April 15, 2024

FAME-IBM 2012 demo: An information Uplift approach for IPTV network monitoring

Published June 13, 2023, 10:20 a.m. by Courtney

This demo describes the contribution from kdeg, TCD for the FAME-ibm 2012 work plan.

You may also like to read about:

this is the 2012 film ibm project

demonstration i'm your chainsaw a phd

student from kdec trained college dublin

rob brennan is also involved in this


in this year's demonstration our

semantic application engine is located

with open flow management to support the

quality of experience based iptv network

monitoring through our visual widgets

our semantic uplift approach com

combines network logs

network models and domain expert

knowledge together to simplify the iptv

service delivery network monitoring

we use semantic 10 technologies to

capture and model the dome expert


in this year's demo we made three

enhancements on our approach

compose event temporal reasoning and

enhanced widgets

this figure shows how our semantic

athlete uplift approach works

our approach takes multi-network log


annotate systematic concepts to the raw


maintain annotated information into an

entity port

and then aggregate this information to

support root cause analysis for network


this is the visual interface of

last year's demo in which we support the

status and real-time event monitoring

and also the knowledge-driven anomaly

root cause analysis

the three enhancements this year we

applied on our layered visualization


in the

information uplifting layer semantic

concepts are annotated to the

streamlocked data

according to the domain experts insight

this annotated information is maintained

and upgraded

in the semantic processing layer

we enhance the reasoning capability for

compose and temporal events here

this uplifted information is presented

through our enhanced widgets

in the virtual dish visual

representation layer to spot better

understanding for iptv network


we will also use a diamond to show all


new enhancements with the scenario too

in this scenario a central router in

connect work was offline

which made

network traffic overflow on the other

two routers

and causes the low quality of the

experience problem for all ftm network


this is our visual interface

it's a web-based in

interface made by flags and its

communicates with our information

eclipse engine through blades middleware

the expert knowledge is modeled by

semantic anthologies and rules the

reasoning is driven by gender

this panel is quality of

experience panel

this is all

in this panel we can see the status of

different user groups

red label means bad condition and green

label means

normal condition

if we click the source domain user group

we can see the users in this group and

its network context

and after we click a particular user

we can know the activity service details

and the network contacts for this


here our approach enables the reasoning

cross composing event from

quality of experience iptv service and

network status

after we click the contacts label

the context of this iptv service is

showing in the network context panel

we can see this activity service goes

through video server code network

dislike and gateway

this is a very abstract view if we want

to know more detail we can drill down

particular node and

like a code network

and its

sub content is displayed through our

multi-resolution network content

contacts panel

the real time normally is reported as a

red bubble in the anomaly panel

but normally here means the event

affects the

quality of experience for network users

if we click particularly normally

in the anomaly analysis panel we can see

the analysis results of this anomaly

in this scenario the low quality of

experience normally is caused by an iptv

locality problem happens on all iptv


and our next step we performed a

track back analysis

because code network is the last node in

the network has bad status

and all nodes after it has

has ballistic

has bad condition too

we informed the code network probably is

the reason caused the low quality


this reasoning across the network logs

and service logs

which is benefited by our new composing

event reasoning capability

for the code network

there are two


one router is offline and the other two

router has

overflow problem

but the overflow problem happened after

the offline problem so according to our

new temporal reasoning capability we can

infer the root reason for this low qe

problem is caused by central rotor 3

it's offline

the suggested solution is to restart

this router

this is the demo of this year's visual

interface in this demo we showed how our

semantic information uplift approach and

its three new features

composing that temporal reasoning and

enhanced widget

supports better qe based fptv network



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