This document provides an overview of information modeling steps, including:
1. Introduction to key concepts in information modeling such as objects, relations, and different modeling perspectives.
2. Additional steps in information modeling like defining the scope or AS-IS/TO-BE, object modeling, table modeling, and documentation.
3. The next steps after information modeling are aligning the model with process and system models by mapping processes and systems to object groups.
Data Warehousing, Data Mining, Data Marts, Data Cube, OLAP Operations, Introduction to Common Messaging System, Web Tier Deployment, Application Servers & Clustered Deployment, IBM Notes and IBM Domino
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
Practical Conceptual Modeling at UX Detroit Feb 2015Andrew Hinton
See the slides with all CORRECT notes here: http://understandinggroup.com/2015/02/practical-conceptual-modeling/
A presentation by Kaarin Hoff, Andrew Hinton, and Joe Elmendorf (not present at the event), for UX Detroit's Feb 2015 meetup. An introduction to some of the content that will be in the IA Summit 2015 workshop
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Data Warehousing, Data Mining, Data Marts, Data Cube, OLAP Operations, Introduction to Common Messaging System, Web Tier Deployment, Application Servers & Clustered Deployment, IBM Notes and IBM Domino
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
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See the slides with all CORRECT notes here: http://understandinggroup.com/2015/02/practical-conceptual-modeling/
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2. Modeling Steps
• Introduction to Information Modeling
• Delimitations
• AS-IS or TO-BE
• Object Modeling
• Table Modeling
• Documentation
• Next step
2
3. Introduction to Information Modeling (1)
• Different modeling perspectives
– Organization Model
– Process Model
– Information Model
• One distinguishing factor separates them – the stability of the model:
–
3
4. Introduction to Information Modeling (2)
• Objects
– The building blocks of a business
• Something concrete or abstract that is essential for the business
• Identified by a unique and stable key
• Have one or more properties
– There are 2 types of Objects
• Resource objects, e.g. CUSTOMER
– Exists on its own and is not dependent of any other information
• Event objects, e.g. ORDER
– Normally dependent on one or more resource objects and don’t exist on its own
4
CUSTOMER
11
ORDER
12
5. Introduction to Information Modeling (3)
• Relations
– Displays connection and illustrates a possible state
• Connects Object Object or Object Relation
• Can not exist by them self, but is dependent on Objects/Relations to link together
• Borrow keys from the Objects
– There are 3 types of relations
• 1:1 - 1 to 1 relation, e.g. CUSTOMER and CUSTOMER INFO
– The Object can only be connected to one instance of another Object (or it self)
• 1:M - 1 to Many relation, e.g. CUSTOMER and ORDER
– The Object on the 1-side can be connected to one ore more instances of the Object on the M-side
• M:M - Many to Many relation, e.g. ORDER and PRODUCT
– A relation which is connected to instances in both the related Objects
– Could often be replaced by an Object
5
CUSTOMER
11
ORDER
12
placed by
6. Introduction to Information Modeling (4)
• Example
– The objects CUSTOMER, ORDER, PRODUCT, CUSTOMER INFO
6
CUSTOMER
11
ORDER
12
placed by PRODUCT
13
CONTAINS
CUSTOMER
INFO 14
describes
7. Introduction to Information Modeling (5)
• Example
– The CUSTOMER table
7
CustomerId Name Address … …
10001 SEB AB … … …
10002 Swedbank AB … … …
10003 Handelsbanken AB … … …
…
…
8. Delimitations
• Important to define what should be modeled and what should not be modeled
• Delimitations could e.g. be made based on:
– Process
– Geography
– Functionality
– Products
– Topics
• When a first Information Model exists, it can be extended to include previous
delimited parts step by step
8
9. AS-IS or TO-BE
• Should the Information Model be for the current business (AS-IS) or the future business
(TO-BE)
– The Information Model should be neutral against the corresponding Process Model and
System Model
– In a later step the Information Model should be aligned with the corresponding Process
Model and System Model
9
10. Object Modeling (1)
• Individual brainstorming about concepts/terms in the business
• Categorize the concepts/terms:
– Object (O) - Customer
– Relation (R) - Customer Info describes Customer
– Property (PY) - Customer Name
– Example (EX) - Ericsson
– Process (PS) - Monthly Closing
• In order not to get to many Objects, normally Object Groups are modelled which in turn
can contain more fine grained Objects
– Object Group - Customer
• Object - Private Customer
• Object - Corporate Customer
10
11. Object Modeling (2)
• The Object Groups are categorized in 5 groups
– Stakeholder (Intressent)
– Event (Händelse)
– Offering (Erbjudande)
– Context (Omgivning)
– Infrastructure (Infrastruktur)
• The Information Model is then populated according to the following outline:
11
Context Offering
Stakeholder
Infrastructure
Event
12. Table Modeling
• For each Object Group / Object in the Object Modeling
– Describe the Object Group / Object
– Identify keys
• Primary keys
• Foreign keys
• Fictitious keys (e.g. order number)
– Identify properties
– Exemplify in a table with realistic data
• Normalize the tables => Separate out information in their own tables
– Investigate if there is repeated information in any column
– When more than one key, investigate if any of the properties don’t depend on all of the keys
– Investigate if there are properties that depends on another property
12
13. Documentation
• List of concepts/terms
• List of Object Groups / Objects
• Information Model
• Table Model
13
14. Next step
• Where is the information used?
– Alignment with Process Model
– Mapping of Process Object Group
• Where do the information reside / is the information stored?
– Alignment with System Model
– Mapping of System Object Group
14