Database Types and Applications

Types of Databases and Database Applications

• Traditional applications involve numeric and textual databases.
• Recent applications include multimedia, GIS, biological/genome, and mobile databases alongside data warehouses.
• Social networks capture data about people and their communications through posts, tweets, photos, and videos.
• Platforms include Facebook, Twitter, and LinkedIn.
• Search engines like Google, Bing, and Yahoo collect web pages for search.
• New technologies from non-database software vendors manage large web data.
• Big Data uses storage systems with distributed computer clusters
• NOSQL systems also manage large quantities of data.
• Cloud computing involves data residing in huge data centers.

Basic Definitions

• A database refers to a collection of related data.
• Data is known facts recorded with implicit meaning.
• Mini-world represents a real-world segment stored in a database, such as student grades at a university.
• A Database Management System (DBMS) is software for creating and maintaining a computerized database.
• A Database System includes the DBMS software and the actual data, and it sometimes includes applications.

Impact of Databases and Database Technology

• Businesses leverage databases in banking, insurance, retail, transportation, healthcare, and manufacturing.
• Service industries use databases in finance, real estate, law, electronic commerce and small business.
• Education utilizes databases for content and delivery.
• Social networks, environmental/scientific fields, medicine, and genetics rely on databases.
• Personalized applications are based on smart mobile devices.

Typical DBMS Functionality

• Defining the database involves specifying data types, structures, and constraints.
• Constructing or loading the database means storing the initial content on a storage medium.
• Database manipulation includes retrieval through querying and reporting, and modification via insertions, deletions, and updates.
• Database access is being done more and more through Web applications
• Processing and sharing allows concurrent user and program access, ensuring data validity and consistency.

Application Activities

• Applications interact by generating queries requesting specific data subsets.
• Transactions may read and/or update values or generate new data in the database.
• Applications prevent unauthorized data access and adapt to changing user needs.

Additional DBMS Functionality

• Protection or security measures prevent unauthorized access.
• "Active" processing involves internal actions on data.
• Presentation and visualization is critical
• Database maintenance includes database, software, and system upkeep of associated programs.

Example Database (UNIVERSITY)

• The mini-world is a university environment.
• Mini-world entities include students, courses, sections, departments, and instructors.
• Relationships include sections being part of specific courses, students taking sections, and courses having prerequisites.

Main Characteristics of the Database Approach

• A DBMS catalog stores a database description that includes data structures, types, and constraints. -This description is meta-data.
• Program-data independence insulates programs from data changes.
• Data models offer data abstraction by hiding storage details via a conceptual view.
• Views support multiple perspectives, presenting only relevant data to each user.
• Concurrent users can retrieve and update data.
• Concurrency control in the DBMS guarantees proper transaction execution.
• A recovery subsystem ensures transaction effects are permanently recorded.
• OLTP (Online Transaction Processing) allows many concurrent transactions per second.

Database Users

• Users are either those who use/control content and develop applications or those who design DBMS software.
• Actors on the scene include database administrators(DBA) and Database Designers
  • DBAs handles authorizations, coordination, monitoring, and resource management.
  • Database designers define content, structure, constraints, functions, and transactions based on their understanding of end-user needs.
• End-users are divided into Casual, Naïve/Parametric, Sophisticated, and Stand-alone users.
• Casual end users access databases occasionally when needed.
• Naïve or Parametric end users consists of a large group of end-users who use defined functions or canned transactions Examples include mobile apps users, social media users, bank-tellers.
• Sophisticated end users are business analysts, scientists, engineers.
• Stand-alone end users maintain personal databases with read-to-use packaged applications such as tax software.
• System analysts understand user needs and design applications.
• Application programmers implement and debug according to analyst specifications.
• Business analysts analyze large data volumes for decision making.

Database Users – Actors Behind the Scene

• System designers/implementers design and implement DBMS, modules, and interfaces, testing and debugging them.
• Tool developers create software for modeling, design, performance monitoring, prototyping, user interface creation, and simulation.
• Operators and maintenance personnel manage database system hardware and software environments.

Advantages of Using the Database Approach

• Redundancy is controlled in data storage, development, and maintenance.
• Data is shared among users.
• Unauthorized access is restricted, managed by DBA staff.
• Object-oriented DBMSs allow persistent storage for program objects.
• Efficient query processing with storage structures (e.g., indexes).
• Optimized queries for efficient processing.
• Backup and recovery services.
• Multiple interfaces for different user classes.
• Complex relationships can be represented among data.
• Integrity constraints are enforced.
• Inferences and actions are drawn from stored data.

Additional Implications of Using the Database Approach

• Enforcing standards leads to success
• Standards apply to data formats, meta-data, and layouts.
• Application development time is reduced incrementally with each new application.
• The flexibility to change data structures means the database evolves as needed.
• Availability of current information is critical for transactions such as shopping and reservations.
• Economies of scale are achieved by reducing overlaps of resources and combining application/data across many apartments

Historical Development of Database Technology

• The Hierarchical and Network Models were introduced in the 1960s and dominated the 1970s.
• The relational model was introduced in 1970 and researched/experimented with at IBM, followed by many other universities.
• Relational DBMS products emerged in the early 1980s.
• Object-Oriented Database Management Systems (OODBMSs) emerged to cater complex data processing.
• New category called object-relational DBMSS (ORDBMSs) lead to many relational DBMSs now incorporating object database concepts
• Extended relational systems are adding capabilities for multimedia data, text, XML, and other data types.
• The Web contains data in HTML with links.
• E-commerce utilizes standards like XML.
• Scripting languages create dynamic Web pages.
• Database updates happen through Web pages.

Extending Database Capabilities

• New functionality expands into scientific applications, Earth/atmospheric sciences, XML, image/audio/video management, data warehousing/mining, spatial DM, and time-series data.
• Research and development focuses on new data types, complex data structures, operations, and indexing schemes.
• The first decade of the 21st century had increased user-generated data (Facebook, Twitter) and applications such as search engines.
• Cloud storage and backup have been making unlimited amounts of storage available for both users and applications
• Big Data technologies and NOSQL databases manage petabytes of data per day.
• Hadoop (from Yahoo) and MapReduce (from Google) enable distributed data processing based on Spark technology.
• NOSQL systems support rapid search/retrieval from documents and processing of graphs.

When Not to Use a DBMS

• Main inhibitors of using a DBMS include high initial investment and possible extra hardware.
• Overhead goes into ensuring security, concurrency and integrity functions.
• When a DBMS is unnecessary:
  • No need for complexity, simple with definitions -No need for shared access to data
• When a DBMS is infeasible:
  • Embedded systems may not have enough storage
• When no DBMS may suffice: -Stringent real-time requirements -Systems aren't able to handle complexity of data -Users need special operations not supported by the DBMS