Download
logical data modelling n.
Skip this Video
Loading SlideShow in 5 Seconds..
Logical Data Modelling PowerPoint Presentation
Download Presentation
Logical Data Modelling

Logical Data Modelling

328 Views Download Presentation
Download Presentation

Logical Data Modelling

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Logical Data Modelling • The sole purpose of an Information System is to support or automate business activities by storing and processing relevant business information or data. • It is therefore critical to the success of any IS development that the meaning, structure and business rules of the required data are fully analysed, understood and modelled. • During the Investigation phase we are concerned with understanding the underlying (i.e. logical) data requirement rather than making decisions about its physical implementation.

  2. Current and Required Data • Most of the data required for the future system will be the same in content or meaning as that used currently. • The other form of data analysis concerns requirements for new business data. • The approach of starting with an analysis of existing data (or even re-using existing data models) will provide the most rigorous and efficient approach. • This approach will also help in driving out restrictions in data support arising from existing technical constraints.

  3. Physical vs Logical Data Structures • An organisation’s data will be physically stored in many different places, e.g. paper files, computer files. • This data will almost inevitably contain duplications and compromises due to the physical restrictions of storage, processing or practicality. • Example: • A physical purchase order form will hold information about products (product name, product number, product price), suppliers (supplier name, supplier address), the order’s heading (purchase order number, purchase order date) as well as the quantity of each product ordered (quantityordered). • While we may have a single physical grouping of data on one purchase order form, what we actually have is information about several different things - products, suppliers, and purchase orders. • In other words the underlying logical view is of a number of separate data groupings, each describing a different business concept or object. • We will also find that information on, for example, products is physically held in many other places, such as on customer orders, invoices and despatch notes. • This all leads to a confusing mess of duplication and interconnecting information, which in turn leads to problems in maintaining data consistency and integrity.

  4. The LDM • In SSADM the vehicle for analysing the logical structure of an organisation’s information is the Logical Data Model (LDM). • A Logical Data Model is a way of graphically representing what that information is really all about, how it relates to other information and business concepts, and how business rules are applied to its use in the system. • The LDM is possibly the most important and ultimately the most rigorous product of an entire SSADM project. • Logical Data Models consist of two parts: • a diagram called the Logical Data Structure (LDS); • a set of associated textual descriptions that explain each part of the diagram.

  5. Entities • Any object or concept about which a system needs to hold information is known as an Entity Type (or entity for short). • To be a valid entity we must wish to hold information on more than one occurrence of it. Entity occurrences are real world instances of an entity type. • For example the entity type Supplier will have occurrences such as:

  6. Entities (continued) Supplier unique name • The symbol for an entity in an LDS is a round cornered rectangle containing the entity’s name (which must be unique): • An entity must have a number of properties to qualify as such: - There must be more than one occurrence of the entity. - Each occurrence should be uniquely identifiable. - There must be data that we want to hold about the entity. - It should be of direct interest to the system.

  7. Attributes • Each item of information (or data) that we hold about an entity is known as an attribute or data item. • Examples of attributes for Supplier might be suppliernumber, suppliername, supplieraddress, and suppliertelephoneno. • The detail of an entity’s attributes is not formally included on the LDS itself. This is held in separate textual descriptions, which will be discussed later.

  8. Relationships supplier for Purchase Supplier ? ? Order placed with • Entities do not exist in isolation, but are related to other entities. • In physical data structures these relationships are signified by physical links such as pointers or placement in the same file or document. • In logical models relationships represent business associations or rules and not physical links. • Any entities that are related are linked by a line on the LDS. • The line is labelled with the name of the relationship, and is named in both directions.

  9. Degree • The number of occurrences of each entity type participating in a given relationship is denoted by the degree or cardinality of that relationship, and illustrated on the LDS by adding ‘crow’s feet’ to the relationship’s line. • There are three types of degree: • Many to Many (m:n). This tells us that each occurrence of A is related to one or more occurrences of B, and each occurrence of B is related to one or more occurrences of A. • One to Many (1:m). This tells us that each occurrence of A is related to one or more occurrences of B, but each occurrence of B is related to only one occurrence of A. • One to One (1:1). This tells us that each occurrence of A is related to only one occurrence of B, and each occurrence of B is related to only one occurrence of A.

  10. Optionality Supplier supplier for placed with Purchase Order • Each relationship is further annotated to show if it must exist for all occurrences of the participating entity types. • If there can be occurrences of one entity that are not related to at least one occurrence of the other, then the relationship is said to be optional for that entity. • The relationship line is then converted to a dashed line at its optional end (which could mean both ends if both entities are optional participants).

  11. Developing the LDS • To start with we are only interested in producing a high level model of the current system’s underlying data structure. • Due to its largely conceptual nature Logical Data Modelling can be one of the most intense activities of an SSADM project. • In many projects development of the LDM is started by holding brainstorming sessions with small groups of analysts and users. • With a little practice analysts often find that the best method of data modelling is to draw up possible LDSs almost instinctively • Relationships are added as each entity is identified and then checked with users on the spot. • This approach has a lot to recommend it, particularly at this level of detail or for small systems, as diagrams are produced and verified quickly.

  12. Identifying Entities • To identify entities in the current environment we can begin by looking at our physical data stores to find out exactly what it is that they hold information about. • If we take the customer order file and discuss it with users, we find that it not only contains details of each individual order, but of the customers themselves, • i.e. customer address, customer telephone number etc., and so encompasses at least two entities, namely Customer and Customer Order.

  13. Verification • Once the list has been drawn up we should verify it with key users during preliminary scoping interviews. • The key questions to ask of each entity are: • Are any of the candidates merely attributes of another entity? • Do any of the candidates represent a subset of occurrences of another entity? • Do all of the entities have a unique identifier? • During this process we may discover new entities, merge existing entities or discard candidates as being outside the area of investigation. Note: There will often be relationships between entities that exist in the real world, but which are not of relevance to the system under discussion. E.g a customer of ZigZag may well be employed by one of its suppliers. This is NOT something that ZigZag will be interested in recording!  

  14. Adding Relationships • We now examine each entity to see if it is directly related, in a way that is of interest to the system, to any of the other entities. • The best way to do this is in discussion with users, either taking each entity in turn, or starting with a key entity and moving around the LDS “network” as the relationships are identified. • Having identified where we think relationships exist, we now consider their degree, optionality and names. • We do this by identifying the business rules that apply to each entity pairing. • The basic process is the same for all pairings, so we will look at just one example.

  15. Stock - Delivery • We first consider the relationship from the Stock perspective: • Each Stock occurrence will consist of a quantity of a single product, all of which was delivered on the same delivery. • If within the depot we have a quantity of a given product, some of which was delivered in one delivery and some in another, then we will have more than one Stock. • This is an example of one of ZigZag’s business rules, and one that will continue in the new system. • Thus each Stock occurrence is related to just one Delivery. • Each delivery may contain a number of different products, each of which will be stored as a separate stock (remember that each Stock occurrence is a quantity of a single product). • Thus each Delivery is related to one or more Stock occurrences.

  16. Stock – Delivery (continued) • We now consider the optionality of the relationship: • Each Stock must have been delivered by a Delivery. • So the relationship at the Stock end is mandatory. • However a Delivery could be rejected for quality reasons by the depot, in which case the delivery would be recorded but would not be related to any subsequent Stock occurrences. • So the relationship is optional at the Delivery end. • Choosing a name is often the hardest part of the procedure. • It is important to name a relationship in both directions as it forces us to examine the true nature of the relationship, sometimes leading to the discovery of additional relationships or even entities. • We should always try to choose phrases that accurately reflect the users’ view of the relationship. In our example it is not too difficult to find reasonable names: delivery of and delivered by.

  17. Overview LDS • Continuing this process for all of the relationships identified on the matrix gives us a first-cut overview LDS for the current system:

  18. Drilling Down……. Depot Product Zone Type Supplier Product Delivery Invoice • The overview LDS provides us with a good basis for building a more complete model of current data. • We begin the process of creating a detailed model by looking at this model and discussing it with users to check our understanding of the scope of current data and to uncover lower level entities which can be added immediately.

  19. Masters and Details Master Supplier Product Detail Stock • Most relationships are 1:m. • The entity at the ‘1’ end is known as the master and the entity at the ‘m’ end as the detail. • The terms master and detail refer only to an entity’s role in a particular relationship. • It is quite possible for an entity to be the master in one relationship and the detail in another.

  20. Keys • We should be able to select at least one identifier for each entity type, • i.e. an attribute that enables each occurrence of an entity to be uniquely identified, • e.g. for Customer we could use customer number. • Any attribute or set of attributes which together uniquely identify an entity is known as a candidate key. • One of these candidates (there will often only be one) should be selected as the primary key. • Whenever we require direct access to an entity, the primary key is used to identify which occurrence we are interested in. • For example, if we needed to access the Supplier entity to find out a supplier’s address, we would use the primary key of supplier number to identify the correct occurrence.

  21. Foreign Keys • If we have a relationship between two entities we need to be able to associate the occurrences at one end with the related occurrences at the other. • In a relational model (such as the LDM) we do this by including the primary key of the master in the set of attributes of the detail. • The copy of the master’s primary key in the detail entity is known as a foreign key.

  22. Key Navigation Supplier supplier for placed with Purchase Order • Supplier attributes: • Supplier No. (Primary Key), e.g 271 • Supplier Address etc. • Purchase Order attributes: • P.O. Number (Primary Key), e.g 5001 • P.O. Date etc. • Supplier Number (Foreign Key), e.g 271 • To access all purchase orders placed with supplier number 271, we look for all occurrences of Purchase Order with a supplier number attribute value of 271. • Coming in the opposite direction, to access the supplier for purchase order 5001, we look for the single occurrence of the Supplier entity whose primary key is equal to the supplier number given in the foreign key of purchase order number 5001, i.e. supplier number 271.

  23. Types and Notation • Primary Keys belong to one of three types: • A Simple Key, consisting of a single attribute; • A Compound Key, consisting of two or more foreign keys; • A Hierarchic or Composite Key, consisting of one or more foreign keys and a qualifying non-foreign key attribute. • Notation • The primary key is underlined and the foreign key preceded by an asterisk to show the contents of each entity: Supplier (supplier number, supplier address, supplier tel. no.) Purchase Order (P.O. number, P.O. date, *supplier number)

  24. Resolving Many-to-Many Relationships • Many design techniques can only be carried out on hierarchical (i.e. master-detail) relationships which are hidden by m:n relationships. • m:n relationships make navigation around the model very difficult or even impossible (and, although we are not really concerned with technical issues at this point, they cannot be implemented). • m:n relationships very often hide information about the participating entities or the relationships themselves.

  25. Resolving Many-to-Many Relationships - example • Each Product may be ordered by one or more Purchase Orders. • Each Purchase Order must be an order for one or more Products. • So where do we place the quantity ordered?

  26. Resolving Many-to-Many Relationships - example Purchase Order Number: 0021113 Purchase Order Date: 4/3/01 Supplier: Delivery Address: 2327 Depot 1 Bella Sonic Harrow Way Lake Industrial Estate Harrow Unit 5 HA4 3NB NE3 7AJ Qty Your Our Description Format Unit Product Product Price Ref Ref 100 BJB001 884690 The Best of Johnnie Boy CD 6.99 500 3485VHS/3 993201 Unbranded Blank 3hr Video Tapes BV 0.53 • If we look at a sample purchase order of ZigZag, we will discover that details of quantities and products are held in individual purchase order lines.

  27. Resolving Many-to-Many Relationships - example • So in this case we can choose a natural link entity, which we will call Purchase Order Item. • Purchase Order Line sounds a bit too similar to the physical printed line on the order form. • The key for Purchase Order Item will be Purchase Order Number plus Product Number – a compound key.

  28. Relationships in M:N Resolutions • Whenever we introduce a link entity we need to ensure that the relationships we recorded previously with its master entities are still valid. • For example, in our overview LDS we recorded a many to many relationship between Despatch and Customer Order. • This may at a high level appear reasonable as it is common for some items in an order to go into one van load (Despatch) and some into another. • However, the contents of each item within the order is always despatched in its entirety in the same van load (i.e. if 3 copies of Puccini’s Tosca are ordered within a single customer order, they will all be delivered together). • Therefore, each Despatch is actually related to many Customer Order Items, rather than to whole Customer Orders.

  29. Link Entities • Depot Zone and Product Type provide another more complex illustration of many to many relationships: Each Depot Zone may store one or more Product Types. Each Product Type must be storable in one or more Depot Zones. • The attributes that make up Depot Zone are Depot Zone Number, Shelf Height, and Depot Zone Description etc. Depot Zone Number is a unique identifier that is assigned to each Depot Zone, and is the label attached to the end of each row of shelving in the zone. Depot Zone Description would include values such as CD and DVD, Videos and Books, and Tape etc, which describe the sorts of products that the shelving in each zone can accommodate. • The attributes of Product Type include Product type code and Product type name, where the Product type code is an abbreviation of the Product type name, e.g. ‘BV’ for ‘Blank Video’, ‘DVD’ for ‘DVD’ etc. • So, for example, we might have the following cases: Depot Zones 101 and 105 store ‘DVD’ and ‘CD’ product types; Depot Zone 102 stores ‘VHS’, ‘BV’ and ‘SPB’ (small paperback book) product types.

  30. Link Entities Product Depot Zone Type allocated by stored in allocation for allocates storage for Depot Zone Allocation • To make these associations we would have to set up lists of foreign keys in both entities, of arbitrary length. • Significant maintenance overhead • Navigation around the model very difficult • Against the rules of relational data modelling • Solution: A link entity • Each occurrence will store a valid association or pairing of a Depot Zone occurrence with a Product Type occurrence, such as: Depot Zone Product Type 101 DVD 101 CD 105 DVD 105 CD 102 VHS 102 BV 102 SPB

  31. Pigs Ear

  32. Resolving One-to-One Relationships • The problems associated with 1:1 relationships are less clear-cut than with m:n relationships: • 1:1 relationships often obscure an underlying single entity. • There may be a missing link entity. • Later design techniques may require all relationships to be master-detail. • In the ZigZag overview LDS there are two 1:1 relationships - between Delivery and Purchase Order and between Supplier Invoice and Delivery . • Deliveries are identified by the purchase order they are satisfying • The only information currently held about them details which parts of the purchase order they have successfully delivered. • It is quite easy in this case to view Delivery as a logical extension (or conclusion) of a Purchase Order, so we will merge the two entities and transfer all of Delivery’s relationships to Purchase Order. • To do this successfully, Purchase Order will contain attributes delivery date and supplier’s delivery reference while Purchase Order Item will contain quantity delivered.

  33. Resolving One-to-One Relationships (continued) Supplier Purchase Invoice Order Purchase Invoice Order Item Item Supplier Purchase Invoice Order Purchase Order Item

  34. Resolving One-to-One Relationships (continued) Purchase Order Supplier Purchase Invoice Order Supplier Invoice Purchase Purchase Order Order Item Item

  35. Removing Redundant Relationships Customer Is this relationship Redundant? Customer Order Customer Order Item • One of our aims when drawing up an LDS should be to include only the minimum number of relationships needed to apply all of the business rules relating to data. • Any unnecessary relationships are termed ‘redundant’, and will involve us in a maintenance overhead if implemented. • The major difference between relationships and a route map is that each relationship carries with it a meaning, and so different ‘routes’ between entities will often have different meanings, or enforce different rules.

  36. Removing Redundant Relationships Purchase Order Is this relationship Redundant? Supplier Invoice Purchase Order Item • Each Purchase Order may be related to a number of Supplier Invoice, each of which is related to a PO Item. • Each PO Item may relate to just one Supplier Invoice, which relates to just one Purchase Order. • HOWEVER – Each Purchase Order MUST contain at least one PO Item. • If the Invoice is not present then removing the direct relationship would mean that a relationship could not be established between Purchase Order and PO Item.

  37. Product Type Depot Zone Supplier Depot Zone Allocation Product Product Substitute Purchase Order Customer Despatch Supplier Invoice Customer Order Purchase Order Item Customer Order Item Stock

  38. Selected ZigZag Entities and Attributes

  39. Completing the Documentation

  40. Small Projects Entity Description Table and Data Catalogue Table for Small Projects

  41. Validating the LDM • we need to check that the LDM can provide access to all of the data items required by each update or enquiry process. • Most processes will need to access a number of data items, which will be specified by some selection criteria. • These items will often be represented by the attributes of more than one entity. • navigate around the relationships of the LDS, applying the selection criteria to filter out the entity occurrences we need to provide all of the necessary data. These navigations are called ‘Access Paths’.

  42. Validating the LDM • For example, when allocating a zone in which to store the stock of a particular product received in a delivery (a process called ‘Allocate Stock Zone’), we will need to find out which depot zones have been designated for the storage of that type of product. • The entry point to the LDS is via the product number in the entity Product. • We can then access its product type, and then the possible zones in which this product can be stored by reading through all the occurrences of Depot Zone Allocation for that Product Type.