The Entity-Relationship Model

IS698

Min Song

Overview of Database Design

Conceptual design: (ER Model is used at this stage.) What are the entities and relationships in the

enterprise? What information about these entities and

relationships should we store in the database? What are the integrity constraints or business

rules that hold? A database `schema’ in the ER Model can be

represented pictorially (ER diagrams). Can map an ER diagram into a relational schema.

ER Model Basics

Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes.

Entity Set: A collection of similar entities. E.g., all employees. All entities in an entity set have the same set of

attributes. (Until we consider ISA hierarchies, anyway!)

Each entity set has a key. Each attribute has a domain.

Employees

ssnname

lot

ER Model Basics (Contd.)

Relationship: Association among two or more entities. E.g., Attishoo works in Pharmacy department.

Relationship Set: Collection of similar relationships. An n-ary relationship set R relates n entity sets E1 ...

En; each relationship in R involves entities e1, ..., en. Same entity set could participate in different

relationship sets, or in different “roles” in same set.

lot

dname

budgetdid

sincename

Works_In DepartmentsEmployees

ssn

Reports_To

lot

name

Employees

subor-dinate

super-visor

ssn

Cartesian or Cross-Products A tuple <a1,a2,…,an> is just a list with n elements in

order. A binary tupe <a,b> is called an ordered pair. Given two sets A,B, we can form a new set A x B

containing all ordered pairs <a,b> such that a is a member of A, b is a member of B.

In set notation: A x B = {<a,b> | a in A, b in B}. Example: {1,2,3} x {x,y} =

{<1,x>,<1,y>,<2,x>,<2,y>,<3,x>,<3,y>}

A Formal Treatment of Relation: The Cross Product

Let E1, E2, E3 be three entity sets. A relationship among E1, E2, E3 is a

tuple in E1 x E2 x E2. A relationship set, or relation, is a set

of relationships. So if R is a relation among E1, E2, E3, then R is a subset of E1 x E2 x E3.

Key Constraints

Consider Works_In: An employee can work in many departments; a dept can have many employees.

In contrast, each dept has at most one manager, according to the key constraint on Manages.

Many-to-Many1-to-1 1-to Many Many-to-1

dname

budgetdid

since

lot

name

ssn

ManagesEmployees Departments

Participation Constraints Does every department have a manager?

If so, this is a participation constraint: the participation of Departments in Manages is said to be total (vs. partial). Every did value in Departments table must appear in

a tuple of the Manages relation.

lot

name dnamebudgetdid

sincename dname

budgetdid

since

Manages

since

DepartmentsEmployees

ssn

Works_In

Weak Entities

A weak entity can be identified uniquely only by considering the primary key of another (owner) entity. Owner entity set and weak entity set must participate in a

one-to-many relationship set (one owner, many weak entities).

Weak entity set must have total participation in this identifying relationship set.

lot

name

agepname

DependentsEmployees

ssn

Policy

cost

ISA (`is a’) Hierarchies

Contract_Emps

namessn

Employees

lot

hourly_wagesISA

Hourly_Emps

contractid

hours_workedAs in C++, or other PLs, attributes are inherited.If we declare A ISA B, every A entity is also considered to be a B entity. Overlap constraints: Can Joe be an Hourly_Emps as well

as a Contract_Emps entity? (Allowed/disallowed) Covering constraints: Does every Employees entity also

have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)

Reasons for using ISA: To add descriptive attributes specific to a subclass. To identify entities that participate in a relationship.

Aggregation

Used when we have to model a relationship involving (entity sets and) a relationship set. Aggregation

allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships.

Aggregation vs. ternary relationship: Monitors is a distinct relationship, with a descriptive attribute. (i.e., until) Also, can say that each sponsorship is monitored by at most one employee.

budgetdidpid

started_on

pbudgetdname

until

DepartmentsProjects Sponsors

Employees

Monitors

lotname

ssn

since

Conceptual Design Using the ER Model

Design choices: Should a concept be modeled as an entity or

an attribute? Should a concept be modeled as an entity or

a relationship? Identifying relationships: Binary or ternary?

Aggregation?

Entity vs. Attribute

Should address be an attribute of Employees or an entity (connected to Employees by a relationship)?

Depends upon the use we want to make of address information, and the semantics of the data:

If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued).

If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modeled as an entity (since attribute values are atomic).

Entity vs. Attribute (Contd.)

Works_In4 does not allow an employee to work in a department for two or more periods.

Similar to the problem of wanting to record several addresses for an employee: We want to record several values of the descriptive attributes for each instance of this relationship. Accomplished by introducing new entity set, Duration.

name

Employees

ssn lot

Works_In4

from todname

budgetdid

Departments

dnamebudgetdid

name

Departments

ssn lot

Employees Works_In4

Durationfrom to

Entity vs. Relationship First ER diagram OK if a

manager gets a separate discretionary budget for each dept.

What if a manager gets a discretionary budget that covers all managed depts? Redundancy:

dbudget stored for each dept managed by manager.

Misleading: Suggests dbudget associated with department-mgr combination.

Manages2

name dnamebudgetdid

Employees Departments

ssn lot

dbudgetsince

dnamebudgetdid

DepartmentsManages2

Employees

namessn lot

since

Managers dbudget

ISA

This fixes theproblem!

Binary vs. Ternary Relationships

If each policy is owned by just 1 employee, and each dependent is tied to the covering policy, first diagram is inaccurate.

What are the additional constraints in the 2nd diagram?

agepname

DependentsCovers

name

Employees

ssn lot

Policies

policyid cost

Beneficiary

agepname

Dependents

policyid cost

Policies

Purchaser

name

Employees

ssn lot

Bad design

Better design

Binary vs. Ternary Relationships (Contd.)

Previous example illustrated a case when two binary relationships were better than one ternary relationship.

An example in the other direction: a ternary relation Contracts relates entity sets Parts, Departments and Suppliers, and has descriptive attribute qty. No combination of binary relationships is an adequate substitute: S “can-supply” P, D “needs” P, and D “deals-

with” S does not imply that D has agreed to buy P from S.

How do we record qty?

Summary of Conceptual Design Conceptual design follows requirements analysis,

Yields a high-level description of data to be stored ER model popular for conceptual design

Constructs are expressive, close to the way people think about their applications.

Basic constructs: entities, relationships, and attributes (of entities and relationships).

Some additional constructs: weak entities, ISA hierarchies, and aggregation.

Note: There are many variations on ER model.

Summary of ER (Contd.)

Several kinds of integrity constraints can be expressed in the ER model: key constraints, participation constraints, and overlap/covering constraints for ISA hierarchies. Some constraints (notably, functional

dependencies) cannot be expressed in the ER model. (e.g., z = x + y)

Constraints play an important role in determining the best database design for an enterprise.

Summary of ER (Contd.)

ER design is subjective. There are often many ways to model a given scenario! Analyzing alternatives can be tricky, especially for a large enterprise. Common choices include: Entity vs. attribute, entity vs. relationship,

binary or n-ary relationship, whether or not to use ISA hierarchies, and whether or not to use aggregation.

Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful.

Chapter 3Data Storage and Access Methods

Title: Operating System Support for Database ManagementAuthor: Michael StonebrakerPages: 217—223

Problem Definition Apparent disconnect between DBMS performance

goals and operating system design and implementation.

Services provided by OS are inadequate and sub-optimal.

Paper evaluates the following services: Buffer pool management File system Interprocess communication Consistency control Paged virtual memory

Contributions

Demonstrates OS services are too slow or inappropriate for DBMS tasks.

Attempts to make OS designers aware of and more sensitive to DBMS needs.

Key Concepts Buffer Pool Management

OS has a fixed buffer pool that handles all I/O UNIX uses LRU replacement strategy, which may

not be ideal for a DBMS Large performance overhead to pull a block into

the buffer. Approx. 5000 instructions for 512 bytes

No good prefetch strategy. UNIX does not implement a selected force out

buffer manager where the DBMS can dictate the order of the commits

Key Concepts The File System

UNIX implements its file system as character arrays and forces the DBMS to implement its own higher level objects.

Tree Structured File Systems UNIX implements 2 service using trees

Keeping track of blocks in a given file Hierarchical directory structure

DBMS adds a third tree to support keyed access One tree with all 3 kinds of information is more

efficient.

Key Concepts Scheduling Process Management and

Interprocess Communication Performance

Task switches are inevitable Processes have a great deal of state information

making task switches expensive Critical Sections

Buffer pool is a shared data segment. Problems arise if OS deschedules a DB process

holding a lock on the buffer pool. Server model

OS needs to provide a message facility for multiple processes to message a single process.

Server must do its own scheduling and multitasking.

Key Concepts Consistency Control

Many Operating Systems can only place locks at the file level.

DBMS prefer finer granularity. When DBMS implement its own buffer pool,

crash recovery by the operating system would be impossible.

Paged Virtual Memory Large files may not be able to be stored in

memory Binding chunks of the file into user space may

incur a performance loss.

Validation Content is mostly informational.

Based off previous papers and existing implementations of current systems.

Examples are cited primarily from the UNIX OS and the Ingres DBMS.

Issues could be biased and may not be common or applicable to all OS and DBMS combinations.

Assumptions Presents the topic as one that is applicable

to across a number of DBMS and OS

Author constrains his examples to UNIX and Ingres.

Paper was written in 1981. Operating Systems have advanced considerably since then. His points may no longer be applicable.

Changes if Rewritten Today Increase the diversity of operating systems

and DBMS

Add industry perspective. Are the problems Stonebraker presents really a problem for DBMS designers?

Quantify claims by providing statistical analysis of performance hits.

Chapter 3: Data Storage and Access Methods

Title: The R* Tree: An Efficient and Robust Access Method for Points and Rectangles

Authors: N. Beckmann, H. Kriegel, R. Schneider and B. Seeger

Pages: 207-216

The R* Tree: An Efficient and Robust Access Method for Points and Rectangles

Problem Problem Statement Why is this problem important? Why is this problem hard?

Approaches Approach description, key concepts Contributions (novelty, improved) Assumptions

Problem Statement – R* Tree Given

Data containing points and rectangles Spatial queries (point, range query, insert, delete)

Find - An Access Method (Data Structure) A hierarchical organization of rectangles Example from wikipedia

Objectives Efficiency of spatial queries

Constraints Balanced tree Each node is a disk page and has >= m (min # of entries)

entries. Root has at least two children unless it is a leaf Efficiency metric = number of disk-pages accessed

Why is this problem important?

Multi-dimensional Applications Large geographic data. e.g., Map objects like

countries occupy regions of non-zero size in two dimension.

Common real world usage: “Find all museums within 2 miles of my current location".

CAD …

Many DBMS servers support spatial indices Orcale, IBM DB2, …

Why is this problem Hard? B-tree split methods ineffective in 2-dimensions

Ex. Sorting

Size variation across data Rectangles Large rectangles limit split options!

Non-uniform data distribution over space

Dynamic Access Method Insertions and deletions Overlapping directory rectangles => multiple search paths

Novelty of Contribution Related Work

Traditional one-dimensional indexing structures (e.g., hash, B-tree) are not appropriate for range search

B+ tree Represents sorted data in a way that allows for

efficient insertion and removal of elements. Dynamic, multilevel index with maximum and

minimum bounds on the number of keys in each node.

Leaf nodes are linked together as a linked list to make range queries easy.

Novelty of Contribution Related Work

R-tree R-tree is a foundation for spatial access method A complex spatial object is represented by

minimum bounding rectangles while preserving essential geometric properties

Over-lapping regions Heuristic: minimize the area of each enclosing

rectangle in the inner nodes.

Principles of R-tree

Reference: A Guttman ‘R-tree a dynamic index structure for spatial searching’, 1984

Height-balanced tree similar to a B-tree with index records in its leaf nodes containing pointers to data objects.

Heuristic Optimization: minimize the area of each enclosing rectangle in the inner nodes.

Performance Parameters beyond R-tree (Q1) The area covered by a directory rectangle should be minimized.

(Q2) The overlap between directory rectangles should be minimized.

(Q3) The margin of a directory rectangle should be minimized.

(Q4) Storage utilization should be optimized.

Intuitions: Reduce overlap between sibling nodes. Reduce traversal of multiple branches for point query Reinsert old data changes entries between neighboring nodes and thus

decreases overlap. Due to more restructuring, less splits occur

Difference between R-tree and R*-tree

Minimization of area, margin, and overlap is crucial to the performance of R-tree / R*-tree.

The R*-tree attempts to reduce the tree, using a combination of a revised node split algorithm and the concept of forced reinsertion at node overflow. This is based on the observation that R-tree structures are highly susceptible to the order in which their entries are inserted, so an insertion-built (rather than bulk-loaded) structure is likely to be sub-optimal. Deletion and reinsertion of entries allows them to "find" a place in the tree that may be more appropriate than their original location. Improve retrieval performance

Example

R1

R2

R3

R5

R4

R1

R2

R3

R5

R4

R1

R2

R3

R5

R4

Preferred by R-tree

Preferred by R*-tree

Validation Methodology

Methodology Experiments with simulated workloads Evaluation of design decisions

Results R*-tree outperforms variants of R-tree

and 2-level grid file. R*-tree is robust against non-uniform

data distributions.

Summary Paper’s focus

R*-tree – implementations and performance

Ideas Heuristic Optimizations (pp. 208)

Reduction of area, margin, and overlap of the directory rectangles

Better Storage Utilization (pp 211) Forced Reinsertion (splits can be prevented)

Experimental comparison Using many data distributions

Assumptions, Rewrite today Assumptions

Indexing data in two-dimensional space Bulk load and bulk reorganization not available Concurrency control and recovery costs are negligible

Reinserts during split!

Rewrite today Bulk-load of rectangles Compare with newer methods

R+ tree (disjoint sibling), Hilbert-R-tree Analytical results

Formally compare R*-tree with alternatives