# Cognitive Computing to Optimize IT Services

Abbas Raza Ali
Cognitive and Analytics Practice Leader, IBM
<EMAIL_ADDRESS>
###### Abstract

In this paper, the challenges of maintaining a healthy IT operational
environment have been addressed by proactively analyzing IT Service Desk
tickets, customer satisfaction surveys, and social media data. A Cognitive
solution goes beyond the traditional structured data analysis by deep analyses
of both structured and unstructured text. The salient features of the proposed
platform include language identification, translation, hierarchical extraction
of the most frequently occurring topics, entities and their relationships,
text summarization, sentiments, and knowledge extraction from the unstructured
text using Natural Language Processing techniques. Moreover, the insights from
unstructured text combined with structured data allow the development of
various classification, segmentation, and time-series forecasting use-cases on
the incident, problem, and change datasets. Further, the text and predictive
insights together with raw data are used for visualization and exploration of
actionable insights on a rich and interactive dashboard. However, it is hard
not only to find these insights using traditional structured data analysis but
it might also take a very long time to discover them, especially while dealing
with a massive amount of unstructured data. By taking actions on these
insights, organizations can benefit from a significant reduction of ticket
volume, reduced operational costs, and increased customer satisfaction. In
various experiments, on average, upto 18-25% of yearly ticket volume has been
reduced using the proposed approach.

###### Index Terms:

Knowledge Extraction; Optimizing IT Services; Cognitive Computing; Topic
Clustering; Semantic Text Analytics; Service Desk.

##  I Introduction

With the increasing complexity of Information Technology’s (IT) operational
environments, organizations have build a need to maintain healthy IT systems
while reducing operational cost, ticket volume and improving customer
satisfaction at the same time. The IT operations are managed by Incident,
Problem and Change (IPC) ticketing system where an issue is considered as an
IT Service Desk ticket [2]. In this paper, these challenges have been
addressed by utilizing structured and unstructured ticket information provided
by the Service Desk (SD).

Cognitive computing offers the means to transform and scale-out IT Services
Delivery by eliminating the need for repeated training of the services
delivery skills. The learning ability of the system provides a key
differentiator to allow the cognitive solutions to continuously improve with
the on-going interactions and adapt to the changing needs. Additionally, the
quality of service can be improved by avoiding errors of omission as well as a
faster resolution of the problems through automated understanding and
reasoning of the most valid solutions. The human element, while significantly
reduced in the effort, will however continue to play a key supervisory role to
provide for undisruptive user interaction. In all, cognitive computing
transformation provides unsurpassed client experience and business
competitiveness at operational efficiency and agility that have not yet been
possible to date.

The SD receives tickets automatically generated by various device monitoring
systems. Also, the SD agents manually create tickets in response to the users’
requests. There are several analytical tools in the market place today that
provide reports based on only the structured data attributes of historical
ticket records. Some of these tools have the query capabilities to also
provide the most frequently occurring keywords or specified patterns in the
unstructured attributes of the tickets. These reports are then analyzed by
specialized IT professionals with the objective to remedy those revealed as
the most predominant topics. These tools surely provide benefits but with few
limitations such as capabilities to process vast amounts of data, inability to
reveal obvious and hidden issues, segregate the noise from the text, and
extract users’ sentiments related to key topics from Customer Satisfaction
(CSat) surveys and social media data. Existing surveys in the literature
estimates that 80% of information resides in unstructured data [6]. The
proposed approach is a solution that addresses all the mentioned limitations.

A key differentiator of the proposed approach is the ability to extract
meaning from fragmented sentences in the unstructured ticket description.
Generally, this unstructured text is in multiple languages which is detected
using language identification and normalized into a common language, English,
using machine translation techniques. Conversely, due to different language
skill levels of the globally sourced pool of agents, the grammar quality also
varies.

In order to develop a common baseline for the application of analytics, this
solution first translates all the non-English text into English, using
statistical translation models. The use of a statistical versus a grammar-
based model is necessitated due to the grammar quality of the input text. The
statistical translation model adds an additional level of complexity for a
cognitive system.

The system has enabled a number of clients to uncover patterns and trends for
identification of the contributing causes and prescription of precautions
within no time, using data from hundreds of thousands of SD tickets. Typically
these types of analyses would have taken the SD support teams weeks or
sometimes months. Hence, the identification of contributing causes enables the
support teams to leverage log analytics for root cause analysis. Additionally,
this allows the team to take a proactive stance toward identifying
opportunities to optimize the IT operations and improving client satisfaction
by demonstrating the following outcomes:

  * •

Identification of contributing causes and actionable precautions from problem
description and resolution.

  * •

Transparency to pervasive issues otherwise hidden behind volumes of
unstructured data in tickets under different categories.

  * •

Identification of emerging problems, trends and automated change point
detection.

  * •

Identification of patterns that can enable IT support teams to leverage real-
time machine and data analytics to find the rapid root cause.

  * •

Identification of self-service and automation opportunities, and transform
operations from reactive to proactive.

  * •

Transparency to pervasive issues otherwise hidden behind volumes of
unstructured text not captured in ticket categories.

  * •

Sentiment analysis of the CSat survey comments to determine potential future
dissatisfiers.

  * •

Social Media and CSat sentiments correlation with IT and applications’ issues
to improve client experience.

  * •

Predictive Analytics for improving resolution times by re-prioritization and
routing of the tickets.

  * •

Identification of skills and knowledge gaps derived from the analysis of first
time resolution of failures, resolution time and agent performance
differences.

The paper is organized as follows. Section II is devoted to an overview of the
cognitive computing platform components and methodology of the overall system.
Section III outlines key algorithms developed for this system. Section IV
describes various applications and use-cases whereas results of various
experiments are presented in Section V. The paper is concluded in Section VI.

##  II Methodology

The system comprises of three layers to extract structured insights from the
unstructured text as shown in Figure 1. The structured and unstructured data
of SD is gathered in a common repository. The Semantic Text Analytics (STA)
module of the system produces a pair of unique ticket identification and
unstructured text attributes as input from the repository to transform into
meaningful structured categories. These categories include language
identification, machine translation, entities and their relationships, topic
clusters, text summarization and sentiments. All these categorical features
provide orthogonal dimensions of a ticket. These new features combined with
the existing structured fields become an input to the Predictive Insights (PI)
module.

The PI module applies various classification, segmentation and forecasting
models. The predictions, clusters and forecasting projections, combined with
the raw data and text analytics insights are used to feedback the IT
infrastructure and SD environments to optimize them. The visualization module
is positioned to analyze a large number of tickets with their text and
predictive insights. Further, the module is utilized to explore opportunities
that leads to the optimization of end-to-end SD operations. This analysis
cycle is adaptive where the feedback of insights is used to enhance the
quality of the insights for new data which gradually improves the accuracy of
the system.

Figure 1: Schematic view of Cognitive IT Operational Analytics Platform

The STA module further comprises of three sequential layers to extract
structured insights from unstructured textual data. These layers include Text
Cleansing and Feature Extraction (TCFE), NLP techniques, and Post-processing
layers. The high-level architecture of the module is illustrated in Figure 2.
The unstructured input text is first cleansed and normalized by eliminating
its case, applying stemming, frequency and domain-specific stopwords
elimination, entity elimination, and regular expression-based keywords
candidate selection. The cleansed text becomes the input of NLP algorithms,
however, not all the TCFE techniques need to apply as pre-processing of an
algorithm. The language identification checks whether the text is in English
[2], in the case of non-English text it is translated into English [9]. The
text is further processed for hierarchical extraction of most frequent topic
failures and resolutions [5, 3], dynamic extraction of entities (applications,
servers, printers, IP addresses, etc.) that are failing the most frequently
and their relationships [8], text summarization [10] and sentiment analysis
[8]. The STA outputs several categorical features that represent key
information of the unstructured text.

Figure 2: Overview of Semantic Text Analytics

The text features extracted by STA are combined with structured features of
the raw data. The combined dataset has been used to predict, segment and
forecast several predictive use-cases, and this module is known as PI. It
consists of various use-cases which include predicting whether a newly
generated ticket will violate the Service Level Agreement (SLA), identifying
the most suitable (agent) skill to service the ticket, predicting Mean Time
Between Failures (MTBF), propagating prevention of IT devices (servers,
routers, switches, etc.) and correlating with corresponding incident ticket
data. Apart from the predictive use-cases PI includes relatively simple
analysis such as mean and standard deviation of hourly and peak CPU
utilization of the devices. These insights feedback to SD environment to
reduce the overall ticket volume and the feedback model helps to adapt the
overall system.

The resulting output of STA and PI combines with raw data for interactive
visualization and exploration dashboard known as Interactive Exploration and
Visualization Dash-boarding (IEVD) module. Also, IEVD provides statistics and
trends on the various structured data attributes of the raw ticket to identify
new opportunities. The IEVD has the capability of processing massively large
amounts of data and the characteristic to dynamically refresh visualization
upon selection of one or several categories in real-time mode [7].

The process of taking actions to remedy the key failures and continually
refreshing the findings by feeding new data is a proactive approach to
maintaining a healthy IT infrastructure, increasing customer satisfaction and
reducing SD operational costs. An example of such a workaround is when a
network connection from a user’s PC to an application server gets hung; an SD
agent may not be able to determine what could have gone wrong and may suggest
to the user to kill the session, reboot the machine and start a new connection
with the server. While this might work for a while, further investigation at
the network and server sides is suggested to identify the root cause of the
problem.

##  III Algorithms

This section uncovers the details of algorithms developed for STA, PI and
IEVD. These modules are connected with each other where every module consumes
the output of the ones processed earlier.

###  III-A Semantic Text Analytics

The TCFE component of STA has been used to remove noise and insignificant
dimensions from the text. It includes space and punctuation based text
tokenization, punctuation removal, case elimination, text normalization,
frequency and domain-specific stopwords elimination, and regular expression-
based keywords candidate selection. Furthermore, sentiment analysis based
sentence filtering and shallow semantic parsing (POS tagger) based filtering
have also experimented on a few datasets. These two techniques have a
significant impact on the quality of the topic clustering technique where in
most cases the addition of these techniques is failed to boost the quality of
the clusters.

####  III-A1 Language Identification

There are two approaches to language identification tried, a) encoding-based
and b) statistical language identification. Languages are identified by their
alphabets in an encoding-based approach. Their encoding can be used to
identify the languages, for an instance, in UTF-8 there is an encoding region
for Japanese, Korean and Chinese languages. However, it is a challenge to
distinguish European languages because their alphabet distribution overlaps
with each other. On the contrary, the statistical model-based approach uses a
classification framework to identify the language of the text. The two levels
of modeling are required to address those languages where space is not used as
word separator which includes Chinese, Korean, and Japanese. The first level
is used to identify where the given text is a European or Chinese, Japanese,
or Korean language where alphabet features are used for modeling framework.
Accordingly, the second level further classifies that the given text belongs
to which European language where word features are used in the modeling
framework. Table I shows statistics around both levels of language modeling.

TABLE I: Two levels statistical language modeling Input | First-level decoding | Second level decoding
---|---|---
Language | Documents | Errors | Documents | Error
Spanish | 98 | 0 | 98 | 0
Danish | 98 | 0 | 98 | 1
French | 98 | 0 | 98 | 0
German | 98 | 0 | 98 | 0
Italian | 98 | 0 | 98 | 0
Portuguese | 98 | 0 | 98 | 0
Swedish | 98 | 0 | 98 | 0
Japanese | 98 | 0 | 98 | 0
Korean | 98 | 2 | 98 | 0
Chinese | 98 | 3 | 98 | 0

####  III-A2 Statistical Machine Translation

A statistical approach to Statistical Machine Translation (SMT) has been
implemented by using Bayes’ rule. The equation 1 finds most appropriate
translation of a sentence in foreign language F into target language English
E.

$argmax_{E}[P(E\mid F)]=\frac{P(F\mid E)\,P(E)}{P(F)}$ (1)

The translation model calculates the probabilities of matching the source
segments to the target segment by a bilingual corpus. The language model
calculates the best sequences from target segments and combines them as a
final output. The system has been divided into three key components a)
language modeling, b) language decoding, and c) model tuning as shown in
Figure 2. The system has the capability to translate 10 non-English languages
into English which are illustrated in Table II where the coverage and weighted
coverage percentages represent the occurrence and frequency of the translated
words respectively.

Figure 3: Components of Statistical Machine Translation

The system has been evaluated by first tokenizing translated English text into
uni- and bi-grams with their frequency of occurrence. The most frequently
occurring N-grams are filtered out and translated using another ’authentic
system’ with translation accuracy of 97%+ to generate reference translation.
The words which are not translated by SMT, known as out-of-vocabulary (OOV)
words, are expected to be translated in reference translation. The source SMT
and reference translations have been compared to compute (frequency-based)
coverage and weighted coverage. Also, SMT’s OOVs are translated using the same
’authentic system’ and used to tune the overall translation model.

TABLE II: Statistical Machine Translation coverage and weighted coverage Input | Description (%) | Resolution (%)
---|---|---
Language | Coverage | Wt. Coverage | Coverage | Wt. Coverage
Spanish | 87.86 | 92.79 | 90.20 | 93.11
Danish | 93.22 | 92.74 | 87.34 | 89.56
French | 86.34 | 84.30 | 85.09 | 86.65
German | 90.82 | 92.45 | 89.72 | 90.44
Italian | 81.04 | 81.78 | 83.27 | 85.01
Portuguese | 86.00 | 91.01 | 88.88 | 91.35
Swedish | 84.51 | 86.03 | 85.89 | 86.71
Japanese | 75.32 | 76.10 | 79.45 | 78.45
Korean | 75.89 | 76.78 | 79.32 | 79.31
Chinese | 78.54 | 78.89 | 81.32 | 83.12

####  III-A3 Entities and Relationships

Entity and Keyword extraction entail extracting tokens having any patterns.
The information extracted from rules tagged under keywords consists of all
caps words (application names), two or more underscores (application or server
names), two or more dots (IP address), etc., whereas lexical look-ups are also
made to extract domain-specific information. Consequently, the Named Entity
Recognition (NER) annotator has been applied to the text to extract the
information which is not picked by the rule-base and lexical look-up
techniques [12].

The dependency relations between entities are extracted, using [11], to
analyse the actual cause of the ticket known as relationship. It gives key
summary and action elements against a ticket and filters the irrelevant
details. Mostly specific elements of the ticket describe the problem or the
actions taken.

####  III-A4 Topic Clustering

The topic Clustering algorithm is used to uncover the cause of the problem
from unstructured text data. It is the most important technique of the STA
that extracts key topics of the problem description and resolution. Several
algorithms have tried to come-up with an accurate topic clustering algorithm
including Vector Space Model (VSM) [4], Latent Semantic Indexing (LSI) [3] and
hierarchical N-gram clustering [5]. From different trails, VSM is found to be
a less accurate technique compared to LSI and N-gram clustering. The reason is
that the VSM is effected by the word usage diversity, which sometimes degrades
the performance severely [2]. On the other hand, LSI works well for auto-
generated logs, formulated in Equation 2, and N-gram clustering for manually
generated tickets.

$P(w_{j}\mid D_{i})=\sum_{T}k{P(w_{j}\mid T_{k})\,P(T_{k})}{P(D_{i}\mid
T_{k})}$ (2)

In Equation 2 the words $w$ of a document $D$ consist of a set of $K$ shared
latent topics $T_{1},...,T_{k}$ associated with the document-specific weights.
Each topic $T_{k}$ in-turn offers a bi-gram distribution for observing an
arbitrary word of the language. Top five most pre-dominant issues (topics),
extracted from unstructured problem descriptions of various datasets, are
listed in Table III.

TABLE III: Most pre-dominant issues extracted via Topic Clustering Dataset-1 | Dataset-2 | Dataset-3
---|---|---
Low server disk space | Unreachable network | Account locked
Backup Scheduler | Related to change | VPN issues
Services not running | Due to maintenance | Citrix session reset
Faulty printer | Job failed | Outlook issues
Password reset | High CPU utilization | Access issues

####  III-A5 Text Summarization

The unstructured text descriptions are typically very noisy and it is
difficult to identify the part of a text containing useful information. An
ontology-based approach has been implemented containing information, concepts
and actions that are relevant for classification. A Term-Document-Matrix (TDM)
has been generated on historical data that selects top noun and verb terms
with an aggregated frequency greater than 80%. The ontology repository has
been generated with selected terms that group concepts by category including
action, condition, entity, incident, negation, quantity and sentiment. These
groups are annotated with weight factors, POS type and phrase type
information.

The newly generated ticket tag words with ontology concepts, similar to NER
tagging and matching the POS phrase tags with corresponding ontology concept
annotations. The N-gram range of 2, 5, 10, or 15 words applied on moving
window over ticket description, summarize a number of matching ontology
concepts and their weights for each range. Finally, the sentences with the
highest density have been extracted to provide a summary that consists of
summary sentences, matching ontology concepts, Noun Phrases (NP) keywords and
NP + Verb Phrases (VP) keywords. Figure 4 illustrates the process of
extracting essential phrases.

Figure 4: Extraction of essential phrases using semantic mapping

####  III-A6 Sentiment Analysis

A Recursive Neural Tensor Network (RNTN) based sentiment model is used to
identify the sentiments of the whole sentence instead of words [13]. However,
the word-based sentiment analysis is usually carried out by defining a
sentiment dictionary which then builds a tree to compute the overall
sentiment. In the SD environment sentiment analysis is usually applied on CSat
surreys and Social Media snippets.

Figure 5 shows orthogonal dimensions that STA computes from unstructured
textual data. These dimensions are combined with their frequencies to find
useful insights.

Figure 5: An example how STA extracts useful information from unstructured
data

###  III-B Predictive Insights

There are several predictive use-cases applied to the raw structured
attributes and categorical insights produced by STA to classify, extrapolate
and segment important features. These predictive use-cases are explained in
the following sub-sections.

####  III-B1 Predict Service Level Agreement Violation

The SLA violation is considered as a critical predictive use-case for an SD
[1]. The service level is violated when the turnaround time of a ticket
exceeds the agreed duration. This delay may severely impact customer business,
degrade customer satisfaction, and sometimes the vendor has to compensate the
customer by paying a huge penalty in case of breaching the agreed SLA. The SLA
violations can be reduced by identifying high-risk tickets which can take more
time to solve than expected. Table IV shows accuracy numbers on different
datasets where the model is using both structured fields of raw data and
unstructured text analytics insights.

TABLE IV: Accuracy of various predictive use-cases Input | Use-cases (%)
---|---
Dataset | Type | Size | SLA | OAA
Dataset-1 | Incident | 267,493 | 86.41 | 79.32
Dataset-2 | Incident | 808,936 | 83.06 | 81.08
Dataset-3 | Incident | 447,740 | 86.00 | 77.73
Dataset-4 | Incident | 190,455 | 81.92 | 77.01
Dataset-5 | Incident | 65,892 | 79.03 | 75.56

####  III-B2 Optimal Agent Assignment (OAA)

This use-case determines the optimal agent-incident pairing to minimize the
meantime to resolution (MTTR). The optimal pairing is used to smartly route
incidents to the agents rather than using the traditional approach of ’first
come first serve’. It reduces the overall resolution time of an incident which
is explained in [14]. An overall average accuracy of 83% is recorded on four
different datasets.

####  III-B3 Mean Time Between Failures (MTBF) Prediction

The MTBF prediction use-case predicts the average time to failure of identical
category tickets which fall under identical application and operating
environments. It is the sequence of time of operation until the next failure
occurs, which impacts both reliability and availability. Reliability is
defined as the ability of SD to perform its required functions for a specified
period of time. On the other hand, availability is the degree to which SD is
operational and accessible.

####  III-B4 Propagation Prevention

Propagation Prevention assesses the incidents that are likely to propagate
future incidents and triggers that can be addressed to prevent such a
propagation. The problem description and resolution topic clusters form
associative rules which are used to determine the propagation trend among
incidents. The newly arrived incidents have been flagged for the possibility
of subsequent incidents where the resolution groups have been assigned to
these incidents which can be proactively used to prevent propagation.

###  III-C Interactive Exploration and Visualization Dash-boarding

IEVD provides a 360-degree view of service desk tickets that harnesses all of
the relevant pieces of information. It is an interactive visualization and
search dashboard that can simultaneously and securely search the contents of
multiple and heterogeneous datasets. IEVD also includes sophisticated
mechanisms for normalization, crawling, indexing, and subsequently exploring
the data [7].

IEVD is composed of three layers including Engine, Model and Presentation. The
data and corresponding insights from SD repositories are crawled to the Engine
layer. The Model layer creates relationships between different datasets to
provide 360 degree view of SD tickets. Finally, the presentation layer
provides an interactive exploration and visualization interface that allows to
visually prepare query and submit it to the Engine. The data that is once
crawled and indexed can be queried in real-time regardless of the volume and
number of dimensions of the data.

##  IV Applications

This system has been deployed to optimize the service desk operations of
several industries like banking, chemical, insurance, communication, retail,
automotive and others. In most cases, hundreds of thousands of tickets of type
incident, problem, change and SR were analyzed. Additionally, in a lot of
cases, CSat data was correlated to incidents and analyzed while social media
data was used for a couple of engagements. In all cases, the value ad of
unstructured data analytics was demonstrated via a straight forward comparison
to that of structured data analytics. It was shown how the most frequent
patterns in unstructured data revealed by the system could not be possibly
classified in the structured attributes of the ticket records.

In one of the engagement and for a predominant failure topic, unstructured
data analytics reported a much larger proportion of tickets in that category
than that of structured data analytics. In another one, statistical trends and
correlation analysis revealed insights for a predominant failure topic. In the
last example, a quite small proportion of incident tickets and yet quite
important was reported by external clients of an organization. These incidents
were backed by comments made by the external customers on social media blogs
and websites. These findings got the attention of the organization who took
action to resolve these issues. The first and the foremost important and
classical use-case for all accounts is the ticket volume reduction. It is
pretty straightforward to extract the most predominant failure topics and sub-
failure topics and their ticket counts. The recommendations are based on
resolution text and IT experience, such as process automation, increasing
email user notification and encouraging the use of self-service are made to
the clients in order to reduce the volume of tickets. The second use-case in
importance is the correlation of incidents that propagated due to erroneous
changes. In some cases, this was due to the lack of a robust test plan prior
to releasing the change into a production system. In other cases, the servers
on which the changes were to be made were brought down during peak work hours
thereby causing users not to be able to do their work on those servers and
incident creation then followed.

The other use-cases include SLA target violation and MTTR analysis. As found
in the system, extensive MTTRs are often due to tickets not being transferred
to the proper resolver group or not to the proper agent within the resolver
group.

##  V Results

There are different experiments prepared to rigorously test different
techniques of the STA on real datasets. These techniques are first
independently evaluated and then as a single integrated system whose objective
is to reduce the total ticket volume. For an instance, the techniques that are
used for text cleansing and feature extraction have a dependency on the
accuracy of topic clusters.

The language identification technique yields 100% accuracy for 8 out of 11
languages mentioned in Table I. The errors in Korean and Chinese languages are
due to overlapping of alphabets. However, the system is more biased towards
European languages. If more than 20% of Asian language input contains English,
the system decodes it as the European language.

Figure 6: POS Based Ticket Feature Extraction

The machine translation of 10 different languages into English is listed in
Table II. The non-English SD problem description and resolution text have been
translated into English where the coverage and weighted coverage measures are
used to evaluate the system. The results show consistently better performance
on European and Latin-American languages as compared to the Asian languages.

TABLE V: Topic Clustering coverage and weighted coverage Input | Description (%) | Resolution (%)
---|---|---
Dataset | Type | Size | Coverage | Coverage
Dataset-1 | Incident | 267,493 | 67.36 | 74.60
Dataset-2 | SR | 173,368 | 70.64 | 77.78
Dataset-3 | Incident | 808,936 | 92.84 | 61.97
Dataset-4 | Incident | 447,740 | 72.86 | 75.68
Dataset-5 | Incident | 65,892 | 63.49 | 81.90

Topic clustering is found to be one of the most challenging and critical
techniques of STA. There are several experiments designed to test the best
topic clustering technique that can persistently give the best results on a
different dataset. Apart from the selection of appropriate algorithm,
different pre-processing techniques have been tested as it is highly dependent
on how the input data has been cleansed. The two key techniques which are used
to clean the data include sentiment analysis and part-of-speech (PoS) phrases.
The idea is to extract negative and noun-verb phrases from noisy data using
sentiment analysis and PoS respectively. The sentiment analysis based sentence
filtering cleansing technique reduced the overall clustering accuracy and
coverage. However, PoS phrases extraction, as a pre-processing technique,
helps in boosting topic clustering quality in most of the cases where it
provides a key phrase describing problem symptoms and actions taken. As the
phrases are getting small, clustering results are showing marginal
improvement. The results of the cluster evaluation are shown in Figure 6. The
overall topic clustering results are summarized in Table V for both problem
description and resolution.

Text Summarization becomes very useful for both summary sentences and NP-VP
phrases. In a few trails, manually labeled test-set have been used to
calculate cosine similarity for various ontology concept weighing factors and
N-gram ranges using ’moving window size’. The N-gram range of 15 words has the
highest similarity to manually labeled sentences in the test-set which can be
visualize in Figure 7.

Figure 7: Cosine similarity quantiles for various ontology concepts of Text
Summerization

##  VI Conclusion

The SD ticket dispatch system plays a vital role to provide persistent IT
services. The proposed system provides a sequential multi-component semantic
analysis platform that combines the insights of a number of text and
predictive analytics techniques to accurately solve a complex task. This
platform extracts information from both unstructured and structured data for
deep analysis and interactive visualization. The three-layer methodology is
found to be very effective in which step-by-step extracts complex information
from a layer where insights computed by all previous layers become its input
to get richer insights. The usage of various NLP techniques on a ticket
provides orthogonal dimensions of problem areas which eventually addresses a
few key challenges. These challenges include the extraction of ambiguous
problem areas of the tickets and low coverage and accuracy of individual
techniques. Many organizations are using this system and they have observed
improvements not only in their SD operations but in the stability of their IT
infrastructures as well. All the text and predictive analytics techniques that
are part of this system are giving significantly higher and consistent
accuracy on different types of cross-domain noisy datasets. There is currently
on-going work to enhance this system by making its data processing even faster
and incorporating new algorithms for a time-based correlation between two or
more related data sets.

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