.S. Rand, Ph.D.


Wild Salmon Center

Portland, Oregon

(
971
)
255
-
5546

prand@wildsalmoncenter.org








1.0 General Information


Functional Requirements Document


Page
1
-
1




2.0 Statewide Salmon Information Management


Functional Requirements Document

























2.0

STATEWIDE SALMON INF
ORMATION MANAGEMENT


2.0 Statewide Salmon Information Management


Functional Requirements Document


Page
2
-
1


2.0

S
TATEWIDE SALMON INFO
RMATION MANAGEMENT


This section
provides
a brief summary of current data management practices for salmon monitoring data
produced by
ADF&G
on a statewide scale. P
ast information management of salmon data are partitioned
across
four management
regions, with very little coordination between regions. The following content
provides a description of how data are stored and existing software systems across regions. Also

included
is a section
summarizing

the
strategies of the
Commercial Fisheries Business Intelligence Group (BIG)
.
The
BIG
team

was recently created (January 1, 2009) to develop standardized statewide data entry and
reporting tools as well as develop a data

management framework for centralizing the storage of fisher
ies

data. Existing ADF&G data management system
s

and repositories have been documented to the best
ability of the Axiom
c
onsultants. This information was gathered through interviews and workshop
s with
ADF&G programmers

and data managers
.

2.1

Background

According to Alaska’s constitution, state government must manage its natural resources to the “maximum
benefit of its people” and manage its “renewable resources on a sustained yield basis.” These

directives
provide the impetus for ADF&G’s management of statewide salmon resources. ADF&G has utilized an
escapement goal based fisheries management system developed by the University of Washington since
1959. Effective sustainable yield management of sa
lmon runs requires collection of
monitoring
data on
salmon escapement and commercial and subsistence harvests. These data exist in various formats and
states of accessibility among the four commercial fisheries management regions.

2.2

Spatial Organization
of Alaska Statewide Salmon Data




Figure 1.
A
DF&G Division of Commercial Fisheries management region

boundaries.
From
http://www.cf.adfg.state.ak.us/regnmap.php
.


2.0 Statewide Salmon Information Management


Functional Requirements Document


Page
2
-
2


The ADF&G
Division of
Commercial Fisheries partitions the state of Alaska into four management
regions: Southeast (Region I), Central (Region II), Arctic
-
Yukon
-
Kuskokwim (AYK or Region II
I), and
Westward (Region IV).

2.3

Statewide

2.3.1

Existing Data Repositories

State commercial harvests

for salmon

dating back to 1969 are recorded in a fish ticket database named
Zephyr. The harvest data exists in two distinct database structures: one
cont
aining
data before 1974 and
another for 1974


present. Currently, paper copies of fish tickets must be entered by ADF&G
technicians post
-
season. A
more advanced harvest tracking

system called eLandings is currently in
development by
an interagency team,

w
hich will allow for in
-
season collection of harvest data, including
electronic reporting by tenders and processors.

The system for salmon is in beta this year with select
processors and tender operators.


eLandings uses a unified Java enterprise codebase a
nd Oracle database for its various client and server
components. These client components include a web interface for processors, a desktop application for
catcher/processors, and a desktop application for ADF&G staff and enforcement. Disconnected client
ap
plications

for at
-
sea processors (SeaLandings)

report to the central server via data email
.
Also in
development is a separate desktop application for tender operators called 'tLandings', commonl
y referred
to as the 'Tender Workstation'. A transfer of data
occurs from the Tender Workstation to eLandings via a
thumb
-
drive delivered to the process
ors
.


The ADF&G Mark, Tag, and Age (MTA) Laboratory also houses several statewide data repositories,
including otolith and coded wire tag (CWT) mark/recapture databas
es. In addition, the MTA
L
aboratory
hosts a statewide age, sex, and length (ASL) data repository. Management regions can currently upload
ASL inventories and datasets into the repository provided they conform to a published specification.


The ADF&G Gene C
onservation Laboratory maintains a statewide genetics database called Loki. Loki is
written in Java with an Oracle database backend. The application was rewritten in 2005 in the Java
Swing framework using the Oracle Application Developer Framework (ADF) an
d Business Components.
This application is used to store molecular genetic markers used in stock identification. The system is
currently being migrated to a Flex
-
based user interface with Java middleware and an Oracle database
repository to improve functio
nality and meet current departmental
technology
standards.

2.3.2

Deficiencies


ADF&G staff continue to
input
data
to
the le
gacy commercial harvest database

(Zephyr) during the
transition to eLandings.
The Zephyr desktop application is the major input and r
eporting tool for
historical commercial havest of Salmon and is written in a product which has reached the end of it’s life
cycle.


Currently reporting
capabilities
in the eLandings system
are

underdeveloped. The unified codebase
underpinning the system r
equires that a cumbersome redeployment be made whenever new reports are
created
, making reporting improvements labor intensive.

The initial intent of the system was for the
interagency participants to report from their historical database of record, and no
t the eLandings data.



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Management of geospatial data in ADF&G is currently weak. Locations are stored as hierarchical codes
rather than spatially explicit data. Many locations, especially fishery statistical areas, have changed over
time yet retained the s
ame name

and code
. Documentation of these changes and adjustment of historical
data to fit new location definitions
are

inconsistent across regions.


Although
a statewide ASL repository is maintained by the MTA Laboratory, use of this system outside of
Reg
ion I has been minimal due to data quality concerns or technological barriers. ADF&G data
management staff report that efforts will be made in the near future to
increase
u
sage

of
the repository.
Public interfaces to this data are somewhat difficult to use
, and output is only possible in a tab separated
text
data dump format.

2.4

Southeast (Region I)

2.4.1

Existing Data Repositories

Region I has utilized a data management application called
Integrated Fisheries Database
(
IFDB
)
/Alexander since 1994. Alexande
r is written in the Centura programming language, utilizes an
Oracle database, and includes both a server component and client desktop applications. The application
includes data management forms and approximately 125 preconfigured reports for salmon in ad
dition to
similar tools for other fisheries. The application has approximately 130 users, consisting of all Region I
commercial fisheries staff.


Various salmon datasets are managed by Alexander. Commercial harvest data dating back to 1969 is
imported from

the statewide Zephyr fish ticket database on a nightly basis. In
-
season commercial harvest
data are also entered to manage the seine fishery. Data on fishery openings are available back to 1969.
Personal use and subsistence data are available from 1985 to

the present. Troll fishery performance data
are stored in the system for use in in
-
season catch estimation. 200,000 aerial survey data records dating
back to 1960 exist in the system. Weir data also exists for about 75 weirs dating back to the early 1900s
.
Approximately 4 million ASL biometric records dating back to 1982 also exist in the system. Finally,
historical

pink salmon sex ratios are available for in
-
season run timing comparisons.


The Alexander system is outdated and regional staff are in the pro
cess of developing a web
-
based
replacement system called Zander. Zander’s user interface will be developed in Flex 3 and access an
Oracle database via Java middleware. These technologies follow Fish and Game departmental
technology

standards.

2.4.2

Deficie
ncies

Region I’s data management system currently lacks fishwheel data from the Chilkat and Taku Rivers,
which is used for in
-
season management. Troll fishery log book data are also missing from the system.
Regional data managers noted the lack of a mobile

ASL data collection application and suggested that
this might be a good candidate for a development project to be used statewide. Current reporting
capabilities also need to be expanded. Region I’s data management system
cannot currently store or
display
geospatial data. As with other regions, locations in collected data are currently recorded as codes
and do not have specific associated geospatial data.




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2.5

Central (Region II)

2.5.1

Existing Data Repositories

Region II uses a catch and escapement tool c
alled Mariner. Mariner is a collection of four distinct
applications and includes components for harvest data, escapement data, and news release management.
The application was created for
PWS

and has been modified for use in Cook Inlet and Bristol Bay. It
s
news release system is also utilized by Region III. The application is written in PHP and uses an Oracle
database.


Managers generate daily reports from Mariner to inform management decisions, and the availability and
accuracy of this data are critical.
The Mariner system also powers several harvest, escapement, and run
timing reports on the Region II public website
,
. A developer was hired by ADF&G in early 2009 to
maintain and continue development on the Mariner system. Efforts are being made to
integra
te
this
system with the
new
statewide eLandings harvest reporting system.


Other specialized management systems include Sedna for the Homer groundfish and shellfish fisheries
and the Fisheries Data Management System (FDMS) for Bristol Bay salmon. The FDMS
includes a
mobile data collection application and is used to streamline the collection of ASL, scale, and genetic
samples.


See section 3 for descriptions of other PWS specific data management systems.

2.5.2

Deficiencies

ADF&G staff are currently assessing

the status of Mariner system and evaluating user needs.
Fisheries
managers have commented that the news release system is inflexible and in need of updates.

2.6

Arctic Yukon Kuskokwim (Region III)

2.6.1

Existing Data Repositories

Region III has aggregated

salmon ASL; aerial survey; tower, weir, and sonar escapement counts; and
subsistence data into a data repository and management system. Datasets in this system generally extend
back to the early 1960s. Region III has completed a comprehensive data salvage

effort, and all available
historical data has been digitized from paper archives. The resulting electronic data are stored in a
Microsoft SQL Server database. A public web interface and a desktop management client have been
developed in ASP.Net. The web i
nterface allows for selection and download of raw data and a few
summary reports. The desktop client tool is intended for internal use by ADF&G and allows for entering
and editing new data.


Region III must contend with internet connectivity issues due to
the remote nature of many of its field
camps, and for this reason several disconnected in
-
season management tools developed in Microsoft
Access and Excel are still in use. Data from these tools are imported into the central historical repository
post
-
seaso
n. Data from various radio telemetry, capture/recapture, and test fishery projects are stored in
A
ccess databases and not
yet
included in the central repository.

2.6.2

Deficiencies

Reporting and data visualization capabilities of Region III’s data manageme
nt system are currently very
limited. Raw data can be downloaded, but high level summary data products are not available. Data

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management staff indicate that a more user
-
friendly data import process is also desirable. Development
of a catch and escapement
management tool capable of historical comparison
that can function without
internet connectivity
would be ideal for Region III. Region III’s salmon data management system also
lacks a geospatial component

and all location
data are

currently stored as codes

rather than explicit
geospatial data;

data management staff indicate that management of poorly defined and dynamic location
codes is a major problem.

2.7

Westward (Region IV)

2.7.1

Existing Data Repositories


Region IV is currently in the proces
s of migra
ting salmon data into a relational database system from
various R:B
ASE
, Access and Excel electronic files. This project is still in the planning stage and much
work remains to be done. Region IV collects harvest, ASL, daily escapement, and aerial survey da
ta.

2.7.2

Deficiencies

Region IV does not yet have a functional centralized database. Salmon data cannot be accessed on the
web
; neither

raw
nor summarized outputs are available
. The current lack of a centralized system leaves
datasets prone to organizatio
nal, versioning and quality control problems.

2.8

Initiatives

of the Computer

Information Services Team (CIS
)


The CIS team
’s long range data management goal is to have a single unified data warehouse for all
ADF&G data with shared reporting tools.
The pro
ject team named the Business Intelligence Group (BIG)
has been formed within CIS and

is investigating possible data warehous
ing

architectures, surveying user
needs, and evaluating several business intelligence reporting tools to interface with the proposed

data
warehouse.


Another project team within
CIS

is the eLandings

team
. This team

is focused on
continuing development
of
the statewide com
mercial harvest tracking system
:
eLandings (see 2.3.1). Another effort called the
Standard Specimen ID (SSID)
projec
t
aims to assign system
-
wide unique identifiers to sample efforts at
their finest level of granularity (e.g. fish, aerial survey sightings, crab pots) to allow linking of samples
between
information
systems. The existing Mariner data entry and reporting s
ystem is currently
being
overhaul
ed
. More information concerning CIS

team

efforts and strategic plans are available in
section

4.












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3.0

PWS SALMON INFORMATI
ON MANAGEMENT



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3.0

PWS SALMON INFORMATI
ON MANAGEMENT


This section detai
ls current data management practices for salmon monitoring data produced by the
Cordova ADF&G office for in
-
season management. The following content provides a description of the
ways in which data are acquired, processed and finally distilled into data pr
oducts which then assist area
biologists in managing salmon fisheries, monitoring long
-
term trends, and developing forecasts. Current
interactions with existing ADF&G data management systems and repositories have been documented to
the best ability of the
Axiom
c
onsultants. This information was gathered through interviews and
workshops with relevant ADF&G staff (Area E biologists and ADF&G statewide programmers).
The

current
lack of enterprise level data management methods severely limits user groups outsid
e the small
group of biologists at the ADF&G office from accessing and utilizing salmon metric data.

3.1

Background


ADF&G

has actively managed
Alaskan
salmon resources since the early 1960s.
ADF&G’s
current
escapement survey effort
in PWS
is comprised of

two distinct aerial survey designs
(one for chum and
pink salmon and the other for sockeye, coho, and Chinook salmon)
and a series of weir, tower and sonar
projects
. Commercial harvest data are also monitored closely. However, data entry, processing and

reporting are cumbersome and require duplication of effort.


3.2

Spatial Organization of PWS Salmon Data


ADF&G biologists have broken the PWS sound area into 10 management areas:




Bering River



Coghill



Copper River



Eastern



Eshami



Montague



Northern



North
western



Southwestern



Unakwik



The following diagram (Figure 1) provides a spatial representation of the management areas and
locations of pink salmon hatcheries and in situ sampling locations.










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Figure
2
.

Prince William Sound Management Area c
ommercial fishing districts, salmon hatcheries, weir
locations, and Miles Lake sonar camp.


Additionally, the department has
further delineated

the PWS area into statistical reporting areas. The
following diagram (Figure 2) displays the spatial bounds of t
he smaller statistical reporting areas
contained within each management area.


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Figure
3
.

Prince William Sound Area showing commercial fishing districts and statistical reporting areas.


These spatial areas are important for understanding reporting require
ments, management decisions and
historic
al

archived data for ADF&G Prince William Sound.

More information on how the Cordova
ADF&G office documents their salmon metric information can be acc
essed in the annual management
r
eport archive contained in the
historic PWS data archive

.


3.3

Chum and Pink Salmon Aerial Surveys

3.3.1

Data Collection Protocols


Between the months of June and September, biologists fly aerial surveys once
per week to count salmon
escapement in 215 index streams. Each weekly flight surveys all 215 streams. Each index stream is split
into three sections corresponding to the bay, mouth and stream extent, and fish counts are grouped into
each
section
for every
index stream.
Fish counts are recorded for each targeted species

(chum, pink and
others). GPS

derived

flight trackline
data are

also
recorded and archived. Data are collected on paper

forms

and entered into an R
:
BASE data entry program at the Fish & Game o
ffice.




3.3.2

Data Processing and Report generation



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Once data has been entered into the R
:
BASE interface it is then exported into a comma
-
separated value
(CSV)
output file and run through a
F
ortran application to compare in

season observations against t
he
historic
al

data archive. Five in

season reports are produced from the
F
ortran application for

each species
.

These reports include the following for chum and pink salmon:



Daily counts per sub district



Cumulative counts per sub district



Daily counts per
stream



Cumulative counts per stream



Weekly counts per sub district


The procedure for preparing, loading and processing the data is well documented in the pinksalmon
reporting procedure section of the
PWS historic data archive
.


The reports compare actual

observed

counts in the various sections of stream (bay, mouth, stream and
cumulative) against
forecasted
counts and
generate
a percent deviation metric

that represents

forecast

accuracy
. These outputs are produced for each individual stream system as well as larger groups of
streams systems. Data for retrospective analysis is limited to
either
even
or
odd years due to the two
-
year
life cycle of pink salmon.

An example of the ab
ove reporting structure can be accessed in the
PWS
pink
and chum aerial survey data preparation and r
epo
rting p
rocedures

resource.

3.3.3

Status of Historical Data

Historical

escapement data for various species
for the years 1960
-
1999
was standardized and loaded into
an ADF&G SASPop database (M
icrosoft

Access 2000) in a format that the department created in the late
1990s to standardize all escapement data.
I
nspecti
on of the SASPop database
reveals

that the structure is
not efficiently normalized
;
the overuse of relationships between table entities
is
illustrated in the diagram
of the SASPop database structure

(Figure 3)
. Consequently, data from 2000 forward is store
d in a
n

R
:
BASE database.

Historical data can be found in the
PWS historic data archive
.




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Figure
4
. Entity diagram of SASPop
historical

escapement data repository.



3.3.4

D
eficiencies

Data
preparation for in season reporting i
s very cumbersome and inefficient for chum and pink aerial
survey data. Report generation requires
transforming
data in
to

various
transfer formats to
facilitate
move
ment

between the
R:BASE
data entry sy
stem and the
Fortran analysis
program which builds the
reports for in
-
season management decisions. There is no functional
historical

data architecture that
allows easy access to
raw
data
,
custom queries

and data summary products
.


3.4

Sockeye, Chinook, a
nd Coho Aerial Surveys

3.4.1

Data Collection Protocols


Between the months of June and October biologists fly approximately 20 aerial surveys
over 84 index
streams
to count escapement for the
sockeye
, Chinook and
coho
species. Data are collected via a PDA

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application developed by
\
PWS ADF&G biologist Glenn Hollowell. Aerial flight
GPS
trac
ks

are

recorded and point
-
based spatial data can be associated with individual fish groups through comparison
of the PDA application data with
a
shapefile
generated
from t
he aerial survey
flight
track data. Two
shapefiles are produced

for each survey effort
:

one for the survey track and the other for the fish groups
and counts

observed
.

3.4.
2

Status of Historical Data

Data exists in a spatially enabled electronic format
(se
e 3.4.1)
from 2007 forward but all other
historical

aerial survey data (1950s


200
6
)
are
stored in aerial survey index area report forms.
These report forms
document observations of fish in the 84 index streams in a tabular format with species as columns

and
index streams as rows.
Some more current data
exist
in electronic format
s

but older data are
on
paper
forms.

Historical data can be found in the
PWS historic data archive
.

3.4.
3

Deficiencies

Aerial survey data for
s
ockeye, Chinook and
c
oho
salmon
exists in a large number of formats, which
makes analyzing and utilizing the data over long time
series

difficult. In addition, the most current data
from 2007 forward is not tied
together well because each survey result exists in its own
separate
electronic file. A centralized spatial database
could

be developed to organize and enable this information.
Data entry tools also do not exist for these data types.


3.5

Weirs
, Towers

and

Miles Lake Sonar Escapement

3.5.1

Data Sources


Data are collected to measure escapement for
sockeye
and
coho salmon
at a series of weirs in the PWS
area. These sites include:




Coghill Weir


Sockeye (1974


current)



Eshami Weir


Sockeye (1954


current)



Shrode Weir


Sockeye (1957


1974 intermittent)



Long Lake Weir


Sockeye and Coho (1974
-
2006)



Tanada Weir


Sockeye (2001
-
2006)



Gulkana
River Tower


Chinook (2002
-
Current)


Data
from weir projects
are collected and recorded as daily summaries (i.e. tot
al number of fish species
observed passing through the weir per day).


Salmon escapement is also measured using sonar devices at Miles Lake. Data exists from 1978 to the
present. Escapement is measured at the entrance to Miles Lake, and salmon species are

undifferentiated.


3.5.2

In Season Management and Report generation


Tower and weir data are utilized in an ancillary fashion during an active fishery.
The
Miles Lake sonar
project
is heavily relied upon to ensure that daily escapement goals are being me
t for the Copper River
system. Data entry for these data types is primarily a manual process that involves phoning in daily

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escapement summaries. Data are transcribed multiple times into various spreadsheets which are then
used for immediate management d
ecisions.


3.5.3

Status of Historical Data


Historical

data for weir and
Miles Lake
sonar escapement metrics are stored in standardized Excel
workbooks
. Though each
workbook
contains a unique
spreadsheet
structure, the following components
are generally

available for all data sources:



Actual Daily Counts


number of fish counted moving upstream
for each
day for
historical

and
current years



Actual Daily Cumulative


cumulative
total number of fish that have been observed moving
upstream that season
for
ea
ch day for
historical

and current years



Daily Percentage


percentage of the total run observed
for each
day for
historical

and current
years



Daily Cumulative Percentage


cumulative
percentage of the total number of fish that have been
observed moving ups
tream that season
for
each day for
historical

and current years



Graphs depicting all of the above relationships for
historical

and current years


Historical data can be found in the
PWS historic data archive
.

3.5.4

Deficiencies


Serious inefficiencies exist in the
methods by which
weir and sonar data are recorded, stored and utilized
for in
-
season management. Data are entered by hand into Excel spreadsheets and then
manually
mani
pulated to

create graphs for management use. Each new management season requires preparation of
the master
workbook
beforehand. Data sharing and distribution
are

problematic. Multiple

data entry staff
manipulating
the same data at the same

time

leads to b
ifurcations
. Comparison of real
-
time data
to
historical data
requires manual processing, and other types of analysis are very limited. Submitting data
to statewide salmon metric repositories is cumbersome and requires duplication of data entry effort.


3.6

Age Sex Length Data


ASL
data have been collected in the
PWS

area continuously since the early 1950s. There are
approximately 575,000 ASL records for multiple species. A majority of the data are for the
sockeye
species (395,000 rows) with smaller numbers
for the
coho
, Chinook and
chum
species. These data have
been organized into a series of M
icrosoft

Access database and Excel files in formats dictated by ADF&G
data managers. ASL data
i
s

not heavily relied upon by managers
in
current in

season management
p
ractices. There is no automated data entry and data access system for this data.
Data are

manually
entered into
Excel
spreadsheets and
stored locally.





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3.
7

Commercial Harvest Data


Commercial harvest data in PWS is handled in a manner consistent with

other areas (see section 2.3).










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4.0

INFORMATION SYSTEM VISION



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4.0

INFORMATION SYSTEM V
ISION

This section was developed from information gathered during user interviews and the ADF&G
workshop.
Please note that it
attempts to lay
out a

sample information systems vision for statewide
salmon data consolidation
for the sole purpose of

identif
yi
ng
how the SoS
-
ADF&G project
might
best
add value to CIS efforts and meet the needs of ADF&G staff in Cordova.
The analysis is
not

intended to
presume what th
e

ultimate statewide vision will be.
To assist the reader, sidebar notations are written to
identify components that are understood to be the purview of the CIS group or highlight areas with
special potential for collaboration or added value
.


As noted above the
following section frames a conceptual vision of the
ADF&G
salmon data
management system

for the purpose of framing and integrating the project’s requirements analysis
. First,
measurable goals are isolated from user needs which were
i
dentified
during the interview process.
Second, use cases are isolated to envision how users will interact with the system to meet their individual
needs.

4.1

Consolidation of User Needs into Measurable Goals


Information gathered from the initial user int
erviews was combined into a series of coherent measurable
goals. This process involved

merging analogous user needs in addition to reconciling conflicts between
user needs in opposition to one another.

The combined list of reconciled and consolidated user
needs was
then
distilled
into a much smaller set of measurable goals. This process involved looking at
individual
user needs as clues to larger overall goals that the system will need to meet.
The development of
succinct
measurable goals
will allow
system
prototyping and
iterative
development phases
to
commence.

4.1.1

Provide Granular
Queriable
Access to Raw Data for User Groups


Although summarized data products will be the desired output for most user groups (
e.g. the
general
public, fisheries managers),
other groups including the research and academic communities need access
to raw data. While summarized data products provide an efficient high level view that allows easy
ingestion of datasets by consumers
with
varying degrees of expertise, expert users ne
ed raw data for
analysis, modeling and custom visualizations. As with summarized data products, raw data should ideally
be accessed through a universal query interface where users can select datasets, geospatial extent,
temporal extent and
data
output form
at.

4.1.2

Develop a Series of Modular Data Entry, Reporting and Data Access Tools


Despite the general similarity of salmon datasets collected statewide, efforts to develop data entry,
reporting and access components are currently duplicated across the fou
r management regions. Regional
data management staff operate mostly in isolation from other regions. Sharing of developed technologies
between regions is minimal; when sharing does occur it often requires cumbersome modifications to
existing applications.
The r
esulting functionality
of modified applications
is often impaired because
existing applications were not developed with modularity in mind.
Currently p
ublic access points for
commercial fisheries data are spread out in various places on the ADF&G comm
ercial fisheries website
and utilize a variety of dissimilar user interfaces.


The development of modular

and

adaptable
data entry, reporting and access
components would reduce
effort duplication
between
management regions and allow regional data managemen
t staff to focus on the

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fulfillment of data management needs
that are
unique to their region. Modular shared data entry systems
would allow for standardized quality control and geospatial management. Modular reporting and data
access tools would provide in
ternal and public data interfaces with consistent look, feel and functionality
across regions, which would in turn decrease the learning curve for users

who are new to the system
.


4.1.3

Provide Standardized Metadata for Datasets


While providing intuitive

access to raw data and data summary products is important, these datasets are
of limited value without the accompaniment of
descriptive
metadata records. Federal Geographic Data
Committee (FGDC) compliant metadata records are required for all federally fu
nded projects. Even when
not required
the production of
FGDC
meta
data are

considered a best practice for all projects since
this
allow
s

for a dataset’s inclusion into data clearinghouses, where
it
can be discovered and used by others.
Standardized metadata

links agency collected data to the rest of the world; datasets without proper
metadata are subject to misinterpretation by users, isolation from other associated datasets, exclusion
from analyses and visualizations where they would have been applicable an
d possible extinction when
they cannot be identified or verified. Besides allowing for data discovery, metadata can also
communicate detailed data collection methods, notes on data quality and other important aspects that are
not captured in the data itsel
f. Ideally a standards based data dictionary or metadata ontology
should
be
developed to explicitly document data syntax and quantitative meaning for salmon information.


4.1.4

Develop Standard Operating Procedures and Data Processing Routines


Standard op
erating procedures for data management must be developed and documented. The
data
collection methods
, data entry and validation protocols, and
established pro
cedures for
migra
ting

validated data into the central repository must all be explicitly stated to
prevent confusion and ambiguity
both inside and outside of
ADF&G
. Clearly defining data entry, validation and vetting procedures will
ensure that all points of data input are held to similar standards of quality.


Datasets will also need to be processed wh
en they are imported into the central data warehouse.
Examples of possible processing routines include code conversions, data validation and structural
conversions (for example, OLTP to OLAP data structures). These procedures should be developed so
that th
ey can be run regularly, independently, and reliably.

4.1.5

Ensure Alaska Department of Fish & Game Data Management Staff can
Support, Modify and Improve Information System


Data management staff must have a thorough understanding of all technologies, appl
ications and
procedures used in the information system to allow for maintenance and development. Thorough and
evolving
documentation of the information system is an important component of this goal. Explicit
descriptions of database structures, data flow,
data quality standards, application function and
organization and other aspects of the system must be documented and accessible to data management
staff. Staff must also be allowed sufficient time to maintain this documentation
;

managers often do not
facto
r documentation
time
into their project estimates. A

wiki
-
style documentation vehicle would be well
suited for this requirement.



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Data management staff must also receive adequate training on the technologies used by the information
system to allow for debu
gging and development. This training can include classroom style classical
training, web based seminars, online tutorials or books. Data management staff must also stay aware of
emerging technologies that can enhance the information system.


4.1.6

Enable A
ll Data to Be Spatially/Temporally Explicit at Multiple Scales


Though virtually all salmon metric data stewarded by ADF&G describes measurements or values which
have an explicit spatial context, the raw data itself is poorly spatially enabled. This is app
arent in the
prolific use of common names in various salmon metric datasets and databases. When sampling has
occurred in a stream system the biologist will typically denote the spatial location with a name or code
which has traditional been used to describ
e a location. An example of this is the “Beaver Creek”
phenomenon in which measurements are associated with a stream extent by its common name. The
problem arises when a second biologist associates metrics with a second distinct stream that is also called
“Beaver Creek.” Additionally, locations and extents of common names, management areas and statistical
areas have changed over time. As a hypothetical example, statistical area 334
-
51 might represent a
completely different area in 2007 than it did in 1985.
Finally, the limited number of available predefined
location codes often cause biologists to choose location codes that do not precisely describe a project’s
sampling location; the code for an entire stream system may be used when sampling actually took pl
ace
on a small subset of the stream. These inconsistencies lead to deep seated problems with retrospective
analyses and attempts to look at long term change at various spatial scales.


Developing a data management framework which explicitly defines geometr
ic objects (points, lines and
polygons) and couples these objects with data will greatly assist all user groups in accessing,
understanding, analyzing and utilizing salmon data. Users will be able to intuitively drill down into
hierarchically nested spatia
l regimes to look at system change on a watershed, basin, sub
-
basin or finer
scale. In addition, spatially enabling data will provide transparent data access for user groups who are not
familiar with the internal ADF&G spatial qualifiers (e.g. common name,

management area, stat area).
Latitude and longitude are a universally understood metrics for spatial description. Providing a geospatial
context for information is a fundamental requirement for developing the ability to programmatically
marshal data betwe
en computer systems via standardized interoperability protocols (Web Feature Service
[WFS] and Web Map Service [WMS]).





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4.2

Use Cases


Use cases provide specific examples of how users will interact with the system to meet a specific goal. A
single use
case can span multiple logical user groups and is more oriented toward a user’s business role.
In addition, multiple use cases can be directly associated with a single user group. Use cases will detail
scenarios in which users approach the system to meet s
pecific needs and then detail the sequence of
events which take place to meet those specific needs.

4.2.1

Use Case Actors

Use case actors are logical entities which are aggregated from the various ways users will interact with
functionality of the system.

In many cases actors can be organized into hierarchically structured schemes
in which the properties of one actor are inherited by other logical actors. Each actor may have one or
more use cases associated with it. For organizational purposes actors have
been grouped into two distinct
domains:
data report/access

and data entry/management.


Use Case Actors


Data

Report
/
Access Domain



Generic Data Browser/Access



Resource Manager (ADF&G)



Resource Ecologist (Agency or NGO)



Commercial Fisherman



Public/Recreat
ional Fisherman



Biometrician, Bio
-
Informatics Researcher



Processors


Use Case Actors
-

Data Entry/Management Domain



Generic Data Entry



ASL Data Entry



Aerial Survey Data Entry



Weir/Tower/Escapement



Genetics Data Entry











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4.2.2

Use Case Diagrams and Ac
tor Hierarchy

Use case diagrams provide a graphical representation of how various use cases apply to system actors
organized into hierarchy structured schemes. Use case actors are represented as stick figures, use cases
are represented as yellow ellipses a
nd use case inheritance between actors is represented as directional
lines noted with the <<extends>> operator. A use case actor that extends another actor will inherit all the
use cases associated with the actor the <<extends>> operator is flowing to.


The following use case diagram displays use cases and inheritance betw
een various use case actors for
the
Data report/access

Domain.




Figure 6
. Use case diagram for
data report/access

domain.

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4.2.3

Use Case Index

Use cases are
listed

into an index f
or organizational purposes.


Use Case
ID

Use Case Name

Primary Actor

1

Generate Ad Hoc Reports

Generic Data Browser

2

Visualize
/Access Data through Online Map

Generic Data Browser

3

Access Pre
-
Batched Reports

Generic Data Browser

4

Access/Query Raw/Sum
marized Data

Generic Data Browser

5

Create/Access Stored Profile

Generic Data Browser

6

Create Localized Reporting Framework

Resource manager

(ADF&G)

7

Authorize Fishery Announcements

Resource manager

(ADF&G)

8

Publish Forecast Data

Resource manager

(A
DF&G)

9

Access Data Interoperability Formats

Biometrician/Bio
-
Informatics Researcher

10

Access Standardized Metadata

Biometrician/Bio
-
Informatics Researcher

11

Create Customized Reporting Framework

Resource Ecologist (Agency or NGO)

12

Access Outreach
Products

Public/Recreational Fisherman

13

Access Fishery Announcements

Commercial Fisherman

14

Access Forecast Data

Processors

15

Access Near Real Time Catch Data

Processors


Table 1. List of all use cases and corresponding actors for
data report/acce
ss

domain.


4.2.4

Use Case Descriptions


Use Case ID:

1

Use Case Name:

Generate Ad Hoc Reports

Use Case
Description:

A user approaches the system to create a report with framing parameters.
These parameters could include: time period, location, salmon

metric and
applicable analysis/aggregation. The system enables the user to rapidly create
the report and receive the information in the format required by the user.

Primary Actor:

Generic Data Browser

Basic Flows:

1.

User navigates to statewide public AD
F&G fishery data access page and
chooses to build a report.

2.

User chooses what type of report and metric(s) from selection lists.

3.

User narrows information request by spatial proximity, time period and
further modifications to analysis and aggregation.

4.

User
reviews request and submits request

5.

System responds with summary or entire result set depending upon size.

6.

System prompts user for download options (e.g. pdf, excel, shapefile

).

7.

User selects output options and then downloads report output if

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possible.

Al
ternate Flows:




Use Case ID:

2

Use Case Name:

Visualize
/Access Data through Web Based Map

Use Case
Description:

User navigates a web based map, manipulates the map layers and base layers
and can access/download data through the online map interface.

Primary Actor:

Generic Data Browser

Basic Flows:

1.

User navigates to statewide public ADF&G fishery data access page and
chooses to view map based data visualization.

2.

User interacts with system.
User browses through various data layers
and turns them on or

off from a tree control. Another control allows the
user to filter
data
by time constraints.

3.

User can request data from the map based interface through utilization
of the WMS protocol by clicking on features.

4.

User can request packaged

data in various f
ormats produced from WFS.

5.

System prompts user for download options (e.g. pdf, excel, shapefile).

Alternate Flows:



Use Case ID:

3

Use Case Name:

Access Pre
-
Batched Reports

Use Case
Description:

Users access a web based library of existing management r
eports and data
analysis for various spatial, temporal and salmon metric domains. User can
select reports, run them against the data warehouse and view/download results.

Primary Actor:

Generic Data Browser

Basic Flows:

1.

User navigates to statewide public

ADF&G fishery data access page.

2.

User chooses to search through existing management reports.

3.

User drills down through a tree structure or searches for reports via
search terms.

4.

User selects which reports to run.

5.

System responds with summary or entire resul
t set depending upon size.

6.

System prompts user for download options (e.g. pdf, excel, shapefile).

7.

User selects options and then downloads report output if possible
.

Alternate Flows:



Use Case ID:

4

Use Case Name:

Access/Query Raw/Summarized Data

Use C
ase
Description:

User searches for and downloads raw datasets.

Primary Actor:

Generic Data Browser

Basic Flows:

1.

User navigates to statewide public ADF&G fishery data access page and
chooses to access raw data.

2.

User chooses to search through existing data
sets and metadata.

3.

User drills down through a tree structure or searches through metadata

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via search terms.