Vision and Politics

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Vision and Politics

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• A. Silberschatz, M. Stonebraker, J. Ullman,
  "Database Research Achievements and
  Opportunities Into the 21st Century," ACM SIGMOD
  Record, March 1996.

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summary of the workshop held in May of 1995
Points of interest

• Key role in creating techonological
  infrastracture
• Areas of database research support for
  multimedia objects, distribution of information,
  new database applications, workflow and
  transaction management, and ease of database
  management and use.
• New capabilities provided by the technology
  developments in hardware capability, hardware
  capacity, and communication.
• ? need for industrial support

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2 themes ? of demand require new
solutionshistorically confirmed of ability to
put ideas to practical useso viability of db
research community vital
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The Changing World of Database Management

• A db system is a computerized record keeping
  system
• Stores provides access to information
• Basic components data hardware shoftware
• (consideration scope magnitude complexity)
• Hardware inpact ? cost ? speed components
  (multiprocessors) ? capacity
• Every humal enterprise includes computerized info

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The Case for DBMS Research

• Goal of this report
• financial support of db research is a worthwhile
  investment
• Illustrating the pay off from funding db research

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Recent Research Achievements

• in 1990 report the grate majority of market are
  US-owned companies
• products from research prototypes
• outline of the new developments

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Object-Oriented and Object-Relational Database
Systems

• In 1990 few research prototype of OODBs.
• Questionable relationship of OODBs and relational
  systems
• Few research prototypes combining features of
  relational DBMS (SQL access to simple data types)
  with OODBs (modelling of complex data) to create
  ORDBs (object relational database systems) and
  DOODs (deductive object oriented database
  systems)
• Today there are a variety of commercial OODBs
• It is a 75M/year market growing at about 50 per
  year.

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Support for New Data Types

• Research attempts of last decade concerning
  spatial and temporal data types are now part of
  commercial DBMSs and GIS

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Transaction Processing

• A DBMS support coordination of many users of
  shared information
• Traditional transaction management not enadequate
  for todays distributed information systems

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New db Applications
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EOSDISEarth Observing System Data Information
System

• EOS is a collection of satellites gathering info
  regarding atmosphere, oceans and land. They
  return 1/3 PentaByte/year of data that are
  integrated in EOSDIS.
• Challenges are
• Providing on-line access to PB-sized Databases
• Supporting thousands of information consumers
• Providing effective mechanisms for browsing and
  searching for the desired data

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Electronic Commerce

• There are thousands of projects supporting
  electronic purchasing of goods. E-commerce
  involves very large number of participants
  interacting over the network.
• Unlike EOSDIS there are many suppliers and many
  consumers. Among the challenges are
• Heterogeneous information sources must be
  integrated
• E-commerce needs reliable, distributed
  authentication and funds transfer

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Health Care Information Systems

• Physicians need to draw on different kinds of
  info like
• Medical records on various hospitals
• Info about drugs
• Procedures
• Diagnostic tools
• Transforming health-care sector will have major
  impact on cost and quality. Challenges are
• Integration of heterogeneous forms of information
• Access control to preserve confidentiality of
  medical records
• Interfaces to information appropriate for
  health-care professionals

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Digital Publishing

• Storage of books articles in electronic form
  and delivery through high speed networks offering
  new features like audio video.
• Education industry draws much closer ro
  publishing and offers facilities like interactive
  learning. Challenges are
• Management and delivery of extremely large bodies
  of data at very high rates
• Protection of intellectual properties

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Trends That Affect Database Research

• Technological Trends
• the last 50 years exponential improvement
• Improvement by factor 10 every 10 years on
•         of machine instruction executable in a
  sec
•         processor cost
•         amount of secondary storage per unit
  cost
•         amount of main memory per unit cost
• Improvement in price/performance ? new products,
  services
• The last few years
•         bits transmitted / unit cost
•         bits transmitted / sec
• so able to deal with Terabytes, complex queries
  cost effectively

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Database Architecture Trends

• changes in db structure and use
• The relational approach is today ubiquitous (from
  very large parallel architectures to home
  computers)
• Client-server architectures will become
  progressively more common for database servers to
  be accessed remotely over networks.
• The traditional data has been joined by various
  kinds of multimedia data. This trend is fuelling
  the success of ORDB

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Information Highway

• of Web bits carried by the Internet ? 15-20
  per month, or a factor of 10 growth per year.
• db will play a critical role in this information
  explosion.

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New Research Directions

• Putting multimedia objects to DBMSs
• Distribution of information
• New uses of db
• New transaction models
• Easy use and management of db

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Support for Multimedia Objects

• Areas of research in multimedia data
• Tertiary Storage
•         new level of storage hierarchy
•         is made by buffering selected data to
  secondary storage like acess to secondary storage
  by buffering selected data to main memory from
  disk
• Tertiary storage devices are orders of magnitude
  slower than secondary storage(disks), yet also of
  vastly greater capacity.

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Support for Multimedia Objects

• New Data Types
• To support multimedia objects
• QoS
• delivering multimedia data to many users
• bottleneck
• different needs (movie/ lecture video)
• optimize access based on predicted use

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Support for Multimedia Objects

• User Interface Support
• Requirenment of new interface other than SQL
• Ex Quering image db need interface that allows
  description of color, shape, other
  characteristics
• For ex. Course video sample frames, text-based
  indexes, segment search

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Distribution of Information

• new environment facilitated by the Web requires
  rethinking of the concepts in current distributed
  database technology
• Degree of autonomy
• Db sources connected through a network owned by
  different participants (health care system Web)
• Refuse connection
• Different systems capabilities
• Accounting and Billing
• Client payments for each access to remote data
• Quering strategies- billing rates. Willing?

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Security and Privacy

• Flexible authentication and authorization systems
• Sale information of anonymous user

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Replication and Reconciliation

• Nodes disconnected
• Data often duplicated
• Copies reconciled at connection
• Frequent event
• Need for high speed protocols algorithms
• Ex call routing system

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Data Integration and Conversion

• Information sources has a variety of formats and
  models
• Use of mediators like agents

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Information Retrieval and Discovery

• problems
• ? information of informally connected sources /
  heterogeneous data
• Changes without notice
• Unclear definitions
• Need for techniques to support searches like in
  db technology (indexes)

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Data Quality

• Different sources with different reliability
• Evaluate and query the reliability or the lineage
  (origin)

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• Data Mining

• Extraction of information from large bodies
• Decision makers
• Fast response
• Formulate query
• Optimization techniques for complex queries
• Use of non expert users

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Data Warehouses

• Huge collections of data mainly used for decision
  support systems. They copy of data from one or
  more databases.
• Issues
• Tools for data pumps (modules for obtaining
  updates/ translate them)
• Methods for data scrubbing (data consistent
  identify different representation of the same
  value)
• Create metadictionary (how data obtained)

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Repositories

• storing and managing both data and metadata
• They must
• Obtain an evolving set of representations of the
  same or similar information (module represented
  as source code, object code, flow diagram etc..)
• Support versions (snapshots of an element
  evolving over time) and configurations (versioned
  collection of versions)

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Easy of use

• Improved interfaces for end user and application
  programmer- administrator
• Easier installation and upgrade of db management
  systems

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Database MetatheoryAsking the Big
QueriesChristos H. PapadimitriouUniversity
of California San Diego
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Theory and its Function  

• In the context of an applied science, theory in
  broad sense is the use of significant
  abstraction, scientific research, the suppression
  of low-level details of the object or artifact
  being studied or designed.

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Solution to complexity imposed by theoreticians 

• (a) They develop mathematical models of the
  artifact. Turing machines, formal languages, and
  the relational model

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Solution to complexity imposed by theoreticians 

• (b) abstract models can become reality
  (typically, algorithms and representational
  schemes) that are derived from the mathematical
  models.This function of theory is what we usually
  mean by synthesis or positive results.Such
  results must be actually verified by experiments.

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Solution to complexity imposed by theoreticians 

• (c) Analyze the mathematical models to predict
  the outcome of the experiments (and calibrate the
  models).

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Solution to complexity imposed by theoreticians 

• (d) explore. They develop and study extensions
  and alternative applications of the model, and
  they seek its ultimate limitations.
• Introduce and apply more and more sophisticated
  mathematical techniques.
• build a theoretical body of knowledge and a
  mathematical methodology that overcome the
  motivating artifact and model
• Exploration is usually guided by aesthetics,
  taste, and sense of what is important and
  relevant.

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uncontroversial necessary parts of the research
and discovery process in any science of the
artificial 

• (a)Model building
• (b) synthesis
• (c) analysis
• criticized most predictably
• liked by theoreticians exploration

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arguments in defense of exploration  

• (1) It has been historically beneficial to
  computer science
• (2) in reasonable doses, it promotes the fields
  health and connectivity
• (3) exploration and proving elegant theorems are
  natural and attractive activities, and so it
  would be wrong and futile to repress them.

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Drawbacks

• can disortent the field and lead at into crisis,
  when it is disproportionately extensive in
  comparison to model budding, synthesis, and
  analysis
• (2) will not thrive if it consistently ignores
  practice
• (3) requires true discipline and honesty in its
  exposition, especially in avoiding frivolous and
  unchecked claims of relevance and applicability.

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On Negative Results  

• In computer science theorems are judged by (as in
  mathematics)
• Elegance
• Depth
• importance in long-term research
• But here also
• complexity-reducing or points out a setback in
  this regard.
•  

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• negative results are the only possible
  self-contained theoretical results
• Positive results complexity reducing solutions
  such as algorithms and presentation schemes must
  be validated experimentally and can therefore be
  considered as mere invitations to experiment.
• delimitation is the ultimate success in
  exploration  

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What is Good Theory?  

• Paul Feyerabend
• Science is an essentially anarchic
  enterprise. There is no idea that is not
  capable of improving our knowledge. The only
  principle that does not inhibit progress is
  anything goes.

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What is Good Theory?  

• although there is no such thing as bad science,
  success is an important aspect
• Not just an inner process driven by methodology
  and results but a much more complex predicate of
  the social dynamics of the field and its
  environment, and of course open to circumstances
  and chance.
• An adoption metaphor in computer science from
  other sciences is essential and increases its
  prestige and propagandistic value.

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What does this all mean for theoreticians?

• free-style exploratory theoretical research
• its success will depend mainly on its
  propagandistic value, ability to
  contaminate its environment,
• especially on its potential to influence
  practice
• Theoreticians should be expositor and popularizer
  to bring his or her results to the attention of
  the experimentalist and the practitioner, to
  convince them of their value by arguments that
  are measured, rigorous, and credible

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What does this all mean for theoreticians?

• do your own experiments helps a lot
• ultimate success of a scientific idea is, of
  course, the launching of a victorious scientific
  revolution

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Paradigms and Revolution
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The stages of the scientific process according to
Thomas Kuhn for natural science
                   
Immature science
Normal science
Crises
Revolution
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The stages of the scientific process according to
Thomas Kuhn for natural science
Immature science
Normal science
Crises
Revolution
                   
 

• long periods of normal science, in which the
  field progresses incrementally within a broadly
  accepted framework that includes not only
  scientific assumptions and theories, but also
  conventions about what are appropriate questions
• to ask and how further development should
  proceed. Such a framework is called a paradigm.
  Copernicus model and Einsteins general theory
  seem to be the most frequently mentioned
  paradigms.
• scientists consider it their duty to defend the
  paradigm and show that it works

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Natural Science

• But cruel facts that do not fit in the paradigm
  accumulate, despite the communitys ingenious
  efforts to sweep them under the rug the paradigm
  creaks and staggers, and we enter a stage of
  science in crisis
• ?ew kinds of ingenuity and imagination develop
  and compete. Eventually, and typically, one of
  them triumphs and becomes the next paradigm this
  is the stage of scientific revolution
• ultimate success of a scientific idea is, of
  course, the launching of a victorious scientific
  revolution

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Immature science
Normal science
Crises
Revolution
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adaptation to applied science and the sciences of
the artificial.

• Kuhns model ? static / eternal
• in the sciences of the artificial study
  artifacts, which keep changing while studied or
  because it is being studied
• tight closed-loop interaction between a science
  and its object.
• in case of computer science stages of Kuhns
  model are much accelerated
• Crises in natural science are caused by the
  accumulation of anomalies, observations of the
  objective reality that cannot fit the current
  paradigm. In contrast, in computer science we
  have no objective reality against which to judge
  our scientific work.

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the operational analog of falsifiability in
computer science

• research units (researchers, papers, research
  groups, results,or subfields) influence each
  other
• Connection
• Autistic behavior is the exception that tests the
  wisdom of the rule
• Most of theory is within a few hops from
  practice, and vice-versa.

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• bottom snapshot
• local situation seems unchanged (say, the average
  degree is the same)
• connectivity is low
• Tangents and introverted components are the rule
• The little connectivity that exists is via long
  paths
• Practitioners stop communicate to relevant theory

interaction unpleasant, unfriendly, defensive
style The field is in crisis.
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• Revolution as in natural science
• Practitioners (having given up on theory) develop
  and use their own abstractions, models, and
  mathematical techniques
• while theoreticians make their own attempts to
  reconnect to practice (responding to pressures
  from within their community and outside).
• The uninspiring practical problems and the
  unresponsive theoretical work that triggered the
  crisis become less central, and new small
  research traditions blossom.
• Well-targeted exploratory theory connects several
  of them, and a new healthy state emerges from the
  ashes. A successfully championed new research
  paradigm may then take over

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Why this relational model is applied in computer
science

• (1) It was a powerful and attractive proposal
  (whose plausibility was expertly supported by
  theoretical arguments)
• (2) it was explicitly open-ended, a whole
  framework for research problems, applications,
  and experiments
• (3) it came as the result of a crisis (or was it
  immature science?)
• (4) it was indeed followed by a period of normal
  science.
• we are now in the blues of a crisis, or even in
  the flames of an on-going revolution

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A Brief History of Practice

• Ancient Greek tradition strongly favors theory
  over practice (Aristotle)
• Before the last century, an inventor could become
  famous only if he was a moonlighting major
  theoretician or artist (Archimedes, Aristarchus,
  Leonardo da Vinci) or if his invention helped in
  the spreading of theoretical knowledge
  (Gutenberg).
• Practice starts obtaining a measure of
  respectability with Galileo (1564- 1642) (and
  later under the influence of the British
  empiricist philosophers)
• However, only after James Watt (1736- 1819) did
  sophisticated theoretical knowledge come to the
  assistance of practice and invention, thus
  launching the industrial age and the traditions
  of applied science and engineering

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• Respect for practice is so universal today
• Theory and practice collaborated two centuries,
  with theory dominating important domains in
  applied science due to its academic prowess and
  prestige
• Serious and systematic ideological attack against
  the value and necessity of theory in applied
  science seems to be a novel and disturbing
  phenomenon of the last decade or so.
• Histrory of computer science, is a miniature of
  the history of science
• The strongest influence came from
  mathematics(and less from electrical engineering
  and physics),