1. The Entity-Relationship Model IS698 Min Song

2. 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.

3. 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 ssn name lot

4. 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 budget did since name Works_In DepartmentsEmployees ssn Reports_To lot name Employees subor- dinate super- visor ssn

5. Cartesian or Cross-Products  A tuple is just a list with n elements in order.  A binary tupe is called an ordered pair.  Given two sets A,B, we can form a new set A x B containing all ordered pairs such that a is a member of A, b is a member of B.  In set notation: A x B = { | a in A, b in B}.  Example: {1,2,3} x {x,y} = {,,,,, }

6. 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.

7. 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-Many 1-to-11-to ManyMany-to-1 dname budgetdid since lot name ssn Manages Employees Departments

8. 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 dname budgetdid since name dname budgetdid since Manages since Departments Employees ssn Works_In

9. 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 age pname Dependents Employees ssn Policy cost

10. ISA (`is a’) Hierarchies Contract_Emps name ssn Employees lot hourly_wages ISA Hourly_Emps contractid hours_worked v As in C++, or other PLs, attributes are inherited. v 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.

11. 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 : v Monitors is a distinct relationship, with a descriptive attribute. (i.e., until) v Also, can say that each sponsorship is monitored by at most one employee. budget did pid started_on pbudget dname until Departments Projects Sponsors Employees Monitors lot name ssn since

12. 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?

13. 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).

14. 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 to dname budget did Departments dname budget did name Departments ssn lot Employees Works_In4 Duration from to

15. 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 dname budget did Employees Departments ssn lot dbudget since dname budget did Departments Manages2 Employees name ssn lot since Managersdbudget ISA This fixes the problem!

16. 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? age pname Dependents Covers name Employees ssn lot Policies policyid cost Beneficiary age pname Dependents policyid cost Policies Purchaser name Employees ssn lot Bad design Better design

17. 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?

18. 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.

19. 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.

20. 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.

21. Chapter 3 Data Storage and Access Methods Title: Operating System Support for Database Management Author: Michael Stonebraker Pages: 217—223

22. 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

23. 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.

24. 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

25. 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.

26. 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.

27. 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.

28. 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.

29. 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.

30. 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.

31. 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

32. 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

33. 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

34. 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, …

35. 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

36. 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.

37. 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.

38. 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.

39. 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

40. 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

41. Example R1 R2 R3 R5 R4 R1 R2 R3 R5 R4 R1 R2 R3 R5 R4 Preferred by R-tree Preferred by R*-tree

42. 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.

43. 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

44. 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