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Chapter 21: New Applications
· Decision-Support Systems
· Data Analysis
· Data Mining
· Data Warehousing
· Spatial and Geographic Databases
· Multimedia Databases
· Mobility and Personal Databases
· Information-Retrieval Systems
· Distributed Information Systems
· The World Wide Web
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Decision-Support Systems
· Decision-support systems utilize data collected by on-line
transaction-processing systems to generate summary data.
· Information for decision support can be extracted by simple
SQL queries, and by SQL extensions.
· Can interface statistical analysis packages (S++) with
databases.
· Data mining seeks to discover knowledge automatically, in the
form of statistical rules and patterns from large databases.
· A data warehouse archives information gathered from multiple
sources, and stores it under a unified schema, at a single site.
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Data Analysis
· Aggregate functions summarize large volumes of data to
support simple data analysis.
· A histogram partitions the values taken by an attribute into
ranges, and computes an aggregate over the values in each
range; cumbersome to use SQL to construct a histogram.
· Cross-tabulation of number by size and color of sample
relation sales with the schema Sales(color, size, number).
Small Medium Large Total
Light 8 35 10 53
Dark 20 10 5 35
Total 28 45 15 88
· Can represent the data in relational form by using the value all
to represent subtotals.
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Data Analysis (Cont.)
Color Size Number
Light Small 8
Light Medium 35
Light Large 10
Light all 53
Dark Small 20
Dark Medium 10
Dark Large 5
Dark all 35
all Small 28
all Medium 45
all Large 15
all all 88
· Obtain (Light, all, 53) and (Dark, all, 35) by eliminating
individual tuples with different values for size, and by replacing
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the value of number by sum.
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Data Analysis (Cont.)
· Rollup: Moving from finer-granularity data to a coarser
granularity by means of aggregation.
· Drill down: Moving from coarser-granularity data to
finer-granularity data.
· Proposed extensions to SQL, such as the cube operation, help
to support generation of summary data.
The following query generates the previous table.
select color, size, sum(number)
from sales
groupby color, size with cube
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Data Mining
· Like knowledge discovery in artificial intelligence, data mining
discovers statistical rules and patterns; it differs from machine
learning in that it deals with large volumes of data, stored
primarily on disk.
· Knowledge discovered from a database can be represented by
a set of rules.
· Discover rules using one of two models:
1. The user is involved directly in the process of knowledge
discovery.
2. The system is responsible for automatically discovering
knowledge from the database, by detecting patterns and
correlations in the data.
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Knowledge Representation Using Rules
· General form of rules:  Xantecedent  consequent
X is a list of one or more variables with associated ranges.
· The rule  transactions T, buys(T, bread)  buys(T, milk)
states: if there is a tuple (ti , bread) in the relation buys, there
must also be a tuple (ti , milk) in the relation buys.
· Population: Cross-product of the ranges of the variables in the
rule.
· Support: Measure of what fraction of the population satisfies
both the antecedent and the consequent of the rule.
· Confidence: Measure of how often the consequent is true
when the antecedent is true.
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Classes of Data-Mining Problems
· Classification: Finding rules that partition the given data into
disjoint groups that are relevant for making a decision (e.g.,
which of several factors help classify a person's credit
worthiness).
· Useful to determine associations between different items (e.g.,
someone who buys bread is quite likely also to buy milk).
· Sequence correlations: determine correlations between
information and related sequenced data (e.g., When bond
rates go up, stock prices go down within two days).
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User-Guided Data Mining
· Primary responsibility for discovering rules is with the user, who
may runs tests on the database to verify or refute a hypothesis.
· Derive from the database the confidence and support for the
rule expressing the hypothesis.
· Example: Refine the hypothesis "People who hold master's
degrees are the most likely to have an excellent credit rating."
into the rule:
 people P, P.degree = Masters and C.income  75, 000
 C.credit = excellent
· Data-visualization detect patterns in large volumes of data via
maps, charts, color-coding, and other graphical
representations.
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Discovery of Classification Rules
· Training set: a data sample in which the grouping for each
tuple is already known.
· Top down generation of classification tree.
­ Each node of the tree partitions the data into groups based
on one attribute.
­ The construction of a path in the tree stops when either the
attribute has properly classified the data, or all attributes
have been considered.
· Credit example: Within each partition based on degree, the
partitions defined by income adequately classify the tuples.
Tree construction stops here for each partition based on
degree.
· In general, different branches of the tree could grow to different
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levels.
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Discovery of Association Rules
 transactions T, buys(T, bread)  buys(T, milk)
· Derive rule by associating a bitmap with each transaction, with
one bit per item of interest in the shop.
· Usually desire rules with strong support, which will involve only
items purchased in a significant percentage of the transactions.
 transactions T, buys(T, i1) and . . . and buys(T, in)  buys(T, i0)
· Consider all subsets of the set of relevant items, and, for each
set, check whether there is a sufficient number of transactions
in which all the items in the set are purchased.
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Discovery of Association Rules (Cont.)
· Few sets: Determine level of support via a single pass.
­ A count is maintained for each set, initially set to 0.
­ When a transaction is fetched, the count is incremented for
each set of items, all of whose bits are set in the
transaction's bitmap.
­ Sets with a high count at the end of the pass correspond to
items with a high degree of association.
· Many sets: Cost of processing each transaction becomes
correspondingly large.
­ Use multiple passes, considering only some sets in each
pass.
­ Once a set is eliminated because it occurs in too small a
fraction of the transactions, none of its supersets needs to
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be considered.
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Data Warehousing
· Provides a single consolidated interface to data, making
decision-support queries easier to write.
· By accessing information for decision support from a data
warehouse, the decision maker ensures that on-line
transaction-processing systems are not affected by the
decision-support workload.
· Issues in building a warehouse:
­ When and how to gather data.
­ What schema to use.
­ How to propagate updates.
­ What data to summarize.
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Spatial and Geographic Databases
· Spatial databases store information related to spatial locations
and support efficient storage, indexing and querying of spatial
data.
· Special purpose index structures are important for accessing
spatial data, and for processing spatial join queries.
· Design databases (also CAD) databases store design
information about how objects (e.g., buildings, aircraft) are
constructed, or layouts of integrated-circuits.
· Geographic databases store geographic information (e.g.,
maps); often called geographic information systems or GIS.
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Representation of Geometric Information
· Various geometric constructs can be represented in a
database in a normalized fashion.
· Represent a line segment by the coordinates of its endpoints.
· Approximate a curve by partitioning it into a sequence of
segments; represent each segment as a separate tuple that
also carries with it the identifier of the curve (2D features such
as roads).
· Closed polygons: list its vertices in order, starting vertex is the
same as the ending vertex.
Alternative: Triangulation -- give polygon an identifier; each of
the triangles into which it is divided carries the identifier.
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Representation of Geometric Information (Cont.)
· Representation of points and line segments in 3-D similar to
2-D, except that points have an extra z component.
· Represent arbitrary polyhedra by dividing them into
tetrahedrons, like triangulating polygons.
Alternative: List their faces, each of which is a polygon, along
with an indication of which side of the face is inside the
polyhedron.
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Representation of Geometric Constructs
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1
2
1
1
3
3
4
5
2
2
1
3
4
5
2
line segment
triangle
polygon
polygon
{(x1,y1), (x2,y2)}
{(x1,y1), (x2,y2), (x3,y3)}
{(x1,y1), (x2,y2), (x3,y3), (x4,y4), (x5,y5)}
{(x1,y1), (x2,y2), (x3,y3), ID1}
{(x1,y1), (x3,y3), (x4,y4), ID1}
{(x1,y1), (x4,y4), (x5,y5), ID1}
object representation
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Design Databases
· Represent design components as objects (generally geometric
objects); the connections between the objects indicate how the
design is structured.
· Simple two-dimensional objects: points, lines, triangles,
rectangles, polygons.
· Complex two-dimensional objects: formed from simple objects
via union, intersection, and difference operations.
· Complex three-dimensional objects: formed from simpler
objects such as spheres, cylinders, and cuboids, by union,
intersection, and difference operations,
· Wireframe models represent three-dimensional surfaces as a
set of simpler objects.
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Geographic Data
· Raster data consist of bit maps or pixel maps, in two or more
dimensions.
­ Example 2-D raster image: satellite image of cloud cover,
where each pixel stores the cloud visibility in a particular
area.
­ Additional dimensions might include the temperature at
different altitudes at different regions, or measurements
taken at different points in time.
· Design databases generally do not store raster data.
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Geographic Data (Cont.)
· Vector data are constructed from basic geometric objects:
points, line segments, triangles, and other polygons in two
dimensions, and cylinders, spheres, cuboids, and other
polyhedrons in three dimensions.
· Vector format often used to represent map data.
­ Roads can be considered as two-dimensional and
represented by lines and curves.
­ Some features, such as rivers, may be represented either
as complex curves or as complex polygons, depending on
whether their width is relevant.
­ Features such as regions and lakes can be depicted as
polygons.
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Geographic Data (Cont.)
· Geographic Information Systems (GIS), provide location
information.
· Vehicle navigation systems store information about roads and
services for the use of drivers.
· Global Positioning System or GPS unit ­ utilizes information
broadcast from GPS satellites to find the current location with
an accuracy of tens of meters.
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Spatial Queries
· Nearness queries request objects that lie near a specified
location.
· Region queries deal with spatial regions, e.g., ask for objects
that lie partially or fully inside a specified region.
· Queries that compute intersections of regions ­ spatial join of
two spatial relations with the location playing the role of join
attribute.
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Spatial Queries (Cont.)
· Spatial data is typically queried using a graphical query
language; results are also displayed in a graphical manner.
· Graphical interface constitutes the front-end; extensions of SQL
with abstract data types, such as lines, polygons and bit maps,
have been proposed as a back-end.
­ allows relational databases to store and retrieve spatial
information
­ queries can mix spatial and nonspatial conditions
­ extensions also include and allowing spatial conditions
(contains or overlaps).
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Indexing of Spatial Data
· k-d tree ­ early structure used for indexing in multiple
dimensions.
· Each level of a k-d tree partitions the space into two.
­ one dimension ­ at the node at the top level of the tree.
­ other dimension ­ in nodes at the next level and so on,
cycling through the dimensions.
· In each node, approximately half of the points stored in the
sub-tree fall on one side and half on the other.
· Partitioning stops when a node has less than a given maximum
number of points.
· The k-d-B tree extends the k-d tree to allow multiple child
nodes for each internal node; well-suited for secondary
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storage.
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Division of Space by a k-d Tree
3 1 3
2
3 3
2
· Each line corresponds to a node in the tree, and the maximum
number of points in a leaf node has been set at 1.
· Each line in the figure (other than the outside box) corresponds
to a node in the k-d tree.
· The numbering of the lines in the figure indicates the level of
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the tree at which the corresponding node appears.
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Quadtrees
· Each node is associated with a rectangular region of space;
the top node is associated with the entire target space.
· Each non-leaf node divides its region into four equal sized
quadrants, and correspondingly each such node has four child
nodes corresponding to the four quadrants.
· Leaf nodes have between zero and some fixed maximum
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number of points (set to 1 in example).
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Quadtrees (Cont.)
· PR quadtree: stores points; space is divided based on regions,
rather than on the actual set of points stored
· Region quadtrees store array (raster) information.
­ A node is a leaf node if all the array values in the region that
it covers are the same. Otherwise, it is subdivided further
into four children of equal area, and is therefore an internal
node.
­ Each node corresponds to a subarray of values.
­ The subarrays corresponding to leaves either contain just a
single array element, or have multiple array elements, all of
which have the same value.
· Extensions of k-d trees and quadtrees handle indexing of line
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segments and polygons.
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R-Trees
· R-trees are a two-dimensional extension of B-trees and, with
variants like R+-trees and R
-trees, are useful for indexing
rectangles and other polygons.
· A rectangular bounding box is associated with each tree node.
· The bounding box associated with a non-leaf node contains
the bounding box associated with all its children.
· A polygon is stored only in one node, and the bounding box of
the node must contain the polygon.
· The storage efficiency or R-trees is better than that of k-d trees
or quadtrees since a polygon is stored only once.
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Example R-Tree
A set of rectangles (solid line) and the bounding boxes (dashed
line) of the nodes of an R-tree for the set of rectangles.
The R-tree is shown on the right.
A
A
B
C
G
I
D
E
F
H
1
1
0
03
3
2
2
B C D E F G H I
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Multimedia Databases
· To provide such database functions as indexing and
consistency, it is desirable to store multimedia data in a
database (rather than storing them outside the database, in a
file system).
· The database must handle large object representation.
· Similarity-based retrieval must be provided by special index
structures.
· Must provide guaranteed steady retrieval rates for
continuous-media data.
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Similarity-Based Retrieval
· Pictorial data -- Two pictures or images that are slightly
different as represented in the database may be considered
the same by a user (identify similar designs for registering a
new trademark).
· Audio data -- Speech-based user interfaces allow the user to
give a command or identify a data item by speaking (test user
input against stored commands).
· Handwritten data -- Identify a handwritten data item or
command stored in the database (requires similarity testing).
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Continuous-Media Data
· Most important types are video and audio data.
· Characterized by high data volumes and real-time
information-delivery requirements:
­ Data must be delivered sufficiently fast that no gaps in the
audio or video result.
­ Data must be delivered at a rate that does not cause
overflow of system buffers.
­ Synchronization among distinct data streams must be
maintained (video of a person speaking must show lips
moving synchronously with the audio).
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Multimedia Data Formats
· Store and transmit multimedia data in compressed form; JPEG
is the most widely used format for image data.
· Encoding each frame of a video using JPEG is wasteful, since
successive frames of a video are often nearly the same.
· MPEG standards use commonalities among a sequence of
frames to achieve a greater degree of compression.
· MPEG-1 stores a minute of 30-frame-per-second video and
audio in approximately 12.5 MB (compares with 75 MB for
video using only JPEG); quality comparable to VHS video tape.
· MPEG-2 designed for digital broadcast systems and digital
video disks; negligible loss of video quality.
Compresses 1 minute of audio-video to approximately 2.25
MB.
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Video Servers
· Current video-on-demand servers are based on file systems;
existing database systems do not met real-time response
requirements.
· Video server -- Multimedia data are stored on several disks
(RAID configuration), or on tertiary storage for less frequently
accessed data.
· Head-end terminals -- used to view multimedia data; PCs or
TVs attached to a small, inexpensive computer called a set-top
box.
· Network -- Transmission of multimedia data from a server to
multiple head-end terminals requires a high-capacity network;
often use asynchronous-transfer-mode (ATM) networks.
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Mobility and Personal Databases
· The mobile computing environment consists of mobile
computers, referred to as mobile hosts, and a wired network of
computers.
· Mobile hosts communicate to the wired network via computers
referred to as mobile support stations.
· Each mobile support station manages those mobile hosts
within its cell.
· Mobile hosts may move between cells, thus necessitating a
handoff of control from one mobile support station to another.
· Without fixed locations and network addressed, can become
difficult to determine the optimal location to materialize a query
result; user's locations may be a parameter of the query.
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Routing and Query Processing
Must consider these competing costs:
· User time.
· Connection time -- used to assign monetary charges in some
cellular systems.
· Number of bytes, or packets, transferred -- used to compute
charges in digital cellular systems
· Time-of-day based charges -- vary based on peak or off-peak
periods
· Energy -- optimize use of battery power; different forms of
data transfer present different power demands (requires less
energy to receive than to transmit radio signals).
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Broadcast Data
· Mobile support stations can broadcast frequently-requested
data; allows mobile hosts to wait for needed data, rather than
having to consume energy transmitting a request.
· A mobile host may optimize energy costs by determining if a
query can be answered using only cached data; if not then
must either:
­ Wait for the data to be broadcast
­ Transmit a request for data and must know when the
relevant data will be broadcast.
· Broadcast data may be transmitted according to a fixed
schedule or a changeable schedule.
­ For changeable schedule ­ the broadcast schedule must
itself be broadcast at a well-known radio frequency and at
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well-known time intervals.
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Disconnectivity and Consistency
· A mobile host may remain in operation during periods of
disconnection.
· Problems created if the user of the mobile host issues queries
and updates on data that resides or is cached locally:
­ Recoverability: Updates entered on a disconnected
machine may be lost if the mobile host fails. Since the
mobile host represents a single point of failure, stable
storage cannot be simulated well.
­ Consistency: Cached data may become out of date, but
the mobile host cannot discover this until it is reconnected.
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Mobile Updates
· Partitioning via disconnection is the normal mode of operation
in mobile computing.
· For data updated by only the mobile host, simple to propagate
update when mobile host reconnects; in other cases data may
become invalid and updates may conflict.
· When data are updated by other computers, invalidation
reports inform a reconnected mobile host of out-of-date cache
entries; however, mobile host may miss a report.
· Version-numbering­based schemes guarantee only that if two
hosts independently update the same version of a document,
the clash will be detected eventually, when the hosts exchange
information either directly or through a common host.
· Facilitating the reconciliation of inconsistent copies of data still
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under research.
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Information Retrieval Systems
· Information retrieval systems use a simpler data model than
database systems, but provide more powerful querying
capabilities within the restricted model.
· Queries attempt to locate documents that are of interest by
specifying, for example, sets of keywords.
· The query a user has in mind usually cannot be stated very
precisely, and hence information retrieval systems order
answers based on their potential relevance.
· Similarity based retrieval ­ similarity may be defined based on
keywords or other metrics (i.e., similarity of images in an image
database).
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Indexing of Documents
· Documents that contain a specified keyword can be located
using an inverted index, which maps each keyword Ki to the
set Si of documents that contain Ki .
· Storing the index for approximate retrieval saves space.
­ false drop ­ a few relevant documents may not be retrieved.
­ false positive ­ a few irrelevant documents may be retrieved.
­ Index should not permit any false drops, but may permit a
few false positives.
· Relevant performance metrics:
­ Precision -- what percentage of the retrieved documents
are relevant to the query.
­ Recall -- what percentage of the documents relevant to the
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query were retrieved.
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Indexing of Documents (Cont.)
· and operation -- Finds documents that contain all of a set of
keywords K1, K2, . . . , Kn. Retrieve the corresponding sets of
documents S1, S2, . . . , Sn. The intersection, S1  S2  . . .  Sn,
constitutes the desired set of documents.
· or operation -- gives the set of all documents that contain at
least one of the keywords K1, K2, . . . , Kn by computing the
union, S1  S2  . . .  Sn, of the sets.
· but not operation -- finds documents that do not contain a
specified keyword Ki. Given a set of document identifiers S, we
can eliminate documents that contain the specified keyword Ki
by taking the difference S - Si, where Si is the set of identifiers
of documents that contain the keyword Ki .
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Browsing and Hypertext
· Hypertext systems take the idea of storing document
identifiers or pointers and provide a facility where the user can
easily switch from one document to another.
· Typically use a point-and-click interface, where a simple mouse
click on the display screen on top of the referred document
retrieves and displays it.
· Hypermedia systems provide not only text, but also other
media such as images, videos, and audio clips.
· Distributed hypertext systems permit references to documents
stored at other sites in a distributed system such as the World
Wide Web.
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Distributed Information Systems
· Distributed information systems running on the Internet have
seen explosive growth in recent years.
· The World Wide Web system supports browsing such
information using the hypertext paradigm.
· Automated tools for locating and indexing information in such
distributed heterogeneous systems have also been developed;
provide standardized ways of accessing data and standardized
GUIs.
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The Gopher and WAIS
· A Gopher system consists of servers and clients.
­ Server organizes data into directories.
­ Client initially communicates with a server; the top level
directory of the server hierarchy is displayed as a menu.
­ Menu item can be another directory in the hierarchy, a
document, or a link to a directory on another server.
­ Allows a seamless connection to remote servers.
· Information retrieval in Gopher is based on browsing and
navigating a directory hierarchy.
· Wide Area Information System (WAIS), retrieves information via
a powerful keyword based indexing mechanism.
· Each site maintains a site description; describes the kind of
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information stored at other sites, and how to access them.
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The World Wide Web
· A distributed information system based on hypertext.
· Most Web documents are hypertext documents formatted via
the HyperText Markup Language (HTML), which is based on
the Standard Generalized Markup Language (SGML).
· HTML documents contain text along with font specifications,
and other formatting instructions; links to other documents can
be associated with regions of the text.
· The displayed document is a hypertext document; with an
appropriate browser, the user can click on a region that has a
link associated with it, and the document pointed to by the link
is then displayed.
· The programming language Java allows documents to contain
programs that are executed at the user's site; thus, documents
can be active, rather than just passive.
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Universal Resource Locators
· In the Web, the functionality of pointers is provided by
Universal Resource Locators (URLs).
· URL example: http://www.bell-labs.com/foo/bar
­ The first part indicates how the document is to be
accessed; "http" indicates that the document is to be
accessed using the HyperText Transfer Protocol.
­ The second part gives the unique name of a machine on
the Internet.
­ The rest of the URL is the path name of the file on the
machine.
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Universal Resource Locators (Cont.)
· With Gopher, what is displayed is either a document or a
directory; a HTML document display is simultaneously a
document and a directory.
· Gopher systems are set up and controlled by system
administrators; anyone connected to the Internet can create
documents on the Web, and give the URL to anyone else.
· Home pages ­ contain information about users and their work.
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Web Servers
· A Web server can easily serve as a front end to a variety of
information services.
· The document name in a URL may identify an executable
program, that, when run, generates a HTML document.
· When a HTTP server receives a request for such a document, it
executes the program, and sends back the HTML document
that is generated.
· The Web client can pass extra arguments with the name of the
document.
· To install a new service on the Web, one simply needs to
create and install an executable that provides that service.
· The HTML language supported by the Web provides a
graphical user interface to the information service.
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Display Languages
· Text markup languages such as the Standard Generalized
Markup Language (SGML) fill a void between plain text and
page description/text formatting languages.
· SGML provides a grammar for specifying document formats
based on standard markup annotations.
· In addition to formatting, hypertext link, and image display
commands, HTML provides some limited input features.
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Java
· Java language allows documents to be active (e.g., animation
by executing programs at the local site).
· Java programs can be stored at server sites (like HTML
documents), and can be downloaded and executed by any
client site.
· Benefits of Java:
­ Flexible interaction with the user.
­ Executing programs at the client site speeds up interaction
greatly, compared to every interaction being sent to a server
site for processing.
· Java's security system ensures that the Java code does not
make any system calls directly. It notifies the user about
potentially dangerous actions, such as file writes, and allows
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the option to abort the program or to continue execution.
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Web Interfaces to Databases
· Extremely useful to link databases used for transaction
processing with the Web.
Example: Information filled in on an HTML order form can be
executed as a database transaction; the results can be
formatted into HTML and displayed to the user.
· Fixed HTML sources for display to users have limitations:
­ Cannot customize fixed Web documents for individual
users.
­ Problematic to update Web documents, especially if
multiple Web documents replicate data.
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Web Interfaces to Databases (Cont.)
· Generate Web documents dynamically -- A document request
executes a program to run queries on the database and to
generate a document based on the results.
· Tailor the display based on user information stored in the
database.
· Define data in Web documents by queries on a database;
whenever relevant data in the database are updated, the
documents will be updated too.
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Web Interfaces to Databases (Cont.)
· Web interfaces to databases simplify the tasks of format
conversions from HTML to SQL and from database results into
HTML.
· Define a HTML document in a macro language with embedded
SQL queries.
Variables defined in HTML forms can be used directly in the
embedded SQL queries.
· When the document is requested, a macro processor executes
the SQL queries, and generates the actual HTML document
that is sent to the user.
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Locating of Information on the Web
· The Archie system automatically follows Gopher links to locate
information, and creates a centralized index of information
found from various sites.
· Web crawlers follow the hypertext links in documents to find
other documents, and build an index on the documents.
· These systems run a background process to
­ find new sites.
­ obtain updated information from known sites.
­ discard defunct sites.
· Since no central authority is required for registering
documents, users can easily create new documents and locate
the documents via an index.
Database Systems Concepts 21.68 Silberschatz, Korth and Sudarshan c 1997