Assessing Expertise Awareness in Resolution Networks

Yi Chen1, Shu Tao2, Xifeng Yan3, Nikos Anerousis2, Qihong Shao1

1Computer Science and Engineering, Arizona State UniversityTempe, AZ 85287, USA

2IBM T. J. Watson Research CenterHawthorne, NY 10532, USA

3 Computer Science Department, University of CaliforniaSanta Barbara, CA 93106, USA

Abstract

Problem resolution is a key issue in the IT serviceindustry. A large service provider handles, on dailybasis, thousands of tickets that report various types ofproblems from its customers. The efficiency of this pro-cess highly depends on the effective interactions amongvarious expert groups, in search of the resolver tothe reported problem. In fact, ticket transfer decisionsreflect the expertise awareness between groups, thusencoding a sophisticated resolution social network.

In this paper, we propose a computational frame-work to quantitatively assess expertise awareness, i.e.,how well a group knows the expertise of others.An accurate assessment of expertise awareness couldidentify the weakest components in a resolution system.The framework, built on our previously developed reso-lution engine, is able to calculate the performance dif-ference caused by excluding a node from the network.The difference exposes the awareness of this node toother nodes in the network. To our best knowledge, thisis the first study on this problem from a computationalperspective. We tested the proposed framework on alarge set of real-world problem tickets and validatedour discovery by carefully analyzing the tickets that areincorrectly transferred. Experimental results show thatour framework can successfully capture groups that donot know others’ expertise very well.

1. Introduction

Problem solving is a key issue in IT service in-dustry. Every day, help desks or call centers of ser-vice providers such as IBM, AT&T, and Citi, mayreceive thousands of phone calls and emails from theircustomers who are seeking technical supports. The

ID Time Entry28120 07-05-14 New Ticket: DB2 failure28120 07-05-14 Transferred to Group A28120 07-05-14 Contacted Mary...28120 07-05-14 Transferred to Group C28120 07-05-14 Status updated ...28120 07-05-15 Transferred to Group E28120 07-05-15 Web service checking28120 07-05-18 Can not solve the problem.... ... ...28120 07-05-18 Transferred to Group F28120 07-05-22 Resolved

Table 1. A sample ticket

reported problems range widely from login failure,application crash, to broken transactions. The problemresolution process is reflected by the life cycle ofproblem tickets. A ticket is opened as soon as aproblem is reported. It is routed among various expertgroups until it reaches a group that can solve theproblem and close the ticket. Table 1 shows a sampleticket transferred multiple times before it was solved.〈A, C, E, F〉 forms its resolution sequence.

As one can see, the efficiency of problem solvingdepends on two key factors: (i) the capability of eachgroup to solve a problem in its technical area, and (ii)its awareness of the expertise of other groups in orderto make a wise decision to transfer an unsolved ticket.Indeed, the efficiency of problem resolution criticallyhinges on the efficiency of identifying the problemresolver. It is not uncommon that due to human erroror inexperience, a ticket is mistakenly transferred to agroup who cannot solve the problem or who cannotdistribute it properly. Both situations might lead to along resolution sequence.

In order to accelerate problem resolution, providing

2010 International Conference on Advances in Social Networks Analysis and Mining

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DOI 10.1109/ASONAM.2010.82

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2010 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4138-9/10 $26.00 © 2010 IEEE

DOI 10.1109/ASONAM.2010.82

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2010 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4138-9/10 $26.00 © 2010 IEEE

DOI 10.1109/ASONAM.2010.82

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better expertise training and awareness training is thekey. Expertise training will increase the capability ofsolving a ticket in each group, while awareness trainingwill improve the probability of transferring tickets tothe right groups. Since it is time-consuming and costlyto provide training to all of the groups in the system, itis very important to identify the weakest components inthe resolution network. If such information is available,training programs could focus on specific employeesto reduce the ticket response time.

While it is easy to calculate the total tickets solvedby one group, it is not easy to evaluate the expertiseawareness due to the presence of ambiguity. Tradi-tionally group awareness is only attempted by HRrepresentatives or managers using survey, peer reviews,and questionnaires, e.g., using questions like “do youknow the skill of group A?”, “How well do you knowA’s skill level, choose from 1 to 5”, etc. A surveyprocess is often tedious, time-consuming, and error-prone. It is also very subjective.

In this paper, we approach this problem from aunique angle: we develop a computational frameworkto objectively and quantitatively assess the awarenessof expert groups in problem resolution, without socialexperiments. This provides a cost-effective alternativeto user surveys. Resolution social networks are built tomodel problem solving, where nodes represent expertgroups, and edges represent ticket transfer relationshipsbetween groups. The transfer relationship actually im-plies the expertise awareness between two groups.

Ticket

Figure 1. Resolution Social Network

Figure 1 is a sample resolution social network. Therelationships in this network are more complicated thantraditional social networks studied in social science.For example, expert groups in resolution network maynot describe clearly how well they communicate witheach other or how much they know others’ skills.

Instead, we have to discover the level of expertiseawareness by ourselves. This knowledge discovery taskis also different from existing social network analysisworks, which often focus on topological measures suchas degree, closeness, and betweenness [15], [7], [19],[13], [11], to evaluate the importance of a node.

We develop an exclusive framework to evaluate thevalue of a node E in a resolution social network. Byremoving E from a network M , we are able to com-pare the state difference with versus without E. Thisdifference could tell us the value or the importance ofE in this network. In this framework, there are severalchallenging issues. First, should we conduct real-lifeexperiments and examine the effects of removing agroup in real ticket resolution? This could lead toprohibitive cost. Second, is it possible to simulate suchremovals virtually? Third, given a resolution network,how should we measure the change before and after theremoval of a node? Is the change significant? In thisstudy, we investigate all these issues and solve theminnovatively using a virtual ticket resolution engine.

The significance of our work has two folds. It cannot only improve problem ticket resolution, but alsoprovide insights on human interaction in a workingenvironment. By providing appropriate training, weakgroups are able to work and communicate more effec-tively. The technical contributions of this work include:

• We propose an objective and quantitative frame-work to analyze the interaction of expert groupsin a ticket resolution network, and evaluate theirawareness of others’ expertise. To our best knowl-edge, this is the first study on this problem froma computational perspective.

• Ticket data from an IT company was experi-mented. Case studies show that our algorithm cansuccessfully capture groups with low productivityin a resolution social network.

The remainder of the paper is organized as follows.We first explain the intuition of our exclusive frame-work in Section 2, followed by a brief introduction onour virtual resolution engine in Section 3. We proposethe expertise awareness model in Section 4. In Section5, we use real world examples to illustrate and verifythe discovery of the proposed approach. Related workis presented in Section 6 and Section 7 concludes thepaper.

2. An Exclusive Framework

A problem ticket can be represented by a tu-ple with two components, (τ,G(k)), where τ is theticket content and G(k) is the routing sequence. Let

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G = {g1, g2, . . . , gn} be the set of all expert groups.The routing sequence of a ticket can be written asG(k) = 〈g(1), g(2), . . . , g(k)〉 (g(i) ∈ G), in which aticket is first issued to g(1), then transferred in theorder of g(2), g(3), . . . , g(k). A step in G(k) is a tickettransfer from one group to another. A ticket (τ,G(k))is open if none of the groups in G(k) can resolve it.Correspondingly, a ticket is closed if the last group inG(k), i.e., g(k), solved the problem, and in this case,the routing sequence is called a resolution sequence.The efficiency of problem resolution can be measuredby the Mean number of Steps To Resolve (MSTR) form tickets:

T =

∑mj=1 |Gj |

m. (1)

The resolution sequences capture the interactionor expertise awareness among different groups. Intu-itively, different expert groups may play different rolesin resolving a problem. A group with certain expertisecan serve as the resolver of a problem ticket. Somegroups serve as transferrer of problem tickets, whorelay the unsolved tickets to other groups. Most ofgroups usually play these two roles at the same time.For those groups who only serve as transferrer, theyare called distributors. Distributors do not resolve theproblems, but are considered to have good knowledgeabout the expertise of other groups.

Given a collection of ticket resolution sequences, itis easy to calculate the solving capability of a group,e.g., using the number of tickets resolved by the group.However, it becomes complicated and difficult whenone wants to evaluate the transfer effectiveness of agroup. When a ticket is transferred from group g(i) tog(j), there are three cases.

Case 1. g(j) is the resolver, i.e., g(i) knows that g(j) isable to answer it correctly, written as g(i) �g(j),

Case 2. g(j) knows how to route the ticket to a rightgroup, i.e., g(i) knows that g(j) is able totransfer it correctly, written as g(i) �→ g(j),

Case 3. g(j) is neither a resolver nor the right group totransfer, g(i) mistakenly sent the ticket to g(j).That is, g(i) is not aware that g(j) is unableto handle this ticket, written as g(i) � g(j).

For a given ticket, it seems difficult to distinguishCase 2 and Case 3 if g(j) is not a resolver. Forexample, suppose we have a ticket resolution sequence,A → B → C → D. It is hard to distinguish ifA � B or A �→ B �→ C, etc. Because of thisambiguity, it is hard to assess the effectiveness ofa transfer (we use expertise awareness and transfereffectiveness exchangeably) in one ticket. However,

for a set of tickets, it becomes possible. This paperwill introduce an exclusive framework designed for thistask. The rest of this section is going to briefly explainthe main idea behind this framework for evaluatingtransfer effectiveness.

The issue of inefficient ticket solving arises from thelack of knowledge on transferring a ticket to the rightgroup. For example, in Figure 1, group D might havelimited knowledge of transferring unsolved tickets toother groups: it always passes unsolved tickets to twogroups.

A fundamental question is how to evaluate the“transfer effectiveness” of D to other groups. Assumethere is a set of tickets G, in which D is involvedas a transferrer and its average MSTR is TG. Foreach ticket, one could resubmit it to the system andresolve it without involving D, i.e., D is excludedfrom the processing of these tickets. Let G′ be the newresolution sequences generated by this process. SinceD is eliminated, G′ could be significantly differentfrom G. We can conclude as follows,

If TG′ < TG, then D is not an effective transferrer;or D is not aware of other groups’ expertise.

Ticket

Figure 2. Transfer Effectiveness

Figure 2 illustrates the above idea and tests thesystem performance by removing D from the reso-lution social network, for those tickets where D is atransferrer.

The methodology of our framework has beenadopted in experimental science: in order to evaluatean element E’s impact to the whole system, one couldknock out E, and then compare the state of the systemin the presence of E with that in the absence of E: Thedelta reflects the value of E. Adopting this method ina resolution social network, we can evaluate the valueof an element (e.g., an expert group, or an interactionedge) by excluding it from the network.

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In this framework, we need to calculate the newMSTR based on a modified social network that ex-cludes a node or an edge. Unfortunately, it is imprac-tical to re-execute the previous tickets in a servicecenter by excluding groups or prohibiting interactions.Rather than modifying a real-world social network, webuild a virtual network (a ticket routing engine, calledVMS, see Section 3) for experiments. This enginecould automatically generate a resolution sequence fordifferent tickets.

3. A Ticket Routing Engine

In this section, we will briefly introduce an auto-matic ticket routing engine, called VMS (Variable-order Multiple active State Search). More details of thisapplicable model can be found in [14]. The problem ofticket routing recommendation is specified as follows:

Input: A database of historical ticket resolutionsequences DS and an open problem ticket (τj , Gj).

Output: The next group that the open ticket shouldbe transferred to.

Objective: To reduce MSTR (Mean number of StepsTo Resolve) as much as possible.

3.1. Markov Model

Our method is based on a Markov model. Leteach Markov state represents a group, the transitionprobabilities between these states capture the localrouting decisions, i.e. the likelihood of a group to bea transfer target, given the previous groups that haveprocessed the ticket. To build the Markov model, wefirst extract routing sequences from problem tickets,e.g., sequence 〈A, C, E, F〉 from the sample ticket inTable 1. Recall that a process is considered Markovianif at any time point, the probability of the process inthe current state is solely dependent on its previousstates. Let us use S(k) to denote the set of groups inG(k), i.e. S(k) = {g(1), g(2), ..., g(k)}. The number ofhistorical tickets that share the same set of transfergroups S(k) is denoted as N(S(k)). We can estimatethe probability of transferring a ticket to gi given a setof previous transfers S(k), P (gi|S(k)) by

P (gi|S(k)) =

{N({gi} ∪ S(k))/N(S(k)) if N(S(k)) > 0,0 otherwise.

(2)

To determine the “optimal” order of a Markovmodel, we consider the conditional entropy of thetraining data and evaluate the entropy of the next group

g conditioned on a given set of past groups S(k), whichis denoted as H(g|S(k)):

H(g|S(k)) = −∑

S(k)∈Gk

P (S(k))∑g∈G

P (g|S(k)) log P (g|S(k)).

(3)

Although increasing Markov order generally leads tobetter predictability, it also increases the complexity ofour model, especially when we apply it to online tickettransfer recommendations. Therefore, users can set athreshold θ to determine the optimal order k, where kis set as the smallest value that satisfies

H(g|S(k)) − H(g|S(k+1)) < θ. (4)

3.2. Routing Recommendation

Our resolution network captures the likelihood thata ticket would be transferred to a group, given thepast group transfer information. Then it makes ticketrouting recommendations based on the majority of thedecisions made in the past. Note that the right resolvergroup for a ticket is unknown at the beginning ofticket routing. What we know is the initial group that aproblem ticket was assigned by the help desk accordingto the reported problem symptom.

To make effective ticket routing recommendations,we use the resolution network to guide the ticketrouting. Starting from a first order model, we canmake a decision solely based on the current group,at each step, choosing the next node with the highesttransition probability H(g|S(k)). Sometimes incorrect“local” decisions may cause unsuccessful transfers. Toovercome this problem, we choose the next groupbased on the transition probabilities from one of thepast states (called multiple active state), i.e., a subsetof S(k).

We introduce a heuristic search algorithm, Variable-order Multiple active state Search (VMS). Here, weassume a ticket should not visit the same group twice.VMS considers a variable order of Markov modelssimultaneously (i.e. a variable length of past transferpatterns are considered), as well as multiple activestates to advance the routing sequence.

In the following sections, we will use the VMSalgorithm as a virtual ticket routing engine to simulatethe resolution network. Note that VMS is not necessar-ily a perfect routing recommendation system, as bothcorrect and wrong routing decisions are modeled inVMS and subsequently are used in routing decisionmaking. However, it indeed provides a simulation ofthe real-life resolution network, which is valuable

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to our exclusive framework for assessing expertiseawareness. We can easily knock out a node gi in VMSby modifying its transfer probability P (gi|S(k)) as 0for all S(k). It is beneficial to improve the quality ofVMS by taking the ticket content into consideration,which is currently in development. Certainly, a high-quality automatic routing engine will make the analysisresult more reliable.

4. Transferrer Role Analysis

In this section, we discuss how to evaluate “transfereffectiveness” of an expert group in a resolution net-work. In order to assess the transfer effectiveness ofan expert group gx, we compare the performance ofticket routing in the presence of gx with the routingperformance in the absence of gx. The performancedifference implies the transfer effectiveness of gx.

The Exclude algorithm is presented in algorithm1. First, we construct a resolution social network Musing all routing sequences in the historical tickets,according to the technique discussed in Section 3.1.Then we simulate the effect of removing group gx

in the resolution network by constructing a virtualsocial network M ′ using routing sequences excludinggx. Note that we focus on the analysis of transfereffectiveness of a group, not the technical expertise.Hence we only consider the sequences where gx is notthe resolution group. At last, we compare the MSTRdifference of M and M ′. The detail steps are describedas below.

4.1. Virtual Resolution Network Construction

To simulate the resolution network in the absenceof an expert group gx, we construct model M ′ us-ing all available information in the historical routingsequences except the one related gx. For each ticketresolution sequence G(k) that contains gx, we separateit into two segments: the first segment is from thebeginning of G(k) to the group right before gx, andthe second segment is from the group right after togx to the end of the sequence. For example, givena ticket sequence G(k) = A → B → X → C →D, when considering X’s separation, we break it toG(kp) = A → B and G(kf ) = C → D. The originalsequence G(k) is then replaced with G(kp) and G(kf ).By doing so, we eliminate the influence of X .

After processing all the routing sequences that in-volve gx, we build up a virtual model M ′ with themethod proposed in Section 3.1, which represents theresolution network in the absence of group gx.

Algorithm 1 Exclude gx

1: Construct M with all the sequences ;2: for resolution sequence G(k) do3: Separate G(k) into G(kp) and G(kf ) ;4: Replace G(k) with G(kp) and G(kf ) ;5: end for6: Construct M ′ by G(k) (Section 3.1);7: for problem j which contains group gx and starts

with gs do8: if gx �= gs and gx is not the resolver then9: step(M ′(gs)) = VMS(gs, M ′);

10: step(M(gs)) = VMS(gs, M );11: calculate improvement ratio Imp(j) using

Eq.7;12: end if13: end for14: Significant Test for all (step(M(gs)),step(M ′(gs)))

in participate sets PS(gx,M ) and PS(gx,M ′) ;15: if M and M ′ are significantly different then16: Ranking(Imp(gs));17: end if

4.2. Significance Test

To determine whether model M ′ (in absence of anexpert group gx) has significant difference with modelM (in the presence of gx) , we adopt standard T-test.

Only the routing sequences that have expert groupgx involved would have a different routing sequencein the absence of gx, and only those sequences canprovide useful information to evaluate the transfereffectiveness of gx. Hence, to evaluate gx, we only con-sider the resolution sequences that contain gx when ap-ply model M , referred as participate set, PS(gx,M)= {Gj}(j = 1, ..., n). We use VMS algorithm tosimulate the mostly likely routing decisions, to getPS(gx,M). Similarly, for virtual model M ′, we canget its participate set PS(gx,M ′) which is the setof resolution sequences contain gx. For each problemticket in PS(gx,M) with initial group gs, we applyVMS to get the number of routing steps, representedas step(M(gs)), and the number of routing stepsusing model M ′, as step(M ′(gs)). Considering allthe instances with step(M(gs)) and step(M ′(gs)) inPS(gx,M) and PS(gx,M ′), we compare the differ-ence between these two models. We use X to repre-sent the distribution of model M on PS(gx,M), whereX ∼ N(μ1, σ

21), and Y to represent the distribution

of M ′ on PS(gx,M ′) where Y ∼ N(μ2, σ22), and

μ1, μ2, σ21 , σ2

2 are unknown variables. We first make ahypothesis: the means of two normally distributed pop-ulations are equal, which is H0 : μ1 = μ2. PS(gx,M)

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and PS(gx,M ′) can be treated as a sample from thewhole space, and the number of independent instancesof X is m which is equal to the size of PS(gx,M),and the number of independent instances of Y is nwhich is equal to the size of PS(gx,M ′). We recordtheir means and variances as following:

X =1m

m∑i=1

Xi, Y =1n

n∑i=1

Yi,

S21m =

1m

m∑i=1

(Xi − X)2, S22n =

1n

n∑i=1

(Yi − Y )2.

The t statistic can be calculated as follows:

t =X − Y√

mS21m + nS2

2n

√mn(m + n − 2)

m + n, (5)

where s21m and s2

2n are unbiased estimators of thevariances of two sets. Compare the calculated t-valuewith the threshold chosen for statistical significance α(usually α is 0.10, 0.05, or 0.01 level). If |t| ≤ tα

2,

then the hypothesis that the two models do not differis accepted. Intuitively, if the change is not significant,then the presence or the absence of gx has limitedinfluence to the resolution network M , and thus haslow significant impact to problem solving. Otherwise(|t| > tα

2), the hypothesis that two models do not differ

is rejected. That is to say, the result is in favor ofan alternative hypothesis, which typically states thattwo models do differ, and thus gx is likely to play animportant social role in the resolution network and inproblem solving.

4.3. Positive or Negative Influence

After we differentiate groups with significant impactfrom the ones with insignificant ones using hypothe-sis test, the next question is how we can determinewhether the impact is positive or negative.

Given an instance j in PS(gx,M ′), the ratio ofthe routing steps required for j using model M overthe ones using model M ′, represented as Impj , iscalculated as following:

Impj =step(M(gs)) − step(M ′(gs))

step(M(gs)). (6)

The average changes to the routing steps to thewhole participate set is

Imp(gx) =

∑mj=1 Impj

|PS(gx,M ′)| . (7)

gx MSTR(M) MSTR(M’) Imp(gx)AUSFS 4.18 2.65 15.1%UDCDS 4.16 2.846 7.89%UDBTVAIXW 3.9 2.9076 7.45%AIXCRM 3.27 2.465 6.45%AUSAIX 3.43 2.333 3.94%DSMOP 4.14 2 3.1%

Table 2. Role Effectiveness Results

If an expert group gx has a positive Imp(gx) value,it indicates that the routing sequences are shortened inthe absence of gx. This indicates a negative impact ofgx in ticket routing. On the other hand, if an expertgroup gx has a negative Imp(gx), then gx plays apositive role in the ticket transfer. The higher absolutevalue of Imp(gx), the larger impact gx has.

Our Exclude algorithm can provide a better under-standing of the transferrer role of expert groups. Forthe groups that play an ineffective transferrer role andhave limited contributions to the resolution network, itis important to target training programs to these groupsto improve their expertise awareness of other groups.

5. Experiments

In this section, we report empirical studies of ourapproach on problem tickets collected from IBM’sproblem management system over a 1-year period fromJan 1, 2006 to Dec 31, 2006. These tickets were clas-sified into 553 problem categories 1, e.g., AIX, DB2,Windows, etc. On average, 50 − 1, 000 groups wereinvolved in solving tickets of each problem category.

In our study, we partition the data into trainingand testing sets for each problem category. Using thetraining set, we build a ticket routing engine presentedin Section 3. After that, we analyze the transferrer roleson the testing dataset. We randomly choose the AIXproblem category for case study.

For all the expert groups in AIX problem category,we first use the method described in Section 4 to dosignificance tests. For the groups whose absence causessignificant changes to the model, we further investigatetheir impact to the ticket routing efficiency measuredby the improvement ratio. Table 2 presents the top6 groups with highest improvement ratios. Since theremoval of such groups can result in shorter routingsequences, these are the groups whose ticket routingskills need to be trained.

As we can see, the expert group with the biggestimprovement ratio is group AUSFS. To better under-

1. When a ticket is first opened, the helpdesk assigns a parameterthat indicates which problem domain it falls into.

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stand the result and validate our method, we checkthe tickets that AUSFS involves. We find that thereare multiple similar tickets about disk operation errorshandled by AUSFS. In Table 3, we show a sampleticket that involves AUSFS: after AUSFS received theticket, it did not provide any useful problem diagnosis,but simply passed the unsolved ticket to other groupsseeking for solution. It implies that AUSFS didn’tpositively contribute to problem solving of this ticket,and its absence in the model may shorten the routingsequences. This case study shows that the groupsevaluated with an ineffective transferrer role in theresolution network should receive awareness training.

6. Related Work

Social network analysis (SNA) is the study ofmathematical models for interactions among people,organizations and groups. Historically, research in thefield has been led by social scientists and physicists[7], [17], [1], [19], [11]. With the popularity of Web2.0 websites, social network analysis has attracted alot of computer scientists’ attention.

Social Role Analysis The use of social networksto discover “roles” for the people (or nodes) in thenetwork goes back over three decades to the work ofLorrain and White [7]. It is based on the hypothesisthat nodes on a network that relate to other nodes in“equivalent” ways must have the same role. Socialnetwork analysis is also introduced to identify im-portant components or properties of network systems,such as the most striking one, the small world phe-nomenon [18] and the power-law degree distribution[3]. Analyzing network properties is also used foridentifying authorities and search algorithms [2], [4],and discovering the network value of customers [12].In element level analysis, several different centralitymetrics such as degree, betweeness, clustering coeffi-cient, and PageRank metrics have been proposed asgraph theoretic metrics are capable of identifying thenodes’ roles [15], e.g., finding particular nodes withan inordinately high number of connections, e.g., [7],[19]. Using these properties, roles are assigned tonodes, and role analysis becomes a well-studied topicin the social network research [13], [11]. However, it isclear that network properties are not enough to discoveran expert’s role effectiveness in a social network.

In this paper, instead of performing structural anal-ysis, we analyze role effectiveness of expert groupsfrom a functional perspective in the context of problemresolution.

Social Relationship The evaluation of social re-

lationships mostly relies on sociologists [8] as wellas computer scientists from the research area of hu-man computer interaction. A definition that catchesthe essence of awareness in a broad way is the onesuggested by Dourish et al. [6] where awareness isdefined as “the understanding of the activity of theothers, which provides a context of your own activity”.Kraut et al. [9] show that geographical proximity isfundamental for the development of personal rela-tions and communication. This includes first of allthe knowledge of persons availability, both physicaland emotional. Another aspect is that understandinghow members’ knowledge is used in a group. Somestudies [10] claim that groups tend to be organizedin knowledge networks where people relate to theknowledge of others. Hence, providing informationabout that knowledge is important, as it will increasethe potential of collaboration within a group. [16]describes a system that provides computer supportto bridge the gaps between people. The system canenhance the awareness and consciousness among labmembers.

In this paper, we develop a novel framework to ana-lyze the interaction of expert groups, and evaluate theeffectiveness of their communications in a resolutionsocial network.

7. CONCLUSIONS

In this paper, we propose an innovative framework toevaluate expertise awareness among different groups ina resolution social network in the context of problemsolving. We explore an exclusive model to measurethe knock-out values of expert groups. The proposedframework is evaluated on a large set of real-worldproblem tickets, which validate that our algorithmcan successfully capture the weakest components ina resolution network. In future, we will explore howto analyze the relationship among experts using theexclusive model, extending our preliminary study [5].We are also interested in studying how to optimize theautomatic ticket routing engine to further improve thequality of the patterns discovered in this study.

Acknowledgment

This material is based on work partially supportedby NSF IIS-0915438 and IIS-0917228.

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Entry DescriptionNew Ticket 29509068 disk operation error: This critical AIX_HD_ERROR was received

by TEC serverTransferred to Group DSMOP transfer to AUSFS for supportTransferred to Group AUSFS RTPDSMOP transferred ticket to supportTransferred to Group WEDDELL Resolution: pdisk has not been rejected from RAID, closing PR.

Table 3. Sample Ticket (Ineffective Transferrer: AUSFS)

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