dopa: a data-driven and ontology-based method for ad hoc

DOPA: A Data-Driven and Ontology-Based

Method for Ad Hoc Process Awareness in WebInformation Systems�

Meimei Li, Hongyan Li��, Lv-an Tang, and Baojun Qiu

National Laboratory on Machine Perception,School of Electronics Engineering and Computer Science,

Peking University, 100871, P.R. China{Limm,Lihy,Tangla,Qiubj}@cis.pku.edu.cn

Abstract. The knowledge in Web Information Systems (WISs) makesthat business process can not be described by a fixed model. Due tonot integrating with domain knowledge seamlessly, traditional workflowmanagement technology can not manage the ad hoc process very well.In order to solve that problem, we propose a data-driven and ontology-based method to support process awareness. Using an ontology-basedmodel we integrate domain knowledge and WIS structure into process. Aset of reasoning rules are designed to regulate the task transfer. Learningis done from the cases to help users make decision. A smart tool usedfor ontology building and WIS generation is developed and we prove thefeasibility of this method in a real Web-based application.

1 Introduction

The concept of Web Information System (WIS) was proposed in 1998[1]. As anew generation of information system, WIS obtains information resources andexhibits business data via Web technologies. In the early time, most WISs weredeveloped by data-oriented approach. Early in 1990s, business process reengi-neering illustrates the increased emphasis on process. Thus system engineers areresorting to a process-oriented approach to develop a WIS.

However, in some knowledge-intensive domains such as hospital, law andstock, the business process is ad hoc. That means the process model can notbe defined in advance. Take a Hospital Information System as an example: doc-tors diagnose according to their own experience and patients’ symptoms. Thedifferent illness and clinical experience make process different every time. It isimpossible to design a model to deal with all the possibilities.

One of the reasons leading to that problem is ignoring the influence of datato business process. In knowledge-intensive WISs, each process is dynamicallydriven by the interaction of function and data. So, case handling trends to be� Supported by Innovation Found For Technology Based Firms under grant number

06C26225110506.�� Corresponding author.

K. Aberer et al. (Eds.): WISE 2006, LNCS 4255, pp. 114–125, 2006.c© Springer-Verlag Berlin Heidelberg 2006

DOPA: A Data-Driven and Ontology-Based Method 115

driven by data flow instead of totally by control flow. However, it is very crucialto describe those data and the relations between data and process. In fact, thedata itself, the relations among them, their influence to process, etc, can beconsidered as knowledge. Further, the cases of process are a kind of knowledgetoo. A typical object describing knowledge is ontology. Many researchers haveproposed some ontology-based process models [3][4]. However, the research isstill in its infancy although most methods make progress in different aspects.

Another important matter is describing process in Web environment. WIShas its own components, which may be Web pages, navigators, messages, etc.Thus, the tasks of a process, transfer between tasks, and the constraints, etc,need depend on those special components to carry out. The process finished bydifferent users is also a concentration but not only the process done by one user.

In order to support the process awareness in WISs in a better way, especiallythe ad hoc processes in knowledge-intensive domain, we adopt a data-driven andontology-based method, DOPA in short, and the chief features are:

– Knowledge-supported: A concept model described by ontology combines theprocess closely with related knowledge and WIS structures;

– Data-driven: Depending on data transmission and estimation, support thedynamic processes;

– Rule-restricted: A set of rules are used to make sure the process is correctand effective;

– Tool-visualized: The graphical tool supports the “What you see is what youget” in model building.

The remainder of paper is organized as follows: section 2 presents some meth-ods of process awareness; section 3 describes WIS Ontology including DOMAINOntology, STRUCTURE Ontology and PROCESS Ontology; section 4 explainshow the case is handled through reasoning and learning from WIS Ontology;section 5 introduces experiment and application; at last, section 6 summarizesthe paper and discusses future work.

2 Related Work

Process-Aware Information Systems (PAISs) support business processes in orga-nizations based on explicit knowledge of both the organization and the processes[14]. Four typical process-aware methods are: Workflow, Groupware, Ad-hocWorkflow and Case Handling. Figure 1(a) compares them from two dimension-alities: process structure and process driver.

In terms of ”process driver”, Workflow and Ad-hoc Workflow drive the processaccording to the control structure defined in models while Groupware takes dataas the basis of process. But due to its weak abilities on control, Groupwarecan not adapt to the complexity in WIS very well. From the dimensionality of”process structure”, the highly-structured model in Workflow must be designedin advice. The main idea of Ad-hoc Workflow to support the semi-structuredprocess is manually changing the process by end users, such as Caramba [8]. Thismethod increases the workloads. Compared with others, Case Handling supports

116 Meimei Li et al.

(a) (b)

Fig. 1. (a) Process-aware Methods. (b) The Web page of Bed Assignment.

both semi and highly structured processes driven by data and process [9]. Butit may be a trouble for Case Handling if the processes are ad hoc structured.

Although the process-aware methods have evolved rapidly, unfortunately, theyseldom consider the domain knowledge. In order to integrate the knowledge,many people are engaged in building the ontology-based process models. Themodel created by Gianluigi Greco [4] provides a semantic vision of the applicationcontext and of the processes themselves. But, it belongs to the ”Workflow”method mentioned in Fig.1(a), so it is hard to handle ad hoc processes.

At the same time, much work has been done to support BPM in Web appli-cation [5][7][10][11]. OOHDM and UWE base on UML to describe the controlflow among operations done by one user in a time. WebRatio is a tool to developdata-intensive Web application using ontology [3][5]. It adopts Web ModelingLanguage (WebML)[6] integrating process modeling into the design of Web ap-plication in hypermedia framework. WSDM is equipped with a Task Modelingstep and defines various relations among tasks corresponding to each possibility.

However, those examples are oriented toward simple personal applications,whose primary function is to exhibit data. Compared with the web sites, WISfocuses on more complex operations of data, abundant domain semantic andbusiness logic. Besides, although some of them e.g. WebRatio extends the datamodel it is still limited to metadata. But in WIS data are so important forprocess that they can not be ignored.

3 WIS Ontology

WIS Ontology includes DOMAIN Ontology, STRUCTURE Ontology and PRO-CESS Ontology. The DOMAIN Ontology describes the domain data and rela-tions between them, while the STRUCTURE Ontology describes the structureof WIS components and PROCESS Ontology describes such a task network thatcomplies with the business logic and routine.

3.1 DOMAIN and STRUCTURE Ontology

The details of DOMAIN and STRUCTURE Ontology in WIS development canbe found in [12]. Here is only a general picture:


– DOMAIN Ontology: It is used to describe the objects in a special do-main and relations between them. For example, in hospital, each personcorresponds to an organization like pharmacy, surgery or orthopaedics. Thedrugs and equipments are distributed to some organizations or persons. Thiskind of knowledge is presented by DOMAIN Ontology. Because it is domain-specific, it should refer to the domain standards, e.g. the Reference Informa-tion Model (RIM) in HL7 [15].

– STRUCTURE Ontology: In WIS, Web pages are the interfaces to com-municate with people. Through the pictures, text fields, tables and buttons,etc, they tell people what they have known and what they can do. The layoutof such elements is the structure of a Web page. The navigations betweenthe Web pages lead users to implement functions step by step. Those pagesand navigations consist of the structure of WIS.

3.2 PROCESS Ontology

In order to illustrate the scenario, an example in hospital is given first. Fig.1(b) isthe page of Bed Assignment in the internet Total Hospital Information System(iTHIS) [2]. The page indicates a task in the hospital process and implicatesmuch knowledge. The textfield “Bed” in it is a basic item in STRUCTUREOntology which has the attributes such as type, size, position and constraint.The item corresponds to a data object “Bed” in DOMAIN Ontology.

In WIS, tasks like “Bed Assignment” of processes are implemented by a setof Web pages connected by navigations. But whether the task is triggered andwhen is implemented rely on the state of data objects. As a result, PROCESSOntology should base on DOMAIN Ontology and STRUCTURE Ontology. Thedefinitions are as following:

Definition 1. The business process BP is a 5-tuples, BP =< TS,DS,RS,ST,FT >, where TS, DS and RS are respectively a set of tasks, data and relations be-tween tasks. Relations represent the fore-and-aft order of two tasks. They are notdefined in advance but during the running time dynamically. ST means the taskat the beginning of BP, e.g. Patient Admission and FT means the final tasks, e.g.Leave hospital. They comply with the constraints Cont(ST ) = 1; Cont(FT ) ≥ 1Cont(s) is the function to get the element number of s. In the whole process, alldata is created by tasks, so the DS is the union of GD in each task (see definition2), which is DS =

⋃∀t∈TS(t.GD).

Definition 2. The task T is a 6-tuples, T =< TN,TC,GD,MD,P S,Ro >,where TN is the name of the task, TC means the indispensable conditions for im-plementing the task. GD is the data generated after running, e.g. the informationof bed assignment while MD is the data needed for running, e.g. the informationof admission and charge. P S is the Web Page Set to implement task function,and Ro indicates the legal role, e.g. nurse can assign the bed. For special tasks,there are Cont(ST, MD) = 0; ∀t ∈ TS ∧ t �= ST, Cont(t.MD) ≥ 1; And theGD of a task is considered as the union of PGD in each page of task’s P S, soGD =

⋃∀p∈P S.PS(p.PGD).


Definition 3. The Web Page Set P S is a 3-tuples, P S =< PS,LS,SP >,where PS is the set of all Web pages in P S, and LS is the set of all navigationsbetween pages in PS. SP means the page at the beginning. From SP, all theother pages in PS must be arrived through navigations in LS, so ∀p ∈ P.PS ∧p �= SP, ∃(pi, li), Link(SP.ok0, p1.ok1 ∈ LS∧ Link(pj · okj , pj+1·ok(j+1) ∈ LS ∧Link(pn · okn, p · ok) ∈ LS(j ∈ (2, n − 1)). Link (a,b) is a navigation from a tob. What is more, in the same period, all the pages can be accessed only by thesame user.

Definition 4. The Web page P is a 4-tuples, P =< BIS,OS,PGD,PAD >,where BIS and OS are respectively set of basic items, like droplist of specialty, andoperation items like button of assign. PGD means the data generated after run-ning the page and PAD is the accessorial data to give some help, like informationin the table of Bed Assignment Summary. The operations in a page have differ-ent types. The data objects related to SELECT or INITIATE are used to provideinformation to users so that they belong to PAD. While INSERT and UPDATEeither create a new object instance or give a new value to some data object, so thedata objects related to them belong to PGD. Some data objects can belong to both,such as the PGD of INSERT can be the PAD of UPDATE. So there are PAD =⋃

∀bi∈BIS(bi.D) − PGD ; PGD =⋃

∀o.OTY =‘Insert′or‘Update′(o.OBIS.D); ∀p,∃o1, o2(o1 ∈ p∧o2 ∈ p∧o1.OBIS = o2.OBIS∧o1.OBIS ⊂ p.PAD∧o2.OBIS ⊂p.PGD).

Definition 5. The operation item O is a 5-tuples, O =< ON,OBI,OC,OTY,OBIS >, where ON is the name of the operation,OBI means the basic item onwhich the action is triggered, OC indicates the execution conditions, e.g. foroperation of assignment OC is that the values of bed, specialty, room and wardcan not be NULL, OTY is the type of O, which are mainly the operations toDatabase, and OBIS is the execution parameters, e.g. droplist of specialty is oneof parameters of operation “Assign”. For all sets in the definition, there are∀s ∈ {TS, DS, PS, BIS, OBIS}Cont(s) ≥ 1.

Definition 6. The navigation L is a 4-tuples, L =< PSou,PTar,LC,LO >,where PSou is the current Web page showed to user, and PTar is the Web pageshowed after the navigation, LC is the condition deciding the direction of navi-gation, such as whether the operation successes or not, and LO is the operationitem on which L is triggered. E.g. the operation “update” is triggered, and if theaction successes, it will return to the same page with new values generated byupdating.

Definition 7. The Relation R is a 4-tuples, R =< RSou,RTar,RD,RO >,where RSou is the former task which is implemented first, RTar is the lattertask which is triggered after RSou is finished, RO is the trigger operation andRD is the data transferred from RSou to RTar. After a task is finished, the MDand the PAD in the SP of the RTar should be transferred for further estimationand implementation. As a result, there is RD = t.MD

⋃t.P S.SP.PAD.

In order to illustrate the elements and their relations of the definition aboveclearly, Fig.2(a) uses a UML model to describe the PROCESS Ontology.


(a) (b)

Fig. 2. (a) UML Model of PROCESS Ontology. (b) Running States of a Task.

4 Reasoning and Learning from WIS Ontology

As we have said that the case is not driven by a well-defined model, then how toestablish the relations among tasks during the running time? A set of rules areestablished for that problem. Certain rules are established by domain experts.While others are reasoned according to the data conditions or recommendedthrough learning.

4.1 Reasoning

When a task is completed, case must transfer to others. During this process,four steps would be taken one by one:

1. Find out tasks which may run after task t as candidate task set (Candi-dataTasks(t));

2. For each ct in CandidateTasks(t), test whether the generated data objectsmeet TC of ct ;

3. Get rid of ungratified ones from CandidateTasks(t), and post task set Post-Tasks(t) is left;

4. Transfer data to every pt in PostTasks(t) and send a request.

All the four steps will cause the transformation of running state. After step 1,ct is initiated. Estimated by step 2, ct is passed if result fails. Otherwise, itturns to be ready. The ready ct becomes to pt. According to the strategy ofdistribution, when request is chosen by a worker and resources are all prepared,pt begins to run. The state transformation of a task in the life cycle is shown inFig.2(b).

Rules are used to express the semantic of each state transformation, and aredescribed as logic sentences, e.g. a ⇒A b , where a is the condition of the rule,b is the result and A means the action needed to be done. In step 1, it needsto find what is ct. If there is certain relation between t and ct, the rule is likeRule-1, and → means it is the compelling rule. Otherwise, frequent choices ofother users can give you some advice, and you can choose a better one or notfrom those advices, so → means it is an optional rule, like Rule-2.


– Rule-1Complete (t) →A Ready (ct), A is: (1) Put ct into PostTasks(t);(2) TransferRD from t to ct ; (3) Send a request and wait in ct.TaskList; (4) Put < t, ct >in Task-Path.

– Rule-2Complete(t) →A Ready(ct), A is the same as Rule-1.If there are not rules like Rule-1 or Rule-2, we will choose ct according tostate of data objects. Instead of all tasks, we choose a subset as ct whose MDintersects with TGD of t .The feasibility can be explained in two aspects:(1)in the view of t, TGD implies changes of data and the new state will affectthe future process; (2) in the view of ct, if MD only contains those dataunchanged for a long time, the task may be finished or has less possibilityto run again. So, the more potential tasks are the ones whose MD includesnew information. Rule-3 to Rule-5 indicate this situation.

– Rule-3ct.MD

⋂t.TGD �= Φ =⇒ Initiate(ct).

– Rule-4Initiate(ct) ∧ ct.TC is contented =⇒A Ready(ct), A is the same as Rule-1.

– Rule-5Initiate(ct) ∧ ct.TC is not contented =⇒ Pass(ct)RD will be transferred from t to ct if ct is ready. There are various mannersto carry the point, and the typical one is using database. Task t stores itsTGD in database from which ct fetches what it wants. Each task maintainsa TaskList to save the requests of cases. After ct is confirmed, a 2-tuples< pre task, post task > is added to the task sequence Task-Path that casehas passed through. The transformation from Ready to Running is describedby Rule-6.

– Rule-6Ready(ct)∧ All resource are prepared ∧ request of case is chosen=⇒Running(ct), A is (1) Tag request in ct.TaskList to inform others that re-quest is replied;(2) Jump to ct.SP to run the task.Tasks do not run as well as we expect. Abnormities usually happen, such ashuman interruption. Therefore, we save those temporary data for restartinglike Rule-7. Another problem is caused by “DELETE” operation. Elimina-tion of the existing data in the former task may lead rollback or collapse oflatter task. Rule-8 and rule-9 are used to solve this problem. But if the latterone has finished, no influence will be exerted.

– Rule-7Running(ct)∧ user triggers to interrupt the task =⇒A Interrupt(ct), A is(1) Store the data which have been generated; (2) Release the resourceswhich have been occupied.

– Rule-8Ready(ct) ∧ RO.OTY == “DELETE” =⇒A Initiate(ct), A is Remove re-quest from ct.TaskList.


– Rule-9Running(ct) ∧ RO.OTY ==“DELETE” =⇒A Ready(ct), A is (1) Deleteall the data which have been generated by ct; (2) Remove request fromct.TaskList.

4.2 Learning

In Task-Path, a sequence of 2-tuples records the trace of a case, such as {<t0, t1 >, < t1, t2 >, < t2, t5 >, < t5, t10 >, < t10, t1 >, < t1, t3 >, < t1, t4 >,< t3, t5 >, < t4, t6 >, < t4, t7 >, < t5, t8 >, < t8, t9 >, < t6, t8 >, < t8, t9 >}.In ordinary workflow log, this example will be {t0, t1, t2, t5, t10, t1, t3, t4, t5, t8,t9, t6, t8, t9}. It only describes the order of tasks but not the “Relation” betweentasks. Note that, it is easier to discover priority, parallel and circle from Task-Path than workflow log. Through synthesis of different Task-Paths, we aim to geta conceptual model from which the system can learn new rules to help users makedecisions. Unlike strict model such as Petri-net, our model dose not emphasizewhether each junction is AND/OR Join or AND/OR Split, but adopts a set ofweights to illuminate the parallel degree. If the weight is larger than the valuegiven by user, the path segment will be added into the rule set for reasoning. Fig.3is a simple example. First, Task-Path needs preprocessing. It aims to:(1)Simplifycircle: Replace the segment of circle with a node, thus the model is converted toa directed acyclic graph (DAG);(2)Get rid of dead nodes: The second element of2-tuples may not be implemented in reality (see Fig.2(b)). In DAG, dead nodesare the leaf nodes which are not final tasks; (3) Mark the running order to helpanalyze AND/OR logic. Fig.4(a) is the model of Task-Path mentioned at thebeginning of this section and Fig.4(b) is the result of preprocessing.

Fig. 3. The model is a directed graph. The node is task, and the edge is Relationbetween tasks. In each node, there are a split table and a join table, recording theweight of AND/OR logic. E.g. “T2, T4 : 0.27” means the weight of the situation thatT2 and T4 implement together is 0.27.

Next, depended on the models in Fig.4 (b), we generate the model in Fig.3.The method of breadth-first search is used to each model, and the split table ofparent node is edited due to the fact that all brother nodes are AND split. Forjoin logic, if the order of some join edge is larger than that of some split edge,e.g. 10 of < t6, t8 > is larger than 9 of < t8, t9 >, the logic of t8 is OR join,otherwise it is AND join. The algorithms of pretreatment and model generationare in Fig.5.


Fig. 4. (a) Model of Task-Path. (b) Result of Preprocessing.

Fig. 5. Algorithms of Preprocessing and Model Generation

5 Experiments and Application

In order to verify the feasibility of DOPA, we combine experiment and applica-tion together. Our main experimental environment is the project WISE whichaims to construct WIS automatically from users’ view. The main parts of WISEare:

WISE Builder: A visual graphic tool helps the engineers build WIS Ontology.It supports “What You See Is What You Get” when defining model [12];

Data Mapper: A data mapping module used to generate the SQL codes fordata access and manipulation. All the actions of SELECT, INSERT, UP-DATE and DELETE are automatically carried out with the help of thispart [13];


Code Generator: A powerful component aims to generate most of codes intarget system.Its function includes: Convert the models to Web pages andgenerate function modules. What is more, it assists Process Builder to gen-erate process engine, etc;

Process Builder: A useful part defines PROCESS Ontology and rules, andcreates the process engine with reasoning and learning ability. The engineembedded in target system will support the process-awareness in WIS.Fig.6(a) shows the architecture of WISE.

Our experiments focus on a simple process in hospital with seven single-pagetasks: User Login, Main Page, Patient Admission, Bed Arrangement, Diagnose,Blood Test, and Leave Hospital. To compare, three experiments have been doneindependently:

Experiment 1: Two professional programmers with 3 years experience on JSPand WIS develop on IBM Websphere Studio Application Developer 5.0, therequirement documents and design details are provided;

Experiment 2: A medicine student who is familiar with the application do-main and a professional programmer depends on a workflow managementtool;

Experiment 3: All the conditions are the same as experiment 2 except thatthe tool is WISE.

The three experiments share the same database and hardware environment.The time cost can divide into two parts: the time of generating function pagesand generating process engine. The top figure in Fig.6(b) records the time costfor generating the seven Web pages and we suppose that the WFM tool inExperiment 2 don’t support the auto-generation of function modules. The timecost for generation of process engine is shown in the bottom figure in Fig.6(b)and consist of manual programming time, modeling time and code-generatingtime. It also shows the time needed for ten times changes in process.

Note that, Experiment 3 generates JSPs and Java Codes automatically, sothe actual workloads are reduced much. The time costs for process generation in

(a) (b)

Fig. 6. (a) Architecture of WISE. (b) Time Cost in Experiments (Unit: min).


three experiments are similar, but the time for updating is different. Althoughthe time for each change in Experiment1 or Experiment 2 is not long, ten timesof the time are needed for ten times of changes. However, in Experiment 3 somechanges can be reasoned through rules so maybe only 6˜7 times of changes areneeded in ten times. Thus the whole time is less than the other two.

6 Summary and Future Work

DOPA is a novel method for ad hoc process awareness in knowledge-intensiveWIS. This paper delves deeply into modeling process ontology and handlingcases through reasoning and learning. The chief contributions include: proposean ontology-based model for business process which integrates domain knowl-edge and WIS structure; present a data-driven method to implement processawareness through reasoning from PROCESS Ontology based on a set of rulesand learning from cases to help users make decision; and develop a smart toolused for ontology building and WIS generation.

The research on supporting the ad hoc process awareness in WIS is just atbeginning and future efforts are oriented to the following aspects:

1. Improve the abnormity handling: dead circle is an instance of abnormity.Further, wrong operations of users or the disconnection of internet will alsodisturb the process. So rules need be refined;

2. Enhance the reasoning and learning: more knowledge will be imported tocontrol the process so reasoning will be more powerful such as supportingontology query. Moreover, the results of learning can be used to assist anal-ysis and optimization;

3. Establish the verification: because the process is ad hoc, verification is neededto pledge the correctness of the resulting process, especially for the hospitalbusiness processes which require the steps to be verified in order to ensurethe suitable patient care.

References

[1] Tomas Isakowitz, Michael Bieber, Fabio Vitali: Web Information Systems. Com-munications of the ACM, Vol.41(7), A CM Press (1998) 78-80.

[2] Hongyan Li, Ming Xue, Ying Ying: A Web-based and Integrated Hospital Infor-mation System. In Proceedings of IDEAS04-DH, China, (2004) 157-162.

[3] Marco Brambilla: Extending Hypertext Conceptual Models with Process-OrientedPrimitives. Lecture Notes in Computer Science Vol.2813, (2003) 246-262.

[4] Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, and Domenico Sacc: AnOntology-Driven Process Modelling Framework. In proceedings of InternationalConference on Database and Expert Systems Applications-DEXA, Zaragoza, Aug.30-Sep. 3 (2004).

[5] Piero Fraternali: Tools and Approaches for Developing Data-Intensive Web Ap-plication. ACM Computing Surveys, Vol.31, No.3 (1999).


[6] Ceri, S., Fraternali, P., Bongio, A.: Web Modeling Language (WebML): a modelinglanguage for designing Web sites. WWW9/Computer Networks 33(1-6) (2000)137-157.

[7] Hans Albrecht Schmid, Gustavo Rossi: Modeling and Designing Processes inE-Commerce Applications. IEEE International Computing Vol.8 (1): JAN-FEB(2004) 19-27.

[8] Dustdar S: Caramba-A Process-aware Collaboration System Supporting Ad hocand Collaborative Processes in Virtual Team. Distributed and Parallel DatabasesVol.15(1), (2004) 45-66.

[9] Wil M.P.van der Aalst, Mathias Weske, Dolf Grnbauer: Case Handling: ANew Paradigm for Business Process Support. Data & Knowledge EngineeringVol.53(2), (2005) 129-162.

[10] Olga De Troyer, Sven Casteleyn: Modeling Complex Processes for Web Applica-tions using WSDM. In Proceedings of the 3rd International Workshop on Web-Oriented Software Technologies, (2003) 27-50.

[11] Nora Koch, Andreas Kraus: The Expressive Power of UML-based Web Engineer-ing. In Proceedings of the 2nd International Workshop on Web-Oriented SoftwareTechnologies, IWWOST, (2002) 105-119.

[12] Tang Lv-an, Li Hongyan, Pan Zhiyong, Tan Shaohua: PODWIS: A PersonalizedTool for Ontology Development in Domain Specific Web Information System.LNCS Vol.3399 , (2005) 680-694.

[13] Hongyan Li, Ming Xue, etc: Understanding User Operations on Web Page inWISE. In proceedings of 6th International Conference in Web-Age InformationManagement, LNCS Vol.3739 (2005) 846-851.

[14] Wil M.P. van der Aalst, Michael Rosemann, Marlon Dumas: Deadline-based Es-calation in Process-Aware Information Systems. BPM Center Report, (2005),http://www.BPMcenter.org.

[15] HL7 Homepage: http://www.hl7.org.

dopa: a data-driven and ontology-based method for ad hoc

Documents