casebook: a cloud-based system of engagement for case management

30 Published by the IEEE Computer Society 1089-7801/13/$31.00 © 2013 IEEE IEEE INTERNET COMPUTING

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Casebook: A Cloud-Based System of Engagement for Case Management

Hamid R. Motahari-Nezhad, Susan Spence, Claudio Bartolini, Sven Graupner, Charles Bess, Marianne Hickey, Parag Joshi, Roberto Mirizzi, Kivanc Ozonat, and Maher Rahmouni Hewlett-Packard Labs

Casebook embraces social and collaboration technology, analytics, and

intelligence to advance the state of the art in case management from systems

of record to a system of engagement for knowledge workers. It addresses

complex, inefficient work practices, information loss during hand offs between

teams, and failure to learn from previous case experience. Intelligent agents

help people adapt to changing work practices by tracking process evolution

and providing updates and recommendations. Social collaboration surrounding

cases integrates communication with information and supports collaborative

roadmapping to enable people to work as they collaborate, thus accelerating

how quickly and accurately they handle cases.

I n case management, people with dif-ferent areas of expertise work together to handle a case.1 Such cases cover

a wide range of application domains, including healthcare, insurance claims processing, product warranty claims, IT management, the legal domain, or sales pursuit management.1,2 Knowledge work-ers commonly form global virtual teams that work collaboratively to manage these cases, which requires knowledge-intensive human judgment and decision making. Although advances in collabora-tion and communication technology have facilitated workers’ interaction, the main management burden is still on knowl-edge workers because collaboration and communication tools are unaware of work

context. Recently, tool vendors in busi-ness process (BPM), enterprise content, and customer relationship management have tailored their solutions to fit specific case-management domains,2–4 but the new push toward adaptive case manage-ment aims to bring flexibility, adaptabil-ity, and responsiveness to the practice (see the “Related Work in Case Management” sidebar for more on such research).1

The state of the art in case-manage-ment technology today is represented by traditional enterprise-resource-planning (ERP)-like systems of record that rely on people maintaining consistent infor-mation, using disparate applications, and manually tracking information related to a case across different systems.

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SEPTEMBER/OCTOBER 2013 31

Consequently, substantial, case-related infor-mation lives outside case-management applica-tions and is isolated and fragmented. Often, this information is archived in knowledge workers’ personal inboxes rather than shared within the organization. This results in complex and ineffi-cient work practices because knowledge workers and people-driven processes lack systematic sup-port, and organizations lack good ways to capture and share knowledge effectively. Consequently, organizations fail to learn from previous case experience and struggle with information loss during handoffs between individuals and teams.

Any solution for adaptive case management should center on cases and on supporting knowledge workers in driving work. It should embrace advances in social and collabora-tion technology, analytics, and intelligence to advance the state of the art in case management from systems of record to systems of engage-ment. In this future state, people would drive the work by actively engaging and interact-ing with others and with information entities in their work environment. We envision semi-autonomous systems that will offer intelligent

and automated support to workers to free them from record keeping and guide them in case handling. Such a system should be adaptive to changing work practices, keeping workers informed about the latest updates and sharing the right information with the right people at the right time to foster collaboration among colleagues, partners, and customers.

Aligned with this vision is Casebook, our cloud-based system of engagement for knowl-edge workers. Casebook addresses the quality, consistency, and efficiency issues in existing case-management systems by automatically capturing and codifying flexible processes, case-related information, and their evolution. Case-book is architected as a cloud-based service to support case management in cross-organiza-tional and cross-company settings. It enables worker engagement by offering social collabora-tion around cases as a focal point for business value generation and interaction. It integrates information flow — usually captured by commu-nication and collaboration tools — with infor-mation about cases, backed with process-aware components that let knowledge workers enact

Related Work in Case Management

Case management is an active topic in research as well as in practice today.1–3 Academia has studied it in various

contexts, including healthcare, IT management, and the public sector.1,3 Related to this are efforts in social business process management (BPM), where the focus has been on introducing modeling frameworks for including social abstraction in process models4 or making the process definition process more collab-orative and social (www.signavio.com). The BPM community presents several examples of work on adaptive business pro-cesses and supporting workers in an environment with a base process definition, defined a priori, but it might not be detailed and can change on the fly.5–7 In addition, one application offers process mining techniques to identify recurring patterns of work in case-management scenarios and analytics that predict likely paths and duration for a case based on previous similar cases.8 Realizing the opportunity in the adaptive case-manage-ment space, industry vendors are pushing toward enhancing and positioning their customer relationship management, BPM, and content-management products as case-management solutions.9

References1. K.D. Swenson et al., Taming the Unpredictable: Real World Adaptive

Case Management: Case Studies and Practical Guidance, 1st ed., Future

Strategies, 2011.

2. “Case Management: Combining Knowledge with Process,” BP Trends, July

2009; www.bptrends.com/publicationfiles/07-09-WP-CaseMgt-Combining-

KnowledgeProcess-White.doc-final.pdf.

3. W.M.P. van der Aalst, M. Weske, and D. Grünbauer, “Case Handling: A New

Paradigm for Business Process Support,” Data Knowledge Eng., vol. 53, no. 2,

2005, pp. 129–162.

4. P. Fraternali, M. Brambilla, and C. Vaca, “A Model-Driven Approach to

Social BPM Applications,” Social BPM, L. Fischer, ed., Future Strategies, 2011.

5. M. Adams et al., “Worklets: A Service-Oriented Implementation of

Dynamic Flexibility in Workflows,” On the Move to Meaningful Internet

Systems 2006: CoopIS, DOA, GADA, and ODBASE, LNCS 4275, Springer, 2006,

pp. 291–308.

6. V. Liptchinsky et al., “A Novel Approach to Modeling Context-Aware and

Social Collaboration Processes,” Proc. 24th Int’l Conf. Advanced Information

Systems Eng., Springer, 2012, pp. 565–580.

7. M. Reichert et al., “ADEPT Workflow Management System: Flexible Sup-

port for Enterprise-Wide Business Processes,” Proc. 2003 Int’l Conf. Business

Process Management, Springer, 2003, pp. 370–379.

8. A. Martens et al., “Advanced Case Management Enabled by Business

Provenance,” Proc. 2012 IEEE 19th Int’l Conf. Web Services, IEEE CS, 2012,

pp. 639–641.

9. H. Motahari-Nezhad and K. Swanson, “Adaptive Case Management: Over-

view and Research Challenges,” Proc. 15th IEEE Conf. Business Informatics

(CBI 13), to appear, 2013.

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Dynamic Collective Work

32 www.computer.org/internet/ IEEE INTERNET COMPUTING

people-driven processes in a plan-as-you-do manner. Casebook also offers roadmapping as a shared workspace for case workers with per-sonalized views for each worker, advanced ana-lytics to recommend experts, and suggestions for next actions determined from templates learned from past cases. It includes intelligent agents for analyzing communication among knowledge workers to support process automa-tion, group deliberation, and incentive schemes to improve how quickly and accurately knowl-edge workers handle cases. Casebook brings together components that support these capa-bilities; we’ve implemented these components in research prototypes and validated them in the context of case-management applications inside Hewlett-Packard.

OverviewCasebook’s main capabilities include case plan-ning, execution and assistance, measuring and learning, and a community-curated case catalog.

The case planning module lets case workers create a roadmap with goals, decision points, and milestones using a Web-based graphical editor. The roadmap is a medium for defin-ing, tracking, and communicating the major activities and events in a case’s life cycle, as seen from the case manager’s vantage point, with shared views for all workers. The case- execution capability supports activities in the roadmap by letting users define tasks and assign them to case participants or agents with the goal of preparing case artifacts as input to roadmap entities (for instance, decision points). Case assistance recommends experts who are experienced in similar cases, and recommends related artifacts, templates, and next steps to take a case forward, as identified by agents that participate in a case conversation.

The case measuring and learning module pro-vides analytic support via an executive dash-board for reporting and monitoring purposes, and supports learning and continuous improvement. As case workers handle cases, they leave a trail of information that the system collects and ana-lyzes, adding to the case’s history and the system’s knowledge. To enable knowledge reuse, Case-book analyzes the recorded activities and work-ers’ feedback from past cases and captures them as case templates. These templates are active; they continuously reflect usage information and user feedback. They also suggest updates to the

template owners and communicate those updates to interested case workers. The system presents analysis results — including contextual and sta-tistical information, and suggestions for updating the definitions or behavior of activities or arti-facts — to template owners so they can adapt the templates accordingly. The module presents sug-gestions for template refinement and contextual metadata, making it easier for template owners to understand the context for each change and help-ing them to make informed decisions.

The community-curated case catalog is an active template repository that captures infor-mation from cases using those templates and enables sharing of both templates and workers experiences using them. For example, case work-ers might look in the case catalog for a similar roadmap, artifact, or task template relevant to their particular case and involve colleagues from other teams in discussions about their experi-ences. To support different application domains, we envision that Casebook will offer rich sets of custom templates for domains such as IT, human resources, legal, sales, and so on.

Casebook enables social collaboration among case workers to facilitate case resolution. It engages groups of employees, external users, or customers to fulfill tasks using crowdsourc-ing. Tasks can be open to all Casebook users or to selected user circles through invitations and permission settings. Each case has a case profile — that is, a container of activities related to the case. These activities are posted to the case’s activity pages, and any participants interested or involved in the case can follow them.

Let’s use a sales pursuit as an example case modeled with Casebook. The task profile includes a description of how workers perform the task, the roles that are involved, input and output artifacts, task variances (based on con-figurable parameters such as deal type, size, geography, and sales region), and a list of sup-porting resources. The process profile includes a description, a list of tasks and their precedence, and a link to the case. Figure 1 shows a case-execution module that focuses on the task and process management aspects of a sales pursuit case. When a user creates a new case, Case-book recommends a set of roadmap and process templates from the sales-pursuit domain (large enterprises usually document this information). The case creator can choose to include one or more templates in the case. Templates and their

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contents (such as tasks) are adaptable according to the case’s context.

Case PlanningCasebook users typically create cases in re -sponse to a customer or worker request — for instance, to initiate an insurance claim, start a recall process for a car model, or manage a new sales pursuit. Our aim with Casebook is to make case creation as lightweight as possible. The case information, at a minimum, has a title but can also include other information over time, such as the case description, classification tags, goals, and attachments (for example, support-ing documents). Figure 2 shows the case data model that Casebook supports. The case is rep-resented as a first-class citizen in a network of entities. It’s connected to people and other enti-ties, including processes, artifacts, and road-maps, which are also represented as first-class citizens in this network. SN-User refers to a

social network user who might be a case par-ticipant. The Owner is a subtype of SN-User that owns entities in this network (that is, has edit or delete access rights to them). Profile refers to a page in the social network that records and dis-plays updates to the entity; any first-class citi-zen in the network can have a profile. Activity refers to any action that a user takes, including uploading a document, commenting on another network entity, or assigning or completing a task. The system posts activity feeds to the SN-User’s profile and can also record them on the profile of the entity that the user acts upon, if such a profile exists.

In developing support for case planning, we focused on some characteristics that are signifi-cant for flexible roadmapping and knowledge worker engagement.

First, cases are social. Users start collabo-rating on a case from its start. A case’s profile links it to people, its roadmap, and its processes.

Figure 1. Casebook snapshot of a sales pursuit management scenario. Knowledge workers collaborate to make progress on tasks in the current stage of the sales process. Casebook displays these activities in this case profile and makes related information about the case tasks, processes, artifacts, and workers accessible to those involved in the case.

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The case manager acts as the case administra-tor and is the first point of contact; as such, the manager can invite other participants (work-ers) to the case. Furthermore, other employees in the organization and case stakeholders (such as customers and partners) can, depending on their authorization, choose to follow a case to receive its updates in their personal activ-ity space. Case participants can communicate with other participants, for example, by initiat-ing a chat session or posting updates. Partici-pants, depending on what notification settings they have selected, can choose to receive

notifications about the case’s progress on all or some of the case activities in their personal case spaces. These features support the free flow of information among stakeholders.

Second, case definitions are collaborative, informal, and highly flexible. A case’s par-ticipants can define, share, and coordinate the details of how they will manage the case in sev-eral ways. They can define goals, collaboratively populate a list with checkpoints and create a roadmap, and define tasks that support road-map activities. A roadmap includes checkpoints (also known as milestones or synchronization

Figure 2. Casebook data model. The case is represented as a first-class citizen in a network of entities and is connected to people and other entities, including processes, artifacts, and roadmaps, which are also represented as first-class citizens in this network.

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points) and decision points (points at which par-ticipants can make decisions). A case manager might initiate a roadmap definition by iden-tifying checkpoints or key decision points. In the sales example, the case manager can define three milestones — the initial bid proposal, solu-tion development, and submitting the proposal and solutions to the customer — each one week apart. The manager might define two decision points — getting regional sales approval and approving the pricing — before submitting the proposals to the customer.

Workers refine the roadmap to the next level by collaboratively defining how to handle the case. They can define a process or task from scratch or select one or more process templates from the case catalog. A process can include a set of tasks and meetings (a task accomplished through conversations between case workers or entities), and a graph that captures interdepen-dencies among the task and meeting nodes. Case participants’ involvement in a task can follow a role-based model, RACI (responsible, account-able, consulted, informed), which is common in project management5 and useful in flexible case management. Roadmap definition and execu-tion can occur simultaneously; participants can incrementally update the roadmap as the execu-tion continues.

Third, as a complementary approach to the user-driven creation of checklists and task roadmaps, Casebook supports task extraction by automatically analyzing the text of conver-sations among case participants (details of the approach are available elsewhere6). The case planning module extracts tasks and their prop-erties, such as assigner, assignee, and dead-line, from the messages participants exchange. It then presents these extracted tasks to case participants, who confirm whether the tasks should be included in the case process. We use a natural-language-processing-based approach to identify tasks (referred to as commitments once a caseworker is assigned to and accepts them). This approach also recognizes state changes in existing tasks, such as delegating, cancelling, or completing a task. Attached to the task entity is the text of conversations among participants that happens throughout the task life cycle, from definition to completion.

Fourth, it isn’t just cases themselves that are social, but also all artifacts surrounding a case. Casebook encourages and captures conversations

about anything related to a case, such as goals, checkpoints, roadmaps, attached documents, or diagrams. It provides support for viewing past conversations in the context of the artifacts to which they relate as a way to preserve knowl-edge about how workers manage those artifacts and adapt them over time. Users can view the documents associated with a case, either in the context of a document view for the case or of the tasks the documents relate to. This enables information from disparate sources in an orga-nization to be managed and tracked uniformly by Casebook for a case. To increase contextual knowledge, Casebook also captures a docu-ment’s relationship to artifacts and templates and whether the document is involved in other activities such as review processes. This enables reporting on whether documents are referred to in other activities such as a governance review, and whether a new version of the document tem-plate exists.

Finally, case conversations and activities are woven in with updates about how processes are progressing. The result is a stream of com-munication that aims to capture as many inter-actions about the case as possible, both from human participants changing processes, updat-ing artifacts, and interacting in conversations around cases, and from the automated processes that generate notifications regarding task, pro-cess, artifact, and conversation statuses as the case progresses.

Case ExecutionTraditional, more rigid process-management systems require that processes be defined before they’re enacted. Casebook supports task defini-tion in a plan-as-you-do manner with dynamic updates of the task model based on changes in process configuration parameters, collabora-tive process enactment that weaves tasks and conversations together, and the instantiation of multiple process templates in a single case. We achieved this by relaxing the constraints set in the workflow-based approach. As an impor-tant concept, we introduce floating tasks — that is, participants can revisit a task many times during a case as new versions of its input arti-facts become available, during the period that the case is open. A task’s output isn’t consid-ered final until the case’s end. If new informa-tion is available at input, the task participants are asked to revisit the outputs. The issue then

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becomes identifying how task updates affect other, dependent tasks in the task dependency model, which is an enriched directed-graph rep-resentation of tasks that denotes dependency relations among tasks and their associated meta-data.7 Each revisitation is considered a new ver-sion of the task in the task enactment recorded in its activity history. The other flexibility aspect is weaving tasks into a case’s participant conver-sations and activity streams, which lets Case-book link unstructured information on cases with more structured process information.

Case LearningOne motivation for learning from existing cases is saving users time and effort when working on similar cases in the future; another is to capture best practices in case domains so that users not only have some understanding of what steps have occurred, but also why. We can compare similar cases and identify patterns of processes used, tasks executed, artifacts used, people involved, activities generated, conver-sations held, and common interactions during the course of those cases. Casebook captures this information in the form of a process tem-plate, providing best practice guidelines on how to conduct work in specific case-management domains, and thus facilitating deeper under-standing for business leadership and process owners, as well as case workers.

Casebook compares similar case executions to identify similarities and variations, so it can advise case workers of, and enable them to compare, variations in execution that have occurred in recent cases. It can also prompt a process template owner to consider updating his or her template to incorporate frequently used variations.8

CasepediaThe case catalog, Casepedia, contains previously captured and evolved process templates for use by users working on similar cases in the future. Users can query the catalog by entering config-uration parameters. In the context of the sales pursuit domain, for example, these parameters might include region, industry, size of deal, and deal type. In response to the query, the catalog returns a list of corresponding process templates as recommendations. The user can choose to include one or more of these process templates in his or her case.

The case catalog is social; it isn’t just a pas-sive collection of information about previous cases and templates. Casepedia enables social discussions about roadmap and process templates, during which participants can share experiences, make recommendations, and ask for changes in reference models. For each case, roadmap, and process template, Casepedia tracks cases that use those templates, whether they change them, and statistical information on how long they took to run, along with contextual parameters, such as regions, industry, and deal size in the sales pursuit example. Process and template owners can analyze this information, and the learning module also uses it to inform usage comparisons. Casepedia becomes not only an outlet for maintaining organization-authored templates but also a place for sharing best prac-tices and lessons learned.

Case AssistanceOnce we have a catalog of process templates and information about past cases, we can put that knowledge to work to benefit workers encounter-ing similar cases in the future. Casebook provides mechanisms for learning a rich, annotated tem-plate model,9 which it then uses for active pro-cess guidance and recommendation. A process recommender automatically recommends one or more next steps to the user based on the current step in the process and the customization that the user has generated so far. The system con-siders information that includes the current pro-cess parameters, the current step in that process, and information that’s been shared between par-ticipants during process execution. The user can then choose whether to include the recommended next steps in the list of tasks to be performed for his or her case. The recommendation system is ontology-based and context-aware. The con-text-aware feature considers both structured and unstructured information related to the process to make recom mendations. It updates the results of its recommendations as context changes. Casebook identifies experts in case domains and on various tasks in a case template and provides information on experts so users can reach out to them for help.

Note that case management can be fun. One of the scarcest resources in business today is timely attention of employees, those in both lead-ership and individual performer roles. Gamifica-tion and worker incentives increase productivity

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considerably.10 We’ve integrated gamification techniques ranging from simple measures such as statistics boards to more elaborate approaches for organizing games among workers, thus sup-porting behavior modification and providing stakeholders with real-time insight into how the environment performs. By incorporating game design techniques and analytics, we’ve added an additional dimension of goal-oriented support to the system that not only enhances case-manage-ment applications but also turns case manage-ment into a fun activity for workers. Users can configure the system to reward “players” as a way to address specific business objectives. These capabilities move the business process interface beyond a simple, human-computer interface into a more powerful, behavior-based feedback loop.

Implementation and ExperimentsWe architected and developed Casebook as a soft-ware service that’s deployed in the cloud envi-ronment. We chose the architecture based on the requirement to accommodate different case-man-agement applications and workload patterns with conceptually the same service container. We’ve implemented each Casebook component — that is, learning, assistance, case catalog, and case planner and execution — as individual services and veri-fied them in engagements with HP enterprise ser-vices (ES), IT, and human resources (HR).7–9 These and other new abstractions and components came together to create Casebook as a unified frame-work for adaptive case management. We validated the case roadmapping and execution modules by supporting the sales pursuit scenario with HP ES Global Sales Pursuit.7 The problem that Casebook solved in this context concerned case flexibility and process adaptiveness — that is, different kinds of deals that varied dramatically in size and geo-graphic distribution. We offered flexible definition and dynamic process execution.

We also validated Casebook’s ability to social-ize around case artifacts with the HP ES Portfolio Lifecycle organization. We tested and validated our analytics solutions for case similarity match-ing and relationship discovery with various groups in HP’s HR, IT, and ES organizations.8 We vali-dated the next-best-step recommendation system with R&D projects in HP Software.9 We also vali-dated our task life-cycle information approach on data from real-world conversations from chat and email discussions in IT case-management con-texts. For email data, the precision of commitment

identification was 90 percent with linear regres-sion learners. For chat data, it was 80 percent because, in most cases, sentences weren’t com-plete and subjects weren’t clearly identifiable.6

C asebook presents a new set of models and techniques for supporting people in manag-

ing and handling cases. It introduces a para-digm shift in case management toward systems of engagement for people and stakeholders. We’ve introduced processes and artifacts as first-class entities in a networked environment, and advanced analytics to support people using intelligent techniques. Casebook goes beyond existing case-management solutions and tech-niques by supporting the principles of design and flexibility in collaborative work, offering intelli-gent assistance to case workers, and providing a holistic, goal-oriented, socially enabled platform. We envision that this work marks the beginning of a trend and series of innovations supporting social and adaptive case management.

We plan to extend Casebook by investigating advanced techniques for gaming analytics and human motivation, social and community-driven case monitoring, effort estimation, and predictive analytics for cases, and leveraging crowdsourced, social, and community-curated approaches for managing and reducing the number of cases that are filed in case-management applications.

References1. K.D. Swenson et al., Taming the Unpredictable: Real

World Adaptive Case Management: Case Studies and

Practical Guidance, 1st ed., Future Strategies, 2011.

2. J.B. Hill, The Case for Case Management Solutions, tech.

report G00235833, Gartner, June 2012; http://info.micro

pact.com/the-case-for-case-management-solutions.

3. “Case Management: Combining Knowledge with Pro-

cess,” BP Trends, July 2009; www.bptrends.com/

publicat ionf i les/07-09-WP-CaseMgt-Combining-

KnowledgeProcess-White.doc-final.pdf.

4. H. Motahari-Nezhad and K. Swanson, “Adaptive Case

Management: Overview and Research Challenges,”

Proc. 15th IEEE Conf. Business Informatics (CBI 13), to

appear, 2013.

5. M. Jacka and P. Keller, Business Process Mapping:

Improving Customer Satisfaction, John Wiley and Sons,

2009.

6. A.K. Kalia et al., Identifying Business Tasks and Com-

mitments from Email and Chat Conversations, tech.

report, HP Labs, 2013.

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7. H.R. Motahari-Nezhad et al., “Adaptive Case Management

in the Social Enterprise,” Proc. 10th Int’l Conf. Service-Ori-

ented Computing (ICSOC 12), Springer, 2012, pp. 550–557.

8. H.R. Motahari-Nezhad, C. Bartolini, and P. Mulendra

Joshi, “Analytics for Similarity Matching of IT Cases

with Collaboratively Defined Activity Flows,” Proc.

IEEE 27th Int’l Conf. Data Eng. Workshops (ICDEW),

IEEE 2011, pp. 273–278.

9. H.R. Motahari-Nezhad and C. Bartolini, “Next Best

Step and Expert Recommendation for Collaborative

Processes in IT Service Management,” Proc. 9th Int’l

Conf. Business Process Management (BPM 11), LNCS

6896, Springer, 2011, pp. 50–61.

10. R. Smith, “The Future of Work Is Play: Global Shifts

Suggest Rise in Productivity Games,” Proc. IEEE

Games Innovation Conf., IEEE, 2011, pp. 40–43.

Hamid R. Motahari-Nezhad is a senior research scientist

at Hewlett-Packard Labs in Palo Alto. His research

interests include service computing, enterprise social

computing, and people collaboration and process man-

agement. Motahari-Nezhad has a PhD in computer

science and engineering from the University of New

South Wales, Australia. He’s a senior member of IEEE.

Contact him at [email protected].

Susan Spence is a senior research scientist with Hewlett-

Packard Labs in Palo Alto. Her research focuses on

distributed systems algorithms and architectures, with

experience in the areas of persistent systems, storage

systems, recommender systems, cloud computing, and

data analytics. Spence has a PhD in computer science

from the University of Glasgow. She’s a member of

ACM. Contact her at [email protected].

Claudio Bartolini leads the Connected Enterprise research

team at Hewlett-Packard Labs in Bristol and Palo Alto.

His background is in architecture and design of soft-

ware systems and frameworks. Bartolini has a PhD in

information engineering from the University of Fer-

rara, Italy. Contact him at [email protected].

Sven Graupner is a service platform architect in the

Chief Technology Office of Enterprise Services at

Hewlett-Packard. His research focuses on the interac-

tion between IT automation systems and people processes

in enterprise IT environments. Graupner holds a PhD in

computer science from Chemnitz University of Technol-

ogy and Ludwig-Maximilians-University Munich, Ger-

many. Contact him at [email protected].

Charles Bess is chief technologist of Hewlett-Packard’s Appli-

cation and Business Services in the Americas and an HP

fellow. His research interests include gamification and

service futures. Bess has an MBA from Southern Method-

ist University and a BSEE from Purdue University. He’s

VP of the International Society of Service Innovation

Professionals and a senior member of IEEE. Contact him

at [email protected].

Marianne Hickey is a senior researcher at Hewlett- Packard

Labs in Bristol, UK. Her interests are in decision-

support technology for service, project, and portfolio

management; data modeling, mining, analytics, and

visualization; adaptive distributed systems; and speech

and language technologies. Hickey has a PhD in com-

puter science from Coventry University. She’s a member

of the Institution of Engineering and Technology and

IEEE. Contact her at [email protected].

Parag Joshi is a researcher at Hewlett-Packard Labs in

Palo Alto. His research interests are automatic analy-

sis and information mining from various text sources,

including email, webpages, and other documents,

using various text analysis and natural-language-

processing techniques. Joshi has an MSc (integrated) in

mathematics from the Indian Institute of Technology,

Kanpur. Contact him at [email protected].

Roberto Mirizzi is a researcher at Hewlett-Packard Labs. His

research is focused on recommender systems, Semantic

Web technologies, user experience, and machine learn-

ing. Mirizzi has a PhD in computer science engineering

from Politecnico di Bari, Italy. Contact him at roberto.

[email protected].

Kivanc Ozonat is a senior research scientist at Hewlett-Pack-

ard Labs. His research focuses on statistical learning,

machine learning, statistical clustering, and text mining.

Kivanc has a PhD in electrical engineering from Stanford

University. Contact him at [email protected].

Maher Rahmouni is a senior research scientist at Hewlett-

Packard Labs. His interests are in operational research,

algorithms, graph theory, analytical modeling and

simulation, information retrieval and extraction,

machine learning, and data mining. Rahmouni has

a PhD in computer science from the Institut National

Polytechnique de Grenoble, France. Contact him at

[email protected].

Selected CS articles and columns are also available for free at http://ComputingNow.computer.org.

IC-17-05-Mota.indd 38 10/08/13 2:52 PM

casebook: a cloud-based system of engagement for case management

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