Folie 1

1
Welcome to the 1st GLOWA-Volta Database Workshop
2
Agenda

• Aims of the workshop
• Deficits relating the datastocks and data
  management of the GVP
• Datamanagement
• Livecycle of data
• Conclusions for the GVP
• Need for integration of the data users to
  database developement
• Role of disciplines to data management
• Steps forward to an optimized data management

3
Aims of the workshop

• Initiation of a dialogue with the GVP-members
  about their requirements to an efficient data
  management
• this dialogue is a process in which the
  following items should be discussed
• data
• use and access
• database structure
• metadatabase
• webpresence
• database team and division of work

4
Aims of the workshop

• These points should be discussed within the
  working groups as far as possible. In this
  workshop we are focussing the items
• data (data flow)
• data use and access
• - set up of a database team and division of work

Technical implementation, structure and type of
the databases, including ways of access should be
developed in a team by members of the departments
as well as computer scientists and project
leaders!
5
Deficits relating the datastocks and data
management of the GVP currently
6
The current situation

• Data server
• data stock is not completed
• data searching by criterias
• is not possible
• arrangement of data is unclear
• relation to the project is
• unclear
• there are no rules for data
• uploading (location, topic etc.)

7
The current situation

• Data mediums
• what is its content?
• to which project/thesis does it belong to?

?
8
The current situation

• Metadatabase
• data stock representation
• is not completed

9
The current situation

• Metadatabase
• if you are looking for data,
• you have to ask your
• colleague in and outside of ZEF!
• maybe the contact person is not available

10
The current situation

• Metadatabase
• blind links

11
The current situation

• Datasets
• inconsistency

12
The current situation

• Datasets
• lack of data description
• which method background?
• are the values correct?

?
13
Data management

• For the avoidance of such problems there is the
  necessity of datamanagement

• Definition (by the Data Management
  Association)
• Data Resource Management is the development and
  execution of architectures, policies, practices
  and procedures that properly manage the full
  data lifecycle needs of an enterprise

• Normally the processes of data management
  should be implemented within a project, when it
  starts!

14
Structuring (Data modeling) - Categorisation -
Sortation - Description and Storing
Lifecycle of Data and Aspects of his Management
15
Lifecycle of Data Procurement of Data

• can happen from
• own investigations
• other institutions
• other (sub-)projects within the main project

• serves
• for providing the operating processes with input
  data

• needs
• certain data sources and formats
• quality
• application interfaces (import)

16
Lifecycle of Data Structuring and Storing

• means
• sorting of data related to a classification
  schema
• by themes
• by projects/subprojects
• by formats
• by applications
• by spatial research area
• .....

17
Lifecycle of Data Structuring and Storing

• or/ and
• by a conceptual data model
• it obtains the data entities and their
  relationships within a scope of a system
• the entities have properties (attributes)
• it is independend of the storing in a database
  and other technical requierements
• it can be designed in different forms
  (relational, network, hierarchical)
• the target system for data storing can be a
  relational database as well as a file system

18
Lifecycle of Data Structuring and Storing

• serves for
• easy search, find and use of data

• needs
• consensus among data producers and users
  within an organization about
• conceptual data model
• data needed and not needed
• rules about data updating and archival storage
• standards for metadata-content
• control of compliance to structure criterias

19
Lifecycle of Data Structuring and Storing

• means
• the physical storing of data

• needs
• storage places for the databases
  (central/distributed)
• physical data model
• derived from the conceptual/logical data model
• takes into account the facilities and
  constraints of a given
• database management system
• database management system with
• interfaces for applications
• query and search services
• backup and security functions

20
Lifecycle of data Administration

• means
• on technical base
• install and maintenance of database system
  (database database management system)
• user access constraints (rights)
• back up and archiving tasks
• security
• performance

• on content base
• Integrity - verifying or helping to verify
• control of data deliver
• control of data input
• metadata

21
Lifecycle of data Administration

• needs
• cooperation between data producers/users and
  administrators for
• maintenance and upgrading the database(-schema)
• definition of the authorization concept for
  database access
• (read only, read/write only, database schema
  modification etc.)

22
Lifecycle of data Use and Processing

• means
• use of data for analysis
• processing of data inside and outside of models
• production of new or modified (output-)data
• control of data accuracy
• preparation of data for other processes/projects

23
Lifecycle of data Distribution

• means
• delivery of data
• inside an organization/project
• by storing in a database (access by transfer
  counterpart)
• transfer by a portable media
• by publishing the metadata

• outside an institution/project
• by direct access to a database
• Web-Services
• publishing the metadata
• data extract service from a database
• data downloads
• Map Services (geodata)

24
Lifecycle of data Distribution

• serves
• inside an organization/project
• for providing work processes with adjusted data

• outside an organisation/project
• for providing work processes with adjusted data
• for providing data for public information about
  the projects

• needs
• knowledge about the requierements of demand
  concerning
• further use of data
• formats
• clients
• ...

25
Lifecycle of data Disposal

• means
• updating the data
• selection and deleting or archiving of data
• being out of date
• being in disuse

• serves
• against data overflow into the databases
• for maintenance the quality of data

• needs
• cooperation between the data producers/users and
  the
• database administrator

26
Conclusions for the GVP

• Conditions
• GVP is divided in a range of projects and
  subprojects
• e.g. in Phase II Land Use with subprojects L1,
  L2 etc.
• e.g. in Phase III Analysis of Long-Term
  Environemental
• Change with the subprojects E1, E2 etc.
• with their own processings, models, input and
  output data
• (- formats) data flows and -storages
• with specific integrations and dependencies
  among each
• other and within use case frameworks

• Projects and their models are provided also
  with data from
• different scientific disciplines like
  Hydrology, Pedology, Social
• Economy, Ecology etc.

27
Conclusions for the GVP

• Conditions

• in Phase III main objective ist the
  Integration of Phase I
• and II research results, knowledge, data and
  tools

• in Phase III the DSS will be realized as the
  GVPs primary output

The several subprojects are connected by data
flow (transfer)
The data flow should be adjusted to the GVP and
DSS requierements. This means there must be a
transparent management, which is centralized and
standardized
GVP Phase III Proposal, S. 8
28
Need for integration of the data users during
development and setup of a GVP-data management

• Each researcher (or on a higher level project)
  is a kind of data manager in his own work space.
  He has
• is own (local) database
• his own input and output data and data
  procurement requierements
• his own usage and processings
• his own distributing of data (to other
  users/projects)
• and therefore his own (short) lifecycle of data
  
• and is integrated in the data flow between the
  projects and also their life
• cycle of data

29
Project 3
Project 2
Project 4
Project 1
Project 1
Project 3
Project 4
30
Role of disciplines in developing concepts of a
data management
project members....

• have to decide, together with other project
  members and the
• database developers, which data should be
  stored centrally to share them, and which can be
  stored locally or at other places
• have to decide which structure of data storing
  is most convenient for an optimized using
• have to give information about their data
  (create metadata)

• and
• they are responsible for the data management in
  their own work area - before they will be
  interdisciplinary coordinated by the database
  administrator

31
Role of disciplines in developing concepts of a
data management
developers of a database ....

• have the responsibility to consult the project
  members about the requirements of data
  management
• have to organize the data flow concerning the
  (technical) way of data storing and access. The
  activities must be adjusted to the operating
  processes/projects and their interfaces
• have to develop the data management standards
  together with the project- members

32
Steps forward to an optimized data management
(within this workshop)
My request to you
Step 1 analyze the data stock (data
dictionary) Step 2 analyze the data flows Step
3 develope the logical data model for data
storing
33
Basic for working groups
Data flow modell combined with data dictionary
Notation

Terminator data producers (data source) or users
(data hollow) outside the system (external
Partners, public)

Process transfer of input data into output data
e.g. by algorithms

Data storage unit as data pool (not local).
Building time differs from using time. A ?
dictionary
A

a
Data flow direction for dataset a ? dictionary
a

Data flow relay in two directions (processes)
34
Basic for working groups
Context-Diagram
GLOWA-Volta
35
Basic for working groups
Diagram 1 GLOWA-Volta
DSS
36
Basic for working groups
Diagram 2 Analysis of Long-Term Environmental
Change
Vendor of remote sensing data
GVP LUDAS (E3)
S 1
37
Basic for working groups
Diagram 3 GVP-LUDAS
working group natural scientists
working group social economists
a, b, c
Elicitation Ghana
A
g
e
f
Evaluation of Elicitation Results (House- hold
Survey)
d
E 4
38
To Do

• Please try to draw a general overview about
  data flows and stocks

• And relate data management options to the
  certain data flows or storages

In the afternoon I would like to discuss the
requirements of a data management system from
your point of view.
Take it all as a form of brainstorming!!
Thank you!!
39
(No Transcript)
40
How to organize (sort) the data into the database
???
Central Database

• Project 1
• theme 1
• format 1
• format 2
• theme 2
• ....

• Formats
• SPSS
• project 1
• project 2
• remote sensing
• ....

• Region 1
• Project
• subproject
• theme
• format

• Project 1
• theme 1
• format 1
• format 2
• theme 2
• ....

41
Basic for working groups
Data flow modell combined with data dictionary
Notation II
a

Dataflow relay in two directions (processes)
b

a
Dataflow division from dataset a into datasets
b and c
c
b

Dataflow a is originated from b and c
a
c
a

Dataflow updating of data to a storage