Download Modern Technologies

Telecommunication
Technology and Management
Chapter 10 and Chapter 11
Thank you for all information and pictures referred in this lecture.
www.att.com and www22.verizon.com
Agenda

Old Technology

Modern Technologies
 Digital TV
 Cell Phone: Calling Features
 Hi-speed Internet

Network Management
 Expert System
 Knowledge Discovery and Data Mining

Other Intelligent Applications
2
Old Technologies
http://www.supermegatrolled.com/just-nokia-3310-destroysthe-dinosaurs/
http://www.prattonline.com/ResponsePoint.htm
3
Trend of Communication
4
Trend of Communication
5
Modern Technologies

Digital TV

TV Wireless Receiver

Requirements:

Wireless service from the Wireless Access Point to the
Wireless Receiver

Power outlet and connection of Wireless Receiver to TV
6
7
Modern Technologies

Home DVR

Ability to schedule recordings and pause live TV

Record up to four shows at once on a single DVR and
play them back in any room

Pause, fast-forward and rewind live or recorded shows
on any TV or pause your recorded show in one room
and pick it up in another

Play the same recorded shows on different TVs at one
time and control them separately
8
Modern Technologies

Interactive Applications (between TV and smart
phone)

Browse available TV content for current or future viewing,
search for content, and remotely manage recordings of
shows and movies on DVR.

With qualifying TV plans, download select popular TV
episodes/series from the Mobile Library to select
smartphones for viewing on the go.

Once a show is downloaded, you can watch it anytime!
9
Modern Technologies

TV Multiview

It allows you to choose your own Multiview
channels you want to watch.
10
Modern Technologies

Phone Services on TV

We can see who's calling without leaving the
comfort of their couch!

Caller ID notifications and Message Waiting Indicators
(MWI) will display on their TV
11
Modern Technologies

Calling Features






Call Screening – Only accept calls from a list of
phone numbers you select.
Call Transfer – Send a call that’s already in
progress to a different phone number.
Locate Me - Provides simultaneous ringing on up
to four wireless/landline numbers when someone
calls your home phone.
Call History – View a list of your recent calls, by
date and time, either online or on your TV screen.
Click to Call – Return a phone call using your Uverse TV screen and remote.
Caller ID on TV – See Caller ID and Voice Mail
notifications on your TV screen.
12
Modern Technologies

Online Photos and Music


Customers can now view the photos they've uploaded at
www.flickr.com on channel 91.
Share music and photos from networked Windows
PCs to TV
13
Modern Technologies

TV for Xbox 360

Access your DVR recordings from your
existing Xbox 360.

Switch seamlessly from playing games to
watching TV—without switching video inputs
on your TV.

Access the U-verse TV Menu, Guide, and a
robust library of On Demand programming.

Chat: Know instantly when your friends are on
Xbox LIVE®.

Use Xbox IM and Chat while watching TV.
14
Modern Technologies: Customer Support
15
Modern Technologies

Mobile Phone
16
Modern Technologies
Digital Voice: Calling Features
17
Modern Technologies
Digital Voice: Calling Features
18
Modern Technologies
Hi-Speed Internet
19
Network Solution for Business
20
Fiber Optic Technology!!
21
Network Management

Telecommunication networks are
extremely complex systems requiring
 High reliability
 High availability

The effective management of networks is
a critical, but complex, task.
 Telecommunications industry has heavily
invested in intelligent technologies.

Telecommunication industry has relied
on intelligent solutions to help manage
telecommunication networks.
http://www.qbase.gr/en/node/124
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Network Management

Building intelligent applications involved acquiring
valuable telecommunication knowledge



Human experts
Applying this knowledge: an expert system.
This knowledge acquisition process is so timeconsuming that it is referred to as the “knowledge
acquisition bottleneck”.

Data mining techniques are now being applied to industrial
applications to break this bottleneck,

Replacing the manual knowledge acquisition process with
automated knowledge discovery.
23
Data Mining
http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
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Expert Systems

Expert systems are programs which represent and
apply factual knowledge of specific areas of
expertise to solve problems



Require a knowledge engineer to acquire knowledge from
the domain experts
Encode knowledge in a rule-based expert system
These rules were very “ad-hoc” and as the number
of rules increased

Expert system became more difficult to understand and
modify
25
Expert Systems

The design of telecommunication expert
systems

needs to recognize all telecommunication
equipment incorporates self-diagnostic
capabilities
http://www.nextnine.com
26
Knowledge Discovery and Data Mining

Knowledge discovery is a field which has emerged from
various disciplines,

Artificial intelligence, Machine learning, Statistics, Databases.

Its process involves identifying valid, novel, potentially
useful and ultimately understandable patterns in data

Data mining, the most researched topic in this process,

Finding interesting patterns in the data via data analysis and
discovery algorithms.
27
Knowledge Discovery and Data Mining

The knowledge discovery process

Data preparation: selecting, cleaning and
preprocessing the data (e.g., filling in missing
values) and transforming it so that it is suitable for
data mining

Data mining: finding patterns in the data

Interpretation and evaluation: interpreting and
evaluating the patterns produced by data mining
28
Knowledge Discovery and Data Mining

A key motivation for knowledge discovery
 Replace or minimize the need for the time-consuming process of
manually acquiring knowledge from a domain expert.

Knowledge discovery is especially attractive to the
telecommunications industry since:
 Telecommunication networks are typically too complex to build
complete simulation models

Huge quantities of data are routinely available

Domain experts often are not aware of subtle patterns in data and
hence automated knowledge discovery can acquire new,
previously unknown, knowledge
29
Network Management Applications

Max & Opti-Max: Locating Problems in the Local
Loop

The Max (Maintenance administrator expert) system

diagnoses customer reported telephone problems in the local
loop, the final segment of the telephone network that connects
the customer to a central office

Max is a rule-based expert system



Diagnoses problems based on results of an electrical test on the
customer’s phone line,
Specific knowledge of the customer’s phone line and general
equipment knowledge.
Max determines where the trouble lies and selects the type of
technician to solve the problem.
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Network Management Applications


Max & Opti-Max: Locating Problems in the Local Loop
 Problem of Max

its performance is affected by the local characteristics of each site
and thus numerous rule parameters must be tuned to optimize its
performance.

This tuning process is time consuming and for this reason a system
called Opti-Max was created to automatically tune these parameters
to appropriate values.
Opti-Max takes as input a set of training examples,
 Problem description and a diagnosis assigned by an expert,

Uses a hill-climbing search to find a set of parameter values
which perform well on these examples.

Opti-Max performs a type of automated knowledge discovery.
31
Network Management Applications

Trouble Locator: Locating Cable Network Troubles


It determines the location of troubles in a local telephone cable network
Data generated by a nightly automated test to help narrow down potential
cables or network equipment which may be faulty;


The Trouble Locator uses a Bayesian network and Bayesian inference
to solve this problem.


Test results are not sufficient to determine the exact cause.
The system begins by generating a local plant topology graph and then from
this generates a Bayesian network, where each node in the network contains
state information (belief of failure) of a plant component.
This system is used by preventative maintenance analysts as a
decision support system.
32
Network Management Applications

TASA: Finding Frequently Occurring Alarm Episodes

The Telecommunication Network Alarm Sequence Analyzer
(TASA)


System for extracting knowledge about the behavior of the
network from a database of telecommunication network
alarms.
The goal of this system



To locate regularities in the alarm sequences in order to filter
redundant alarms
Locate problems in the network
Predict future faults
33
Network Management Applications

TASA operates in two phases


First phase: specialized algorithms are used to
find rules that describe frequently occurring alarm
episodes from the sequential alarm data
An example rule describing an alarm episode
is:

if alarms of types A and B occur within 5 seconds,
then an alarm of type C occurs within 60 seconds
with probability 0.7.
34
Network Management Applications

Second phase: collections of episodes are
interactively manipulated by the user


Interesting episodes from the original set can be
found
TASA supports this process by providing
operations to prune uninteresting episodes


Order the set of episodes
Group similar episodes
35
Network Management Applications

Scout: Identifying Network Faults via Data Mining

It operates by mining historical telecommunication data



Machine learning
Correlation techniques.
Scout identifies patterns of chronic problems directly
from the data by examining the network behavior
over periods of days and weeks.
36
Other Intelligent Applications

APRI: Predicting Uncollectible Debt

The Advanced Pattern Recognition and Identification
(APRI) system


The output of APRI is fed into a decision support
system which can take a variety of actions


To predict the probability of uncollectible debt based on
historical data, including data of past uncollectibles
Blocking a call from being completed.
APRI automatically constructs Bayesian network
models for classification problems using extremely
large databases.
37
ANSWER: A Hybrid Approach to Network
Management

Automatic Network Surveillance with Expert
Rules (ANSWER)

ANSWER utilizes both rule-based and objectoriented technologies

Employing a rule-based extension to the C++
object-oriented programming language.
38
Forecasting Telecommunication Equipment Failures
from Time Series Data

Errors may occur during the transmission of data over the
network,
 These errors can be detected and the data rerouted through
alternate paths.

The effect of the failure of a single component is limited due to
the redundancy in modern large-scale telecommunications
networks.

Modern telecommunication equipment contains self-diagnostic
testing capabilities.
 When any of these tests fail, an alarm message is sent to a
centralized site,

where it may be handled by a human or by an expert system
39
Challenging Works

Existing Researches:


http://www.research.att.com/evergreen/what_we_do
/research.html?fbid=A_Kn38ajPF9#Computing and
Communications Foundations
Existing Software's:

http://www.research.att.com/export/sites/att_labs/sof
tware_tools/index.html?fbid=A_Kn38ajPF9
40
Telecommunication Union

ITU (International Telecommunication Union) is the
United Nations specialized agency for information and
communication technologies – ICTs.
 สหภาพโทรคมนาคมระหว่างประเทศ


http://www.itu.int/en/Pages/default.aspx
National Broadcasting and Telecommunications
Commission (NBTC)

คณะกรรมการกิจการกระจายเสี ยง กิจการโทรทัศน์และกิจการโทรคมนาคมแห่งชาติ

http://www.nbtc.go.th/wps/portal/NTC/eng
41
References



http://www.att.com/
Gary Weiss, John Eddy, Sholom Weiss, “INTELLIGENT
TELECOMMUNICATION TECHNOLOGIES”, AT&T Labs, AT&T
Corporation, United States
http://www22.verizon.com/home/aboutfios/

http://www.research.att.com/evergreen/what_we_do/research.html?f
bid=A_Kn38ajPF9#Computing and Communications Foundations

http://www.research.att.com/export/sites/att_labs/software_tools/ind
ex.html?fbid=A_Kn38ajPF9
42