Transcript Voice over Internet Protocol (VoIP)

VOIP: Voice over Internet
Protocol
Broadband Phone Services
1
Introduction
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Voice over Internet Protocol (VoIP) is a protocol optimized for the
transmission of voice through the Internet or other packet switched
networks.
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VoIP is often used abstractly to refer to the actual transmission of voice
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VoIP is also known as IP Telephony, Internet telephony, Broadband
telephony, Broadband Phone and Voice over Broadband.
VoIP is the ability to make telephone calls over IP-based data networks
with a suitable quality of service and superior cost-efficiency.
www.about.com
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Introduction
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VoIP converts analog voice signals into digital data packets and
supports real-time, two-way transmission of conversations using
Internet protocol.
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VoIP calls can be made on the Internet using a VoIP service provider
and standard computer audio systems. Some service providers support
VoIP through ordinary telephones that use special adapters to connect
to a home computer network.
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Broadband Phone Service
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Broadband phone service [3]
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Enables voice telephone calls to work over your
high-speed Internet connection.
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A broadband phone (also known as a VoIP or
Internet phone) utilizes the same IP network as
your Internet service.
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Hardware adapters connect a standard telephone
to the high-speed Internet connection to create a
broadband phone.
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Broadband Phone Service
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Hardware and software broadband phones are
available [3].
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Hardware broadband phones use an adapter (either as an
add-on to your traditional phone or built in to an all-in-one
phone unit).
The hardware is then connected to either the router on your
network (via Ethernet) or your PC (via USB).
Software broadband phones use a software program to
make broadband calls.
http://qwest.centurylink.com/residenti
al/products/voip/how_it_works.html
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Broadband Phone Service Plans
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Service providers offer many different broadband
phone subscription plans [3].
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As with a cell phone, some service plans for these
telephones feature unlimited local calling or large numbers
of free minutes.
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The cost of broadband phone service is highly variable;
international, long distance and other calling charges often
still apply.
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Broadband Phone Reliability
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Compared to an Internet-based broadband phone
network, the standard home voice telephone
network is extremely reliable [3].
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Calls cannot be made with the broadband phone whenever
your home Internet service is down.
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Additional failures within the broadband phone service itself
will add to any downtime caused by the Internet connection.
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Broadband Phone Sound Quality
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The sound quality supported by broadband phone
service was significantly less than with traditional
telephone services [3].
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It can vary by provider and location, in general the quality
of broadband phone audio is very good.
You might notice a small delay ("lag") between when you
speak and the other party hears your voice.
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Why choose VoIP
[2]
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Why choose VoIP
[2]
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Why choose VoIP
[2]
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Why choose VoIP
[2]
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Why choose VoIP
[2]
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Why choose VoIP [1]
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Cost reduction.
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Consolidation.
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The ability to eliminate points of failure, consolidate accounting systems and
combine operations is obviously more efficient.
Simplification.
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There can be a real savings in long distance telephone costs which is
extremely important to most companies, particularly those with international
markets.
An integrated voice/data network allows more standardization and reduces
total equipment needs.
Bandwidth efficiency:
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PSTN networks reserve one pipe for every call. But in IP networks many
calls can use the same pipe simultaneously.
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Why choose VoIP [1]
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Important problems of fixed line
telephony:
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Limited extension of PBX
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Imperfect simultaneous calls
number
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No common local (short) numbers
between branches
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Slow installation of changes in all
telephony system – maintenance is
done of few companies (cabling,
PBX changes, system changes);
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Advantages of fixed line telephony
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Well tried quality of calls, services
Well known technology
Limited ability to transfer, forward
calls;
Paid calls between branches;
Slow and expensive installation
when moving to new office;
No possibility to integrate
telephony system with computer
systems.
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Why choose VoIP [1]
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Advantages of VoIP
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Extra functions (conversations recording, statistics of calls.
Faster and cheaper installation of the system
Portability
Integration with computer systems
Free calls between company’s branches
Free calls in your network
Lower subscription fees
You can use your current LAN – no need to change infrastructure;
No limit to simultaneous calls
Not limited number of users connected to PBX
Remote and fast maintenance
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References
[1] www.gmsvoip.com
[2] Peter Ingram, “Voice over Internet Protocol
(VoIP) - An Introduction”, Ofcom, 18th
January 2005.
[3] www.about.com
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Computer Networks and
Applications
E-Commerce: Recommender Systems
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Computer Networks [6]
Types of Networks
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Each computer or user in a network is referred to
as a node.
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The interconnection between the nodes is referred
to as the communication link.
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In most networks, each node is a personal
computer, but in some cases a peripheral device
such as a printer can be a node.
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Computer Networks [6]
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The number of links L required between N
PCs (nodes) is determined by using the
formula
L = N(N−1) / 2
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Network Fundamentals [6]
A network of four PCs.
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Network Fundamentals [6]
A star LAN configuration with a server as the controlling computer.
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Network Fundamentals [6]
A ring LAN configuration.
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Network Fundamentals [6]
A bus LAN configuration.
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Internet Applications [6]
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The Internet is a worldwide interconnection of computers by
means of a complex network of many networks.
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Anyone can connect to the Internet for the purpose of
communicating and sharing information with almost any other
computer on the Internet.
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The Internet is a communication system that accomplishes one
of three broad uses:
 Share resources
 Share files or data
 Communication.
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Internet Applications [6]
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The primary applications of the Internet are:
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E-mail
File transfer
The World Wide Web
E-commerce
Searches
Voice over Internet Protocol
Video
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Internet Applications [6]

E-mail is the exchange of notes, letters, memos,
and other personal communication by way of e-mail
software and service companies.
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File transfer refers to the ability to transfer files of
data or software from one computer to another.
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The World Wide Web is a specialized part of the
Internet where companies, organizations, the
government, or individuals can post information for
others to access and use.
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Internet Applications [6]
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E-commerce refers to doing business over the Internet and
other computer networks, usually buying and selling goods and
services by way of the Web.
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An Internet search allows a person to look for information on any
given topic. Several companies offer the use of free search
“engines,” which are specialized software that can look for
websites related to the desired search topic.
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Internet Applications [6]
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Voice over Internet Protocol (VoIP) is the technique of
replacing standard telephone service with a digital voice version
with calls taking place over the Internet.
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Video over Internet Protocol.
 Video or TV over the Internet (IPTV) is becoming more common.
The video (and accompanying audio) is digitized, compressed,
and sent via the Internet. It is expected to gradually replace some
video transmitted over the air and by cable television systems.
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World Wide Web
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A system of globally unique identifiers for resources
on the Web
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Uniform Resource Locator (URL):
http://example.org/wiki/Main_Page
 Domain name is example.org.
 Resource identified as /wiki/Main_Page
The publishing language HyperText Markup Language
(HTML);
The Hypertext Transfer Protocol (HTTP).
www.wikipedia.org
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http://www.w3schools.com/html
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World Wide Web
http://www.w3schools.com/html
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e-Commerce
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How to enhance e-Commerce sales?
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Browsers into buyers
Cross-sell
Recommender Systems!!
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What are recommender systems?
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Recommender systems are systems which provide
recommendations to a user
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Too much information (information overload)
Users have too many choices
Recommend different products for users, suited to
their tastes.
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Assist users in finding information
Reduce search and navigation time
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Case Study: Amazon
www.amazon.com
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Personalized Product
Recommendation?
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Which Sources of Information?
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Sources of information for recommendations:
[1]
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Browsing and searching data
Purchase data
Feedback provided by the users
Textual comments
Expert recommendations
E-mail
Rating
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Type of Recommendations [2]
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Population-based
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The most popular news articles, or searches, or
downloads
Frequently add content
No user tracking needed.
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Type of Recommendations [2]
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Item-to-item
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Content-based
One item is recommended based on the user’s
indication that they like another item.
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If you like Lord of the Rings, you’ll like Legend.
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Type of Recommendations [2]
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Challenges with item-to-item:
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Getting users to tell you what they like
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Financial and time reasons
Getting enough data to make “novel” predictions.
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What users really want are recommendations for things
they’re not aware of.
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Type of Recommendations [2]
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Item-to-item
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Most effective when you have metadata that lets
you automatically relate items.
Genre, actors, director, etc.
Also best when decoupled from payment
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Users should have an incentive to rate items
truthfully.
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Type of Recommendations [2]
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User-based
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“Users who bought X like Y.”
Each user is represented by a vector indicating
his ratings for each product.
Users with a small distance between each other
are similar.
Find a similar user and recommend things they
like that you haven’t rated.
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Type of Recommendations [2]
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User-based
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Advantages:
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Users don’t need to rate much.
No info about products needed.
Easy to implement
Disadvantages
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Pushes users “toward the middle” – products with more
ratings carry more weight.
How to deal with new products?
Many products and few users -> lots of things don’t get
recommended.
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Type of Recommendations: General [1]
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Content-based Recommender System
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Recommend items similar to those users preferred in the
past
User profiling is the key
Items/content usually denoted by keywords
Matching “user preferences” with “item characteristics” …
works for textual information
Vector Space Model widely used
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Type of Recommendations: General [1]
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Not all content is well represented by keywords, e.g.
images
Items represented by same set of features are
indistinguishable
Overspecialization: unrated items not shown
Users with thousands of purchases is a problem
New user: No history available
Shouldn’t show items that are too different, or too similar
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Type of Recommendations: General [1]
Collaborative Recommender System
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Memory-based collaborative filtering techniques
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Main problems: scalability and handling of new users
Model-based collaborative filtering techniques
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High accuracy of prediction
No need for searching the whole user-item rating matrix
(grouping users into models)
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Type of Recommendations: General [1]
Collaborative Recommender System
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Use other users recommendations (ratings) to judge item’s
utility
Key is to find users/user groups whose interests match with
the current user
Vector Space model widely used (directions of vectors are
user specified ratings)
More users, more ratings: better results
Can account for items dissimilar to the ones seen in the
past too
Example: Movielens.org
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Type of Recommendations: General [1]
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Different users might use different scales.
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Possible solution: weighted ratings, i.e. deviations from
average rating
Finding similar users/user groups isn’t very easy
New user: No preferences available
New item: No ratings available
Demographic filtering is required
Multi-criteria ratings is required
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Type of Recommendations: Example[1]
Cluster Models
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Create clusters or groups
Put a customer into a category
Classification simplifies the task of user matching
More scalability and performance
Lesser accuracy than normal collaborative filtering
method
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Type of Recommendations: Example[1]
Item to item collaboration (one that Amazon.com uses)
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Compute similarity between item pairs
Combine the similar items into recommendation list
Vector corresponds to an item, and directions correspond
to customers who have purchased them
“Similar items” table built offline
Example: Amazon.com
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Type of Recommendations: Example[1]
Knowledge based RS
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Use knowledge of users and items
Conversational Interaction used to establish current user
preferences
i.e. “more like this”, “less like that”, “none of those” …
No user profiles maintained, preferences drawn through
manual interaction
Query by example … tweaking the source example to fetch
results
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How RS Work?
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Similarity Measurement [4]
 For two data objects, X = (x1, x2, . . . , xn) and Y =(y1, y2, . . . ,
yn), the popular Minkowski distance is defined as

where n is the dimension number of the object and xi, yi are the
values of the ith dimension of object X and Y respectively, and q
is a positive integer. When q = 1, d is Manhattan distance; when
q = 2, d is Euclidian distance
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How RS Work?
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Similarity wu,v between two users u and v, or wi,j between two
items i and j, is measured by computing the Pearson correlation
[4]
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where the i ∈ I summations are over the items that both the users
u and v have rated and is the average rating of the co-rated
items of the u-th user
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Example
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Prediction and Recommendation Computation
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To make a prediction for the active user, a,
on a certain item, i, we can take a weighted
average of all the ratings on that item
according to the following formula [4]
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Example
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Example
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Example
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Challenging: # Users and # Items
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Clustering Algorithms
[5]
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Complex Networks
Recommender Systems and Social Web
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Complex Networks
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Realistic networks are Complex Networks
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Biological Network: How the brain work
efficiently?
Propagation Network: How viruses propagate
through the computer?
Competitor network: How rumors spread out the
human society?
Communication Network: How information
transmission exchanges on the Internet ?
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Biotech Industry in USA
http://ecclectic.ss.uci.edu/~drwhite/Movie
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Complex Networks
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What is a complex
network?
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Observes any form of user
behavior
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Web surfing logs
E-mails transactions
Communication over Blogs
Friend lists
Purchase history on ecommerce sites
Any other kinds action that
demonstrates user intent
It creates large scale graph
from all this behavior data
http://www.deqwas.com/en/technology.html
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Recommender Systems and Social Web [3]
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Recommender Systems and Social Web [3]
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Facebook only allows a bidirectional
connection among users
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if user A is connected to B then B is also
connected to A
Twitter users can follow without being
followed
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user A is linked to B, B is not linked to A.
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Recommender Systems and Social Web [3]
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Recommender Systems and Social Web [3]
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Recommender Systems and Social Web [3]
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If a user visited certain exhibits and her/his Facebook page mentions
she/he is a "Fan" of certain items, those would be saved for later
matching against new visitors profiles.
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New visitors would be recommended exhibits that were viewed by
people whom they most resemble based on the items they are "Fan".
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Find user profiles resembling current visitor's profile, extract tagged
photos that are also related to museum's key terms, recommend
exhibits relating to those.
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References
[1] Aalap Kohojkar, Yang Liu, Zhan Shi, “Recommender Systems”, March 31, 2008.
[2] Maria Fasli, “Agent Technology for e-Commerce”, http://cswww.essex.ac.uk/staff/mfasli/ATeCommerce.htm
[3] Amit Tiroshi, Tsvi Kuflik, Judy Kay and Bob Kummerfeld, “Recommender Systems and the Social Web”,
International Workshop at UMAP2011 on Augmenting User Models with Real World Experiences to
Enhance Personalization and Adaptation, July 15, 2011.
[4] Xiaoyuan Su, Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Advances in
Artificial Intelligence, Vol. 2009, 2009.
[5] Badrul M. Sarwar, George Karypis , Joseph Konstan, and John Riedl, “Recommender Systems for Largescale E-Commerce: Scalable Neighborhood Formation Using Clustering”, The Fifth International
Conference on Computer and Information Technology (ICCIT 2002) , 2002.
[6] Louis E. Frenzel, Jr., “Principles of Electronic Communication Systems”, The third edition, McGraw-Hill,
2008.
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