Future of Database Systems

Future of Database Systems University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

Lecture Outline • Review • Applications for Data Warehouses • Data Mining • Thanks again to lecture notes from Joachim Hammer of the University of Florida • Future of Database Systems • Predicting the future… • Quotes from Leon Kappelman “The future is ours” CACM, March 2001 • Accomplishments of database research over the past 30 years • Next-Generation Databases and the Future

What is Decision Support? • Technology that will help managers and planners make decisions regarding the organization and its operations based on data in the Data Warehouse. • What was the last two years of sales volume for each product by state and city? • What effects will a 5% price discount have on our future income for product X? • Increasing common term is KDD • Knowledge Discovery in Databases

Conventional Query Tools • Ad-hoc queries and reports using conventional database tools • E.g. Access queries. • Typical database designs include fixed sets of reports and queries to support them • The end-user is often not given the ability to do ad-hoc queries

OLAP • Online Line Analytical Processing • Intended to provide multidimensional views of the data • I.e., the “Data Cube” • The PivotTables in MS Excel are examples of OLAP tools

Data Cube

Operations on Data Cubes • Slicing the cube • Extracts a 2d table from the multidimensional data cube • Example… • Drill-Down • Analyzing a given set of data at a finer level of detail

Star Schema • Typical design for the derived layer of a Data Warehouse or Mart for Decision Support • Particularly suited to ad-hoc queries • Dimensional data separate from fact or event data • Fact tables contain factual or quantitative data about the business • Dimension tables hold data about the subjects of the business • Typically there is one Fact table with multiple dimension tables

Star Schema for multidimensional data Product ProdNo ProdName Category Description … Order OrderNo OrderDate … Fact Table OrderNo Salespersonid Customerno ProdNo Datekey Cityname Quantity TotalPrice Customer CustomerName CustomerAddress City … Date DateKey Day Month Year … City CityName State Country … Salesperson SalespersonID SalespersonName City Quota

Data Mining • Data mining is knowledge discovery rather than question answering • May have no pre-formulated questions • Derived from • Traditional Statistics • Artificial intelligence • Computer graphics (visualization)

Goals of Data Mining • Explanatory • Explain some observed event or situation • Why have the sales of SUVs increased in California but not in Oregon? • Confirmatory • To confirm a hypothesis • Whether 2-income families are more likely to buy family medical coverage • Exploratory • To analyze data for new or unexpected relationships • What spending patterns seem to indicate credit card fraud?

Data Mining Applications • Profiling Populations • Analysis of business trends • Target marketing • Usage Analysis • Campaign effectiveness • Product affinity

Data Mining Algorithms • Market Basket Analysis • Memory-based reasoning • Cluster detection • Link analysis • Decision trees and rule induction algorithms • Neural Networks • Genetic algorithms

Market Basket Analysis • A type of clustering used to predict purchase patterns. • Identify the products likely to be purchased in conjunction with other products • E.g., the famous (and apocryphal) story that men who buy diapers on Friday nights also buy beer.

Memory-based reasoning • Use known instances of a model to make predictions about unknown instances. • Could be used for sales forcasting or fraud detection by working from known cases to predict new cases

Cluster detection • Finds data records that are similar to each other. • K-nearest neighbors (where K represents the mathematical distance to the nearest similar record) is an example of one clustering algorithm

Link analysis • Follows relationships between records to discover patterns • Link analysis can provide the basis for various affinity marketing programs • Similar to Markov transition analysis methods where probabilities are calculated for each observed transition.

Decision trees and rule induction algorithms • Pulls rules out of a mass of data using classification and regression trees (CART) or Chi-Square automatic interaction detectors (CHAID) • These algorithms produce explicit rules, which make understanding the results simpler

Neural Networks • Attempt to model neurons in the brain • Learn from a training set and then can be used to detect patterns inherent in that training set • Neural nets are effective when the data is shapeless and lacking any apparent patterns • May be hard to understand results

Genetic algorithms • Imitate natural selection processes to evolve models using • Selection • Crossover • Mutation • Each new generation inherits traits from the previous ones until only the most predictive survive.

Lecture Outline • Review • Applications for Data Warehouses • Data Mining • Thanks again to lecture notes from Joachim Hammer of the University of Florida • Future of Database Systems • Predicting the future… • Quotes from Leon Kappelman “The future is ours” CACM, March 2001 • Accomplishments of database research over the past 30 years • Next-Generation Databases and the Future

Radio has no future, Heavier-than-air flying machines are impossible. X-rays will prove to be a hoax. • William Thompson (Lord Kelvin), 1899

This “Telephone” has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us. • Western Union, Internal Memo, 1876

I think there is a world market for maybe five computers • Thomas Watson, Chair of IBM, 1943

The problem with television is that the people must sit and keep their eyes glued on the screen; the average American family hasn’t time for it. • New York Times, 1949

Where … the ENIAC is equipped with 18,000 vacuum tubes and weighs 30 tons, computers in the future may have only 1000 vacuum tubes and weigh only 1.5 tons • Popular Mechanics, 1949

There is no reason anyone would want a computer in their home. • Ken Olson, president and chair of Digital Equipment Corp., 1977.

640K ought to be enough for anybody. • Attributed to Bill Gates, 1981

By the turn of this century, we will live in a paperless society. • Roger Smith, Chair of GM, 1986

I predict the internet… will go spectacularly supernova and in 1996 catastrophically collapse. • Bob Metcalfe (3-Com founder and inventor of ethernet), 1995

Lecture Outline • Review • Object-Oriented Database Development • Future of Database Systems • Predicting the future… • Quotes from Leon Kappelman “The future is ours” CACM, March 2001 • Accomplishments of database research over the past 30 years • Next-Generation Databases and the Future

Database Research • Database research community less than 40 years old • Has been concerned with business type applications that have the following demands: • Efficiency in access and modification of very large amounts of data • Resilience in surviving hardware and software errors without losing data • Access control to support simultaneous access by multiple users and ensure consistency • Persistence of the data over long time periods regardless of the programs that access the data • Research has centered on methods for designing systems with efficiency, resilience, access control, and persistence and on the languages and conceptual tools to help users to access, manipulate and design databases.

Accomplishments of DBMS Research • DBMS are now used in almost every computing environment to create, organize and maintain large collections of information, and this is largely due to the results of the DBMS research community’s efforts, in particular: • Relational DBMS • Transaction management • Distributed DBMS

Relational DBMS • The relational data model proposed by E.F. Codd in papers (1970-1972) was a breakthrough for simplicity in the conceptual model of DBMS. • However, it took much research to actually turn RDBMS into realities.

Relational DBMS • During the 1970’s database researchers: • Invented high-level relational query languages to ease the use of the DBMS for end users and applications programmers. • Developed Theory and algorithms needed to optimize queries into execution plans as efficient and sophisticated as a programmer might have custom designed for an earlier DBMS

Relational DBMS • Developed Normalization theory to help with database design by eliminating redundancy • Developed clustering algorithms to improve retrieval efficiency. • Developed buffer management algorithms to exploit knowledge of access patterns • Constructed indexing methods for fast access to single records or sets of records by values • Implemented prototype RDBMS that formed the core of many current commercial RDBMS

Relational DBMS • The result of this DBMS research was the development of commercial RDBMS in the 1980’s • When Codd first proposed RDBMS it was considered theoretically elegant, but it was assumed only toy RDBMS could ever be implemented due to the problems and complexities involved. Research changed that.

Transaction Management • Research on transaction management has dealt with the basic problems of maintaining consistency in multi-user high transaction database systems

Read account balance (balance = $1000) Transfer $100 to Mel Debits $100 SYSTEM CRASH Read account balance (balance = $900) Read account balance (balance = $1000) SYSTEM CRASH Read account balance (balance = $1000) No Transactions : Lost updates John Mel ERROR!

Read account balance (balance = $1000) Withdraw $200 (balance = $800) Write account balance (balance = $800) Read account balance (balance = $1000) Withdraw $300 (balance = $700) Write account balance (balance = $700) No Concurrency Control: Lost updates John Marsha ERROR!

Transaction Management • To guarantee that a transaction transforms the database from one consistent state to another requires: • The concurrent execution of transactions must be such that they appear to execute in isolation. • System failures must not result in inconsistent database states. Recovery is the technique used to provide this.

Distributed Databases • The ability to have a single “logical database” reside in two or more locations on different computers, yet to keep querying, updates and transactions all working as if it were a single database on a single machine • How do you manage such a system?

Lecture Outline • Review • Object-Oriented Database Development • Future of Database Systems • Predicting the future… • Quotes from Leon Kappelman “The future is ours” CACM, March 2001 • Accomplishments of database research over the past 30 years • “Next-Generation Databases” and the Future

Next Generation Database Systems • Where are we going from here? • Hardware is getting faster and cheaper • DBMS technology continues to improve and change • OODBMS • ORDBMS • Bigger challenges for DBMS technology • Medicine, design, manufacturing, digital libraries, sciences, environment, planning, etc...

Examples • NASA EOSDIS • Estimated 1016 Bytes (Exabyte) • Computer-Aided design • The Human Genome • Department Store tracking • Mining non-transactional data (e.g. Scientific data, text data?) • Insurance Company • Multimedia DBMS support

New Features • New Data types • Rule Processing • New concepts and data models • Problems of Scale • Parallelism/Grid-based DB • Tertiary Storage vs Very Large-Scale Disk Storage • Heterogeneous Databases • Memory Only DBMS

Coming to a Database Near You… • Browsibility • User-defined access methods • Security • Steering Long processes • Federated Databases • IR capabilities • XML • The Semantic Web(?)

Some things to consider • Bandwidth will keep increasing and getting cheaper (and go wireless) • Processing power will keep increasing • Moore’s law: Number of circuits on the most advanced semiconductors doubling every 18 months • Memory and Storage will keep getting cheaper (and probably smaller) • “Storage law”: Worldwide digital data storage capacity has doubled every 9 months for the past decade • Put it all together and what do you have? • “The ideal database machine would have a single infinitely fast processor with infinite memory with infinite bandwidth – and it would be infinitely cheap (free)” : David DeWitt and Jim Gray, 1992

Future of Database Systems