Database management system
- 2. The process of designing the general structure of the database: • Logical Design – Deciding on the database schema. Database design requires that we find a “good” collection of relation schemas. – Business decision – What attributes should we record in the database? – Computer Science decision – What relation schemas should we have and how should the attributes be distributed among the various relation schemas? Physical Design – Deciding on the physical layout of the database
- 3. • Models an enterprise as a collection of entities and relationships – Entity: a “thing” or “object” in the enterprise that is distinguishable from other objects • Described by a set of attributes – Relationship: an association among several entities • Represented diagrammatically by an entity-relationship diagram:
- 4. • Extend the relational data model by including object orientation and constructs to deal with added data types. • Allow attributes of tuples to have complex types, including non-atomic values such as nested relations. • Preserve relational foundations, in particular the declarative access to data, while extending modeling power. • Provide upward compatibility with existing relational languages.
- 5. • Defined by the WWW Consortium (W3C) • Originally intended as a document markup language not a database language • The ability to specify new tags, and to create nested tag structures made XML a great way to exchange data, not just documents • XML has become the basis for all new generation data interchange formats. • A wide variety of tools is available for parsing, browsing and querying XML documents/data
- 6. • Storage manager is a program module that provides the interface between the low-level data stored in the database and the application programs and queries submitted to the system. • The storage manager is responsible to the following tasks: – Interaction with the file manager – Efficient storing, retrieving and updating of data • Issues: – Storage access – File organization – Indexing and hashing
- 7. 1. Parsing and translation 2. Optimization 3. Evaluation
- 8. • Alternative ways of evaluating a given query – Equivalent expressions – Different algorithms for each operation • Cost difference between a good and a bad way of evaluating a query can be enormous • Need to estimate the cost of operations – Depends critically on statistical information about relations which the database must maintain – Need to estimate statistics for intermediate results to compute cost of complex expressions
- 9. • A transaction is a collection of operations that performs a single logical function in a database application • Transaction-management component ensures that the database remains in a consistent (correct) state despite system failures (e.g., power failures and operating system crashes) and transaction failures. • Concurrency-control manager controls the interaction among the concurrent transactions, to ensure the consistency of the database.
- 10. 10 Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative name Knowledge discovery in databases (KDD) Watch out: Is everything “data mining”? Query processing Expert systems or statistical programs
- 11. 11 The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Automated data collection tools, database systems, Web, computerized society Major sources of abundant data Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
- 12. 12 Data analysis and decision support Market analysis and management Target marketing, customer relationship management (CRM), market basket analysis, market segmentation Risk analysis and management Forecasting, customer retention, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers)
- 13. 13 Other Applications Text mining (news group, email, documents) and Web mining Stream data mining Bioinformatics and bio-data analysis
- 14. The architecture of a database systems is greatly influenced by the underlying computer system on which the database is running: • Centralized • Client-server • Parallel (multi-processor) • Distributed
- 15. Users are differentiated by the way they expect to interact with the system • Application programmers – interact with system through DML calls • Sophisticated users – form requests in a database query language • Specialized users – write specialized database applications that do not fit into the traditional data processing framework • Naïve users – invoke one of the permanent application programs that have been written previously – Examples, people accessing database over the
- 16. • Coordinates all the activities of the database system; the database administrator has a good understanding of the enterprise’s information resources and needs. • Database administrator's duties include: – Schema definition – Storage structure and access method definition – Schema and physical organization modification – Granting user authority to access the database – Specifying integrity constraints – Acting as liaison with users – Monitoring performance and responding to changes in requirements
- 18. • 1950s and early 1960s: – Data processing using magnetic tapes for storage • Tapes provide only sequential access – Punched cards for input • Late 1960s and 1970s: – Hard disks allow direct access to data – Network and hierarchical data models in widespread use – Ted Codd defines the relational data model • Would win the ACM Turing Award for this work • IBM Research begins System R prototype • UC Berkeley begins Ingres prototype – High-performance (for the era) transaction processing
- 19. • 1980s: – Research relational prototypes evolve into commercial systems • SQL becomes industrial standard – Parallel and distributed database systems – Object-oriented database systems • 1990s: – Large decision support and data-mining applications – Large multi-terabyte data warehouses – Emergence of Web commerce • 2000s: – XML and XQuery standards – Automated database administration