John Krogstie
email: krogstie@idi.ntnu.no

Shang Gao
email: shanggao@idi.ntnu.no

A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems

Keywords: Big data, data quality, Semiotic levels

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The term 'big data' and the accompanying area have received increasing interest over the last years. The area is often defined through describing a number of V-s (Volume, Variety … etc). In a way, with an area where a major conference already in the seventies took the name VLDB -Very Large Data Bases one might wonder what is particularly different, and how the notion of data quality in connection to big data is different than data quality in general.

Data quality has for a long time been an established area of research [START_REF] Batini | Data Quality: Concepts, Methodologies and Techniques Springer[END_REF] and work on quality assessment in data integration has also appeared as an area recently [START_REF] Martin | A Methodology and Architecture Embedding Quality Assessment in Data Integration[END_REF]. A related area that was established in the nineties is quality of models (in particular quality of conceptual data models) [START_REF] Moody | Metrics for Evaluating the Quality of Entity Relationship Models[END_REF]. Data can be looked upon as a type of model (on the instance level), as illustrated e.g., in the product models in a CAD-system. Traditionally, one has looked at model quality for models on the M1 (type) level (to use the model-levels found in e.g., MOF). On the other hand, it is clear especially in product and enterprise modelling that there are models on the instance level (M0), an area described as containing data (or objects in MOF-terminology). Also if we look upon administrative data, e.g. data on persons, it is clear that this is an abstraction, focusing on certain properties (e.g. name, age) of persons based on the purpose of having the data, not being a mirror of reality capturing all perceivable properties of persons. Thus, our outset is that also data quality can be looked upon relative to more generic frameworks for quality of models.

In this paper we look on characteristics of big data in the light of data quality as conceptualized using the SEQUAL-framework for quality of models. We will in section 2 describe some different work describing big data characteristics. In section 3 we described SEQUAL and its specialization for data quality based on earlier work. The main contribution of this work is section 4, where we position the big data characteristics within this semiotic framework. Section 5 concludes the paper.

Background on Big Data

Big Data has by many been 'conceptualized' by letter-magic centred on the letter V. Big Data first related to Volume, but later 3 V, 4 V and 5 V frameworks have been presented. As a start, Beyer and Laney [START_REF] Beyer | The importance of'big data': a definition[END_REF] describe 3 areas:  Volume, referring to the size of the data.  Variety, referring to the heterogeneity of data representations, data acquisition, and semantic interpretation.  Velocity, referring to the rate at which new data arrive and the time in which it must be acted upon.

IBM presents four areas 1 . In addition to the three above, they mention veracity, relating to the uncertainty of the data quality. Another source2 also add a fourth area, namely viability pointing to that it is necessary to filter through all the data and carefully select the attributes and factors that most likely to predict outcomes and matter most to the business.

Finally Advanced Performance Institute [START_REF] Marr | Big Data[END_REF] summarizes the area with 5 V-s, giving a bit more detailed information on the characteristics. We further exemplify with the current situation in the media industry.  Volume refers to the large amounts of data generated going from are Terabytes to Zettabytes and more. This makes many datasets too large to store and analyse using traditional database technology. New big data tools use distributed systems so that we can store and analyse data across databases that are potentially spread around the world. In the news area, it means that larger volumes of data are available to be analysed relative to understanding what is happening.



Velocity refers to the speed at which new data is generated and distributed. One example is a social media messages going viral in minutes. Technology allows us now to analyse the data while it is being generated, without ever putting it into databases. E.g. the SAP HANA architecture is providing an in-memory database solution that can integrate transactional data and analytical queries.



Variety refers to the different types of data that one might want to look at in concert. In the past one mainly focused on structured data that fitted into tables or relational databases. A large amount of the world's data is unstructured (text, images, video, voice, etc.), kind of data that is particularly relevant in the media industry. With big data technology one can now analyse and bring together data of different types such as messages, social media conversations, photos, sensor data, video and voice recordings. Note that the variety aspect is not particular to Big Data, the issues is also found within large organizations in their attempt to address data integration [START_REF] Krogstie | Evaluating Data Quality for Integration of Data Sources Proceedings PoEM[END_REF][START_REF] Martin | A Methodology and Architecture Embedding Quality Assessment in Data Integration[END_REF] internally or in collaboration with business partners.



Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality including accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of the content). In the media industry the veracity issue has become increasingly acute with the mix of journalistic and public sources of news.



Value. Having access to big data is no good unless one can turn it into some value. This can be said to relate to Viability as described above. To have value in the news area, it is important to be able to report quickly based on what is happening. In newspapers we find for instance special systems such as QuakeBot used by LA Times for more or less auto-generating news-articles based on available data. It can also be an issue as for the rights of reusing existing data to create value from the data.

On our own account, we add the need for visualization. To be able to get value from the data, it must be abstracted and visualized in an appropriate way to make the data useful, applying and extending techniques in the area of information visualization [START_REF] Ware | Information Visualization Morgan Kaufmann[END_REF].

SEQUAL Data Quality Framework

SEQUAL [START_REF] Krogstie | Model-based development and evolution of information systems: A quality approach[END_REF] is a framework for assessing and understanding the quality of models and modelling languages. It builds on early work on quality of model [START_REF] Lindland | Understanding Quality in Conceptual Modelling[END_REF][START_REF] Moody | What Makes a Good Data Model? Evaluating the Quality of Entity Relationship Models[END_REF], but has been extended based on theoretical results [START_REF] Moody | Theoretical and practical issues in evaluating the quality of conceptual models: Current state and future directions[END_REF][START_REF] Nelson | A conceptual modeling quality framework[END_REF][START_REF] Price | A semiotic information quality framework: Development and comparative analysis[END_REF] and practical experiences [START_REF] Krogstie | Model-based development and evolution of information systems: A quality approach[END_REF][START_REF] Krogstie | Process modeling value framework[END_REF] with the original framework. It has earlier been used for evaluation of modelling and modelling languages of a large number of perspectives, including data quality [START_REF] Krogstie | A Semiotic Framework for Data Quality[END_REF][START_REF] Krogstie | Evaluating Data Quality for Integration of Data Sources Proceedings PoEM[END_REF], data modeling [START_REF] Krogstie | Quality of Conceptual Data Models[END_REF], ontologies [START_REF] Hella | A Structured Evaluation to Assess the Reusability of Models of User Profiles[END_REF], process modeling [START_REF] Aagesen | Analysis and design of business processes using BPMN[END_REF][START_REF] Krogstie | Quality of Business Process Models[END_REF], enterprise modeling [START_REF] Krogstie | Assessing Enterprise Modeling Languages using a Generic Quality Framework[END_REF], topological modeling (maps) [START_REF] Nossum | Integrated Quality of Models and Quality of Maps, Enterprise[END_REF] and goal-oriented modelling [START_REF] Krogstie | Using Quality Function Deployment in Software Requirements Specification[END_REF][START_REF] Krogstie | Integrated Goal, Data and Process Modeling: From TEMPORA to Model-Generated Work-Places[END_REF]. Quality has been defined referring to the correspondence between statements belonging to the following sets:

 G, the set of goals of the modelling task.  L, the language extension.  D, the domain, i.e., the set of all statements that can be stated about the situation.  Domains can be divided into two parts, exemplified by looking at a software requirements specification:

 Everything the computerized information system (CIS) is supposed to do. This is termed the primary domain.  Constraints on the model because of earlier baselined models such as system level requirements specifications, enterprise architecture models, statements of work, and earlier versions of the requirement specification. This is termed the modelling context. In relation to data quality, the underlying data model is part of the modelling context when it is defined.

 M, the externalized model itself.

 K, the explicit knowledge relevant to the domain of the audience.  I, the social actor interpretation of the model  T, the technical actor interpretation of the model  The main quality types following the steps of the so-called semiotic ladder [START_REF] Falkenberg | A Framework of information system concepts[END_REF] are:  Physical quality: The basic quality goal is that the externalized model M is available to the relevant social and technical actors (and not to others).  Empirical quality deals with comprehension and predictable error frequencies when a model M is read by different social actors  Syntactic quality is the correspondence between the model M and the language extension L.  Semantic quality is the correspondence between the model M and the domain D.  Perceived semantic quality is the similar correspondence between the social actor interpretation I of a model M and his or hers current knowledge K of domain D.  Pragmatic quality is the correspondence between the model M and the actor interpretation (I and T) and application of it.  The goal defined for social quality is agreement among actor's interpretations.  The deontic quality of the model relates to that all statements in the model M contribute to fulfilling the goals of modelling G, and that all the goals of modelling G are addressed through the model M.

Data Quality Relative to the SEQUAL Quality Types

We here discuss means within each quality level, positioning the areas that are specified by Batini et al. [START_REF] Batini | Data Quality: Concepts, Methodologies and Techniques Springer[END_REF] and Price et al. [START_REF] Francalanci | View integration: A survey of current developments[END_REF][START_REF] Hella | A Structured Evaluation to Assess the Reusability of Models of User Profiles[END_REF]. These are emphasised using italic. This overview is largely taken from [START_REF] Krogstie | A Semiotic Framework for Data Quality[END_REF]. Physical data quality: Aspects of persistence, data being accessible (Price) for all (accessibility (Batini)), currency (Batini) and security (Price) cover aspects on the physical level. This area can be looked upon relative to measures of persistence, currency and availability that apply also to all other types of models.

Empirical data quality: This is addressed by understandable (Price). Since data can be presented in many different ways, this relates to how the data is presented and visualized. How to best present different data depends on the underlying data-type.

There are a number of generic guidelines within data visualization and related areas that can be applied, and we will only mention a few of these here. For computeroutput specifically, many of the principles and tools used for improving human computer interfaces are relevant at the empirical level [START_REF] Shneiderman | Designing the User Interface: Strategies for Effective Human-Computer Interaction[END_REF]. For visual presentation of data, one can also base the guidelines on work in cognitive psychology and cartography with the basis that data is meant to be useful in connection to communication between people.

Syntactic data quality:

From the generic SEQUAL framework we have one main syntactic quality characteristics, syntactical correctness. This means that all statements in the model are according to the syntax and vocabulary of the language Syntax errors are of two kinds:  Syntactic invalidity, in which graphemes not part of the language are used.  Syntactic incompleteness, in which one lack constructs or information to obey the language's grammar

Conforming to metadata (Price) including that the data conform to the expected data type of the data (as described in the data model) are part of syntactic data quality. This will typically be related to syntactic invalidity when e.g. the data is of the wrong data-type.

Semantic data quality: When looking upon semantic quality relative to the primary domain of modelling, we have the following properties:

 Completeness in SEQUAL is covered by completeness (Batini), mapped completely (Price), and mapped unambiguously (Price).



Validity in SEQUAL is covered by accuracy (Batini), both syntactic and semantic accuracy as they have defined it, the difference between these is rather to decide on how incorrect the data is, phenomena mapped correctly (Price), properties mapped correctly (Price) and properties mapped meaningfully (Price). Since the rules of representation are formally given, consistency (Batini)/mapped consistently (Price) is also related to validity. The use of meta-data such as the source of the data is an important mean to support validity evaluation of the data.

Properties related to the model context are related to the adherence of the data to the data model. One would expect for instance that  All tables of the data model should include tuples  Data is according to the constraints defined in the data-model

The possibility of ensuring high semantic quality of the data is closely related to the semantic quality of the underlying data model. When looking upon semantic quality of the data model relative to the primary domain of modelling, we have the following properties: Completeness (Moody and Batini) (number of missing requirements) and integrity (Moody) (number of missing business rules) relates to completeness.

Completeness (Moody) (number of superfluous requirements) and integrity (Moody) (number of incorrect business rules) relates to validity. The same applies to Batini's points on correctness with respect to model and correctness with respect to requirements.

Pragmatic data quality: Pragmatic quality relates to the comprehension of the model by participants. Two aspects can be distinguished:  That the interpretation by human stakeholders of the data is correct relative to what is meant to be expressed. In addition to the data it will often be useful to have different meta-data represented (making it easier to understand the intent behind the data).  That the tool interpretation is correct relative to what is meant to be expressed.

Starting with the human comprehension part, pragmatic quality on this level is the correspondence between the data and the audience's interpretation of it. Moreover, it is not only important that the data has been understood, but also who has understood (the relevant parts of) the data.

The main aspect at this level is interpretability (Batini), that data is suitably presented (Price) and data being flexibly presented (Price). Allowing access to relevant metadata (Price) is an important mean to achieve comprehension. Social data quality: The goal defined for social quality is agreement. Relative agreement means that the various sets to be compared are consistent --hence, there may be many statements in the data representation of one actor that are not present in that of another, as long as they do not contradict each other. The area quality of information source (Batini) touches important means for the social quality of the data, since a high quality source will increase the probability of agreement. Another term found in data quality literature on this aspect is provenance. In some cases one need to combine different data sources. This consists of combing the data-models, and then transferring the data from the two sources into the new schema. Schema integration techniques [START_REF] Francalanci | View integration: A survey of current developments[END_REF] are specifically relevant for this area. Deontic data quality: A number of aspects are on this level relating to the goals of having the data in the first place. Aspects do decide volatility (Batini) and timeliness (Batini)/ timely (Price) needs to relate to the goal of having and distributing the data. The same is the case for type-sufficient (Price), the inclusion of all the types of information important for its use. For anything but extremely simple and highly intersubjectively agreed domains, total validity, completeness, comprehension, and agreement as described above under semantic, pragmatic and social quality cannot be achieved. Hence, for the goals on these levels to be realistic, they have to be relaxed, by introducing the idea of feasibility. The time to terminate a modelling activity is thus not when the model is ``perfect'' (which will never happen), but when it has reached a state where further modelling is regarded to be less beneficial than applying the model in its current state. Accordingly, a relaxed kind of these goals being dependent on human judgement can be defined, which we term feasible validity, feasible completeness, feasible comprehension, and feasible agreement. Feasibility thus introduces a trade-off between the value and drawbacks for achieving a given model quality. When we structure different aspects according to these levels, one will find that there might be conflicts between the levels (e.g., what is good for semantic quality might be bad for pragmatic quality and vice versa).

Applying SEQUAL on Big Data Characteristics

On a high level, we can position the big data characteristics described in section 2 in relation to SEQUAL data quality framework in the following way: Physical quality: Volume is particularly relevant on this level, since it can be hard to have access to all relevant data at the same time. That it is the right (most current) data that is accessible is influenced by the velocity of data change. Supporting provenance, it might also be necessary to store the full chain of the data revisions, and not only the last version. In general, provenance meta-data should be represented independent on the technologies used for data storage, e.g. by using PROV 3 . An area that is not so much discussed in the big data literature is aspects of security, although in particular the use of Big Data-oriented techniques on personal data is rife with privacy-challenges. If people are more aware of this, potentially more people will make it more difficult for those working with Big Data techniques to get access to all the data that is of interest, indicating a need to also be open on how Big Data (e.g. location data) is meant to be used in a preferably anonymous manner in doing analysis [START_REF] Biczok | Navigating Mazemap: Indoor human mobility, spatio-logical ties and future potential[END_REF]. Empirical quality: Visualization of data is typically done on the basis of guidelines from cognitive psychology, and applying these is a good approach for achieving pragmatic quality (i.e. that people understand what the accumulated data mean). Note that guidelines for aesthetics are partly incompatible, and one has to make choices based on the usage and interpreters of the representation. In connection to maps [START_REF] Shekhar | Encyclopedia of GIS[END_REF] states that "different combinations, amounts of application, and different orderings of these techniques can produce different yet aesthetically acceptable solutions". Since the data visualization often must be auto-generated (to address issues of velocity), aspects described under this level is even more important for pragmatic quality than for traditional models developed mostly manually by human modelers. Syntactic quality: Variety comes into play here, since not all data sources have a strictly defined meta-model with a pre-defined syntax. This means that to match the different data-sources, certain presumptions have to be made on the structure and contents of data, i.e. one need to instil structure if it is not there, and in some cases meaning (see semantic quality) to data based on at best qualified guesses. Semantic quality: Whereas traditional data quality aspects such as completeness, accuracy and consistency are not covered specifically in Big Data -literature, the area veracity points to data quality more generally. A reason for the wanted variety is to ensure completeness since not all relevant data is to be found in one data source. Variety on the other hand brings traditional challenges in data integration quality [START_REF] Martin | A Methodology and Architecture Embedding Quality Assessment in Data Integration[END_REF] matching data on different level of abstraction and preciseness. When data is retained from sensor networks, one might experience issues of redundancy (e.g. reporting location every second from an object that is not moving). Such redundancies should be filtered out as should erroneous readings due to noise, e.g. indicating than an object suddenly moved a large distance in a short time, and this filtering should be done in the right order. To avoid issues of poor physical quality, one might often abstract the data, in which case it is important that the resulting dataset keep the important characteristics of the original dataset [START_REF] Wad | QoS: Quality Driven Data Abstraction for Large Databases[END_REF]. This points to an interesting side of big data not experienced in traditional modeling and data representations, namely that the modeling is partly done by algorithms, and not only be humans. Pragmatic quality: Relates both to machine understanding of data sources, and of human understanding of the results. As for machine understanding, the issues here is very different for different types of data (e.g. between structured and unstructured data such as text, video etc.). When it comes to human understanding of the results, this is primarily supported by taking empirical quality into account when devising visualizations of the data. Another approach that can be used is to provide personalized output, in which case it might be important to make the user-model used in the personalization controllable for the user [START_REF] Asif | Externalization of User Model in Mobile Services[END_REF] Social quality: Provenance relating to the trustworthiness of the source as part of veracity is central at this level. Also in combination with variety (including data coming from a variety of sources that is evolving in an uncoordinated fashion by autonomous agents constituting parts of a digital ecosystem), we get apparently new issues, since some sources might be more trustworthy than other. Also internally in organizations this might be an issue, e.g. matching personal data in spreadsheets, with data from enterprise systems such as a PLM-system [START_REF] Krogstie | Evaluating Data Quality for Integration of Data Sources Proceedings PoEM[END_REF]. Since these sources are in the same organization though, the possibility to enforce compliance is larger than in a Big Data setting. Due to velocity aspects, one might need to quickly and automatically deduce a trust-level using a trust model [START_REF] Artz | A survey of trust in computer science and the semantic web[END_REF] based on existing meta-data on the datasource. Deontic quality is closely related to what is meant under the point value, are we able to utilize the data for our purpose? Viability is a sub-point of this relating to the discussion of feasible quality in SEQUAL. Based on the goal of data-use, and also partly dependent on data sources to be matched, different weight might be put on different quality levels. A framework for personalization of big data quality deliberations is found in [START_REF] Embury | Incorporating domain-specific information quality constraints into database queries[END_REF], using scientific data as a case.

Conclusions and Further Work

We observe on a high level that the V-characteristics of big data can be mapped and understood relative to the levels of model and data quality described in the SEQUAL framework, although the mapping is not 1-to-1. The focus areas of big data do not describe to the same detail though many of the aspects found relevant in work of information and data quality. Issues with variety in Big Data can often be related to issues found in data integration quality in general, but we have also identified new issues from Big Data research when combining the effect of several V-s.

We notice that the treatment of quality in the Big Data area is so far relatively shallow. A first step is to a larger extent describe quality of Big Data relative to traditional data quality. Future work on our general approach on data quality will be to device more concrete guidelines and metrics and evaluate the adaptation and use of these empirically in case studies, also studies dealing with big data -issue e.g. within the media domain. An important aspects is how to perform trade-offs between the different data quality types. Some generic guidelines for this exist in SEQUAL [START_REF] Krogstie | Model-based development and evolution of information systems: A quality approach[END_REF], which might be specialised for data quality. We will also look at newer work [START_REF] Batini | Methodologies for data quality assessment and improvement[END_REF][START_REF] Jiang | Measuring and Comparing Effectiveness of Data Quality Techniques[END_REF] in the area in addition to the ones we have mapped so far. Due to the rapid changes to big data compared to conceptual models indicates that guidelines for achieving and keeping model quality might need to be further adapted to be useful when achieving and keeping quality of big data ecosystems, especially since a lot of what is normally modelled by humans in a big data scenario are modelled through the use of complex algorithms.

http://www.ibmbigdatahub.com/infographic/four-vs-big-data

http://www.pros.com/big-vs-big-data/

http://www.w3.org/TR/prov-dm/


John Effah
email: jeffah@ug.edu.gh

Virtual Process Control Modelling in Organisational Semiotics: A Case of Higher Education Admission

Keywords: organisational semiotics, business processes, process control, process modelling, virtual processes, WebML

This study explores Web-based virtual process control modelling based on organisational semiotics, Web modelling language (WebML), and higher education admission process. Despite some discussions on control activities in the organisational semiotics literature, less attention has been paid to process control modelling in general and Web-based virtual processes in particular. Process controls help to implement required organisational constraints as regulatory norms that enforce established rules, procedures, and standards for meeting intended organisational goals. This study contributes to organisational semiotics research by extending the discourse on control norms to the realm of Web-based virtual process modelling in a real life situation.

Introduction

This study draws from the theory of organisational semiotics and Web modelling language (WebML) to model virtual process controls based on a case study of a university's admission process. Business processes consist of related activities performed to achieve organisational goals. Processes can be physical or virtual. Whereas physical processes get performed through direct, co-located interaction between agents, virtual processes remove such direct, physical contacts and replace them with mediated remote interactions [START_REF] Liu | Workflow modeling for virtual processes: an order-preserving process-view approach[END_REF][START_REF] Overby | Process Virtualization Theory and the Impact of Information Technology[END_REF]. For Web-based virtual processes, activities occur via the Internet [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF].

Within the organisational semiotics literature, three forms of activities are discussed, namely substantive, communication and control [START_REF] Liu | Semiotics in information systems engineering[END_REF]. While much attention has been paid to substantive norms in business process modelling [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF], relatively less research has focused on control norms, especially in virtual environments. Process controls are embedded constraints that serve as conditions for performing related activities [START_REF] Brambilla | Process modeling in Web applications[END_REF]. However, the increasing migration of physical activities to virtual environments calls for urgent research attention to virtual process control modelling. Therefore, this study employs organisational semiotics [START_REF] Liu | Semiotics in information systems engineering[END_REF] and WebML [START_REF] Brambilla | Process modeling in Web applications[END_REF][START_REF] Brambilla | Web Engineering: Modelling and Implementing Web Applications[END_REF][START_REF] Ceri | Web Modeling Language (WebML): a modeling language for designing Web sites[END_REF] to explore virtual process control modelling in a Web-based higher education admission environment.

The rest of the paper is organised as follows. Section 2 reviews related works on virtual processes, process controls and business process modelling. Section 3 discusses the relevant organisational semiotics models used for the study. Section 4 uses a university's postgraduate admission process as a case study to illustrate a Webbased virtual process model embedded with controls. Section 5 concludes the paper with a discussion on its contribution and recommendations for future research.

Related Works

Virtual Processes

Organisational processes consist of related activities performed to achieve intended goals. Processes can be physical or virtual [START_REF] Overby | Process Virtualization Theory and the Impact of Information Technology[END_REF]. Whereas physical processes require direct contact between agents, virtual processes involve remote interaction [START_REF] Overby | Process Virtualization Theory and the Impact of Information Technology[END_REF] through a mediating technology such as the Internet. The mediating technology can be manual such the traditional postal system or Web-based. The focus of the current study is on Web-based virtual process.

Process virtualization has begun to receive attention in information systems research. However, the focus has been on theorizing and testing for which activities can or cannot be virtualized. Less attention has been paid to process modelling in general and controls in particular. [START_REF] Liu | Workflow modeling for virtual processes: an order-preserving process-view approach[END_REF] and [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF] are exceptions that focused mainly on substantive activities, hence the focus of this study on process controls.

Process Control

Organisations institute process controls to ensure that activities are performed in accordance with prescribed norms [START_REF] Schaefer | Control Patterns -Bridging The Gap Between Is Controls And BPM[END_REF]. For physical processes, controls are recorded in documents and are expected to be enforced by human agents. However, process virtualization offers an opportunity for their automation and delegation of enforcement to machine agents such as the Internet and Web applications. For Web-based information systems, a natural means for enforcing user control is through hyperlink navigation [START_REF] Brambilla | Process modeling in Web applications[END_REF]. However, Web-based agents offer opportunities for process control enforcement beyond navigation. The WebML if condition notation shown in Fig. 1 offers an opportunity for specifying control activities beyond navigation. The if condition notation determines what can or cannot be done following a true/false conditional result [START_REF] Brambilla | Process modeling in Web applications[END_REF]. Control norm has been extensively discussed in the organisational semiotics literature but less attention has been paid to its modelling.

Therefore the if condition notation offers an opportunity for control modelling as discussed in Section 4.

Business Process Modelling

Business process modelling involves the use of symbolic notations to represent organisational activities and their relationships, often in a graphical format [START_REF] Jaffar | Semiotic notation principles for business process modelling[END_REF]. A typical business process model graphically displays related activities and related controls [START_REF] Recker | Business Process Modeling-A Comparative Analysis[END_REF]. Process models therefore graphically display activities and their dependencies to define order of relationships [START_REF] Liu | Workflow modeling for virtual processes: an order-preserving process-view approach[END_REF].

By using symbolic notations and graphical display, business process models help to communicate and provide knowledge on existing or planned organisational activities and their relationships [START_REF] Jaffar | Semiotic notation principles for business process modelling[END_REF]. Business process modelling is therefore expected to be a significant precursor to any business improvement initiative. In line with this expectation, this study employs organisational semiotics and WebML symbolic notations for process control modelling to support process improvement in a higher education admission system.

Organisational Semiotics

Semiotics refers to the doctrine of signs and their interpretations [START_REF] Stamper | Organisational Morphology in Re-engineering[END_REF]. Based on this perspective, organisational semiotics views organisations as information systems where agents create and use signs to perform purposeful activities [START_REF] Liu | Semiotics in information systems engineering[END_REF] that are controlled by norms [START_REF] Stamper | Organisational Morphology in Re-engineering[END_REF]. Business processes are conceived as behavioural activities controlled by norms as rules and regulations that determine which activities are permitted, mandated or prohibited [START_REF] Liu | Semiotics in information systems engineering[END_REF][START_REF] Ali | A conceptual framework for role-based knowledge profiling using semiotics approach[END_REF] and which agents can or cannot perform such activities [START_REF] Li | Integrated clinical pathway management for medical quality improvementbased on a semiotically inspired systems architecture[END_REF]. This study draws from three organisational semiotics models: organisational onion, organisational morphology, and norm analysis method.

Organisational Onion

Organisational onion considers activities as a taxonomy of norms classified into three layers of informal, formal and technical [START_REF] Liu | Semiotics in information systems engineering[END_REF] as shown in Fig. 3.

Fig. 3. The organisational onion [4]

Informal norms such as cultural values are implicit and thus not expressly documented. However, formal norms constitute documented bureaucratic procedures and guidelines. Finally, technical norms are formal norms that have been digitalized as part of computer-based systems. The three layers are however not distinct. The technical is part of the formal, which together with the informal form part of the total organisation [START_REF] Liu | Semiotics in information systems engineering[END_REF]. Norms can also be transformed from one layer to another. Thus, informal norms can be documented to become formal, while formal norms can be computerized or virtualized to become technical. In this study, the technical layer constitutes the virtual environment. Therefore, process virtualization refers to transforming physical processes from the formal and informal layers into virtual processes in the technical layer.

Organisational Morphology

Organisational morphology adopts a functional view of norms and classifies them into substantive, communication and control [START_REF] Stamper | Organisational Morphology in Re-engineering[END_REF][START_REF] Ali | A conceptual framework for role-based knowledge profiling using semiotics approach[END_REF][START_REF] Tan | An Organisational Semiotics Inspired Information Architecture: Pervasive Healthcare as a Case Study[END_REF]. First, substantive norms govern the core activities performed to achieve the basic organisational goals. Second, communication norms concern the passing of messages between agents, such as reminders, inquiries, announcements and orders concerning facts, procedures and instructions. Finally, control procedures are meant to monitor and regulate substantive and communication activities based on expected standards, values or conventions. Control activities may also sanction, reward or punish agents. As shown in Fig. 4, each of the three activities can be further extended into sub-activities following a nested tree structure.

Fig. 4. Organisational Morphology [11]

The level of detail however depends on the context of the organisation or process [START_REF] Stamper | Organisational Morphology in Re-engineering[END_REF]. Traditionally, all the three types of activities were performed through direct, physical interactions. However, in the modern digital economic environment, some of these activities are being migrated online as virtual activities. As noted above, [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF] focused on modelling the virtualization of substantive activities but paid less attention to control norms. The current study therefore focuses on virtualizing control activities as demonstrated below.

Norm Analysis Method

Norm analysis method [START_REF] Ali | A conceptual framework for role-based knowledge profiling using semiotics approach[END_REF] helps to capture details of essential norms for specifying information needs [START_REF] Stamper | Organisational Morphology in Re-engineering[END_REF]. The basic format for behavioural norm specification is as follows:

whenever <context> if <condition> then <agent> is <deontic operator> to <action>
In order to accommodate process specification and their dependencies, [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF] extended the basic format to include predecessor and successor activities as follows:

<predecessor> <basic norm format> <successor> However, their extended model focused mainly on substantive activities and not on communication and control activities. While the basic norm structure is considered sufficient for communication activity, the current study further extends it for control activities with an else clause as follows.

whenever <condition> if <state> then <agent> is <deontic operator>to <substantive/communication norm> else <control norm> This extended norm structure was used to define the specification for control norms of the virtual admission process in Table 1 below.

Case Study: Virtual Admission Control Modelling

The study is based on the postgraduate admission process of the University of Ghana. University of Ghana was established in 1948 and remains one of the oldest and leading universities in Africa. In 2011, the first author initiated an action case study to virtualize the existing postgraduate admission process. In 2013, the system was extended to include process control. The current study is an extension [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF], which focused mainly on process modelling without controls. This study extends the virtual process modelling to include control norms. System and user specifications were elicited by the author through participant observation, interviews, meetings, and focus group discussions with stakeholders.

Norm Specification

The admission process involves applicants completing online forms, uploading supporting documents and submitting them online. The web application then invites referees for reference reports. Once the references have been received, relevant departments assess each application and select recommend successful ones for admission subject to general requirements and vacancy. The admission office then vets all selected applicants based on the university requirements and notify them of the outcome. Informing the applicants ends the admission process. Decision to enrol forms part of the registration process.

With control norm specification architecture introduced in Section 3.3, Table 1 presents specifications for the substantive, communication and control norms identified from the case study.

WebML Notations

WebML [START_REF] Ceri | Web Modeling Language (WebML): a modeling language for designing Web sites[END_REF][START_REF] Ceri | Designing Data-Intensive Web Applications[END_REF] is a visual modelling language that supports data and process driven web application design and development [START_REF] Brambilla | Process modeling in Web applications[END_REF]. It supports both data and hypertext modelling. This study however focuses on the hypertext model to illustrate the implementation of virtual process control based on substantive, communication and control norms. Table 2 shows relevant WebML notations for this study categorised into content, operation and navigation units.

Content units publish or accept data and therefore appear on web page. However, operation units manipulate or send data/information as backend activities. Such units do not therefore appear on web pages. Arrows are used to indicate links. While content units publish or accept data and are therefore displayed on webpages to be manipulated by human agents, operation units are hidden activities that are performed by machine agents. For detailed explanation and other notations see [START_REF] Ceri | Web Modeling Language (WebML): a modeling language for designing Web sites[END_REF][START_REF] Ceri | Designing Data-Intensive Web Applications[END_REF]. In the next section, the create and modify units are used to implement substantive activities; the If unit and the communication unit are used to implement control and communication activities respectively. The content units are useful for accepting/publishing data on web pages. The navigation unit can be used to show direction as well as message passing.

Hypertext Model

WebML notations were used for the hypertext modelling of the virtual admission process control system. Fig. 4 presents the model and illustrates an instance of the application life cycle through the virtual environment based on the norm specifications for process controls shown in Table 1 and the hypertext notations in Table 2.

As shown in Fig. 4, the admission process begins when an applicant visits the application page to view the lists of available programmes presented by the selection programmes index unit. Once a programme is selected, the applicant proceeds to complete the online application form and upload all supporting documents through the submit application data entry unit on the application page.

An instance of the application is then created as represented by the create unit. The system also invites referees to present reports on the application. After which the system informs the relevant department to assess the application. Once the application is created, the system sends e-mail to referees requesting them to submit their reports. Referees then access the reference forms to complete the reports which are then linked to the application. Following the implementation of the virtual admission process with embedded controls, the university's post-graduate admission process has become more efficient and responsive. Before the virtualization of the process controls, the enforcement of the controls were executed manually by human agents. However, by embedding them in the Web-based admission system, the responsibility has been transferred from human to machine agents, which are more efficient at ensuring compliance and enforcing conditions in the virtual process. Previous situations that relied on human memory often resulted in errors and failures to check the controls. However, the machine agent compliance is generally assured once the system has been tried and tested.

Conclusion

This study employed organisational semiotics and WebML for virtual process control modelling. The study contributes to organisational semiotics research, which has so far focused more on substantive norm modelling and less control norm modelling. Following [START_REF] Effah | Virtual Process Modelling Informed by Organisational Semiotics: A Case of Higher Education Admission[END_REF], who employed organisational semiotics and WebML notations to propose a semiotic-based process specification architecture for substantive norms in virtual environments, the current paper extends the architecture to include an if…then…else clause that caters for control norm specification.

This study demonstrates the applicability of organisational semiotics and WebML notations to virtual process with control modelling. The resultant model demonstrates how control norms can be implemented in organisational semiotics to enforce normbased conditions in Web information systems. In terms of contribution to practice, the proposed approach can be applied to organisational domains where process controls are critical such as in hospitals, logistics and inventory management.

A key limitation of the study is the idiosyncratic nature of virtual process control across institutions and contexts. The proposed model is therefore expected to be adapted to rather than adopted for specific contexts. Future research can extend the model beyond the higher education admission context to other process control-based domains such as banking, accounting and health systems.

Fig. 1 :

 1 Fig. 1: If Condition Notation[START_REF] Brambilla | Process modeling in Web applications[END_REF]

Fig. 4 :

 4 Fig. 4: Hypertext Model for Virtual Process Control

Table 1 .

 1 Norm Specification for Virtual Admission Process with Controls

	Activity	Predecessor Specification	Norm Type	Successor
	Application	<open	WHENEVER <Admission is	Substantive	Reference
		admission>	open>		request
			IF <applicant likes a programme on
			offer>
			THEN <applicant>
			IS <may>
			To <select the programme> and <
			complete the online form>and
			<submit the online form>
	Reference	Application	WHENEVER <applicant submits a	Communication	Reference
	request		form>		submission
			THEN <Web-based admission
			system>
			IS <should>
			TO <request for references>
	Reference	Reference	WHENEVER <a reference report	Substantive	Selection
	submission	request	is received>	.control
			IF <All required references are
			received>
			THEN < Web-based admission
			system>
			IS <should>
			TO <link references to relevant
			application>
			ELSE <keep application on hold>
	Selection	Reference	WHENEVER <department	Substantive	Vetting
		submission	accesses an application>	.control
			IF <if applicant qualifies>
			THEN <Department Head>
			IS <should>
			TO <select applicant>
			ELSE <reject applicant> AND
			<inform rejected application>
	Vetting	Selection	WHENEVER <applicant is	Substantive	Admission
			selected by department>
			THEN <Admission office>
			IS <should>
			TO <vet selected application>
	Admission	Vetting	WHENEVER <application is	Substantive	<enrolment>
			vetted>	.control
			IF<if applicant meets all	.communication
			department and university
			requirements>
			THEN <admission officer>
			IS <should>
			TO <admit applicant>and<submit
			admission letter>
			ELSE<reject
			application>AND<inform
			applicant and relevant department>

Table 2 .

 2 Relevant WebML Notations

	Content Units
	Page Unit	Data Unit	Entry Unit	List Unit	Navigation link

Page

Entity [

conditions] Data unit Entry unit Index unit Entity [conditions]

	Control Units
	Create Unit	Modify Unit	If Unit	Communication	Link Unit
						Unit
	Create	Modify		Connect
						SendMail
		Entity
	Entity	[Conditions]		Relationship
	<param := value>	<param	:=	value>


Kecheng Liu
email: k.liu@henley.ac.uk

Shixiong Liu
email: shixiong.liu@pgr.reading.ac.uk

Weizi Li
email: weizi.li@henley.ac.uk

Assessing Pragmatic Interoperability for Process Alignment in Collaborative Working Environment

Keywords: Pragmatic Interoperability, Process Alignment, Collaborative Working Environment, Interoperability Assessment

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Process alignment in collaborative working environment is a complex domain due to human and technical factors and multi-stakeholder involved, and multidisciplinary nature of the problems. Organisation is such a complex area where business processes and functions require multi-systems collaborations, and highly relies on effective information communications among departments, and therefore faces many challenges regarding pragmatic interoperability such as information collision, policy obstacles, and procedure mismanagement. Our previous study has developed the semiotic interoperability framework [START_REF] Li | Semiotic interoperability -a critical step towards systems integration[END_REF] [START_REF] Liu | Evaluation frameworks for information systems integration: from a semiotic lens[END_REF] and several methods for measuring interoperability at different organisational levels [3][4]. This paper is an extension of our previous study on pragmatic interoperability assessment for process alignment. In this study, we conduct four case studies in industrial companies and hospitals in order to gather their viewpoints regarding the concerns when condensing process alignment in a collaborative working environment. Used techniques include interview, observation, and documentation. The collected results firstly are summarised into three layers based on our previous developed pragmatic assessment model, and then are transformed into the elements which constitutes the purposed method, and finally based on the summarised results we purpose a method for assessing pragmatic interoperability for process alignment in collaborative working environment. The method contains two parts: one gives all the elements of pragmatic interoperability that should be concerned when considering process alignment in collaborative working environment, and the other one is a supplementary method for dealing with technical concerns.

Background

In the study of the interoperability, most of the work focuses on discussion at a technical level. Although some of them have extended to deal with semantics, a very limited number of publications elaborate the interoperability at the pragmatic level [START_REF] Liu | Evaluation frameworks for information systems integration: from a semiotic lens[END_REF]. Undoubtedly the research on technical and semantic interoperability can help establish a better understanding of data exchange and data interpretation, as well as leading to the development of supporting technologies and standards. However, the collaboration of different working processes require an assessment of pragmatic interoperability that ensures supported process can act upon the semantic information in order to deal with the complexity. Before defining pragmatic interoperability, our previous work has purposed the concept of semiotic interoperability [START_REF] Li | Semiotic interoperability -a critical step towards systems integration[END_REF], and has applied the concept in healthcare domain for analysing interoperability of collaboration at radiology department [START_REF] Liu | Pragmatic Oriented Data Interoperability for Smart Healthcare Information Systems[END_REF]. The concept of semiotic interoperability is based on the semiotic framework [START_REF] Filipe | Normative Organisational Modelling using Intelligent Multi-Agent Systems[END_REF]- [START_REF] Stamper | Information in business and administrative systems[END_REF] that explains all aspects of how signs can be used and communicated for successful communication. Using the concept allows different parties/processes/organisations to work together through communication with insight into six levels: physical, empirical, syntactical, semantic, pragmatic, and social. At the pragmatic level, the interoperability is concerned with the relationship between signs and the potential behaviour and intention of responsible processes. Thus we defined the pragmatic interoperability as a level concerning the aggregation and optimisation of various business processes for achieving intended purposes of different information systems. In addition to our definition of pragmatic interoperability, other researchers have also contributed. Benson [8] defines it as coordination of work processes across different people to enabling work collaboration. Sadeghi et al. [START_REF] Sadeghi | A mashup based framework for multi level healthcare interoperability[END_REF] state the pragmatic interoperability in healthcare is the ability among healthcare processes and various actors (i.e. healthcare providers and patients) that interact with information systems. Successful communication at this level is achieved if the hearer understands the speaker's intentions, which goes beyond the semantic interpretation of the communicative act. Interoperability is achieved at this level when processes serving different purposes under different contexts by different information systems can be composed to jointly support a common intention.

The study is mainly conducted through interviews and surveys in two industrial companies and two hospitals in China. One of the companies is an automobile manufacturer, and the other one is a software company that provides integration solutions for various building control systems. The background of the automobile manufacturer is that they have enabled automated information sharing among three parties: dealership repair facility, automobile rental companies, and themselves. The background of the software company is that they are currently running several integration projects which manly aim to enable information sharing among systems such as Building Management System (BMS), Energy Monitoring System (EMS), Invasion Surveillance System (ISS), etc. The background of the hospitals is that they tend to achieve information sharing among Radiology Information Systems (RIS), Electronic Health Record (EHR), and Picture Archiving and Communication Systems (PACS). The Radiology department provides diagnostic and interventional radiology for inpatients, outpatients and general practitioner referrals. Various healthcare services such as Computed Radiography (CR), Computed Tomography (CT), X-ray, and Interventional Radiology produce a huge amount of information regarding patient's healthcare delivery and clinical process. This study adopts a qualitative research approach including various techniques [START_REF] Yin | Case study research: Design and methods[END_REF]. During the data collection process, we reviewed the most up-to-date working documents published by the companies and hospitals, conducted an observation, and interviewed 31 experts in order to gather their viewpoints as inputs for developing the assessment method. Details of each technique are presented in following: Documentation & Literature:The documentation included literature review covering research publications, official reports, and working papers from the companies and hospitals. The research publications were sourced from several electronic databases in terms of information systems, process integration/alignment, and health informatics. The working papers were provided by the companies and hospital IT services department. Observations:We conducted one participant observation in working on several projects for the software company; and other three non-participant observations in the automobile manufacturer and hospitals focusing on their work processes, staff routine, individual activities, and technologies/techniques used for information sharing. Interviews:31 semi-structured interviews (summarised in table 1) were conducted with relevant stakeholders. The interviews lasted around 40 minutes and were on a one-to-one basis. The interviews were tape-recorded and later transcribed and rendered anonymously. The results of the interviews are summarised into categories which are used as key inputs afterwards for developing the pragmatics assessment framework.

After summarising all the key elements from interviews, this study also conducted surveys to all the interviewees asking them to scale how much each element would affect the process alignment based on their experiences. However, the results of the survey will be presented in future work.

Findings

As mentioned previously, this section presents the results from interviews, observations as well as literature reviews. The key concerns and barriers when considering working collaboratively in organisations are identified, and then each of the identified concerns are pinpointed into the Pragmatic Interoperability Measurement Model developed previously [START_REF] Liu | Pragmatic Oriented Data Interoperability for Smart Healthcare Information Systems[END_REF] as the key elements for assessment. This section firstly recaps the three layers of organisation (Informal, Formal, and Technical) which is used for setting the outline of all identified key concerns, and secondly these concerns are categorised more specifically into each element for developing assessment method based on the process of achieving intended purposes, at last some of the key elements are selected for discussion.

Our previous work purposed the pragmatic interoperability assessment model [START_REF] Liu | Evaluation frameworks for information systems integration: from a semiotic lens[END_REF] based on Organisational Onion [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF] defining three layers for understanding and capturing key concerns when considering process alignment and working collaboration. As depicted in Figure 1, informal layer contains aspects such as community, social norm, policy, and culture. Those aspects can be expanded to be different behaviour patterns of both organisations and individuals. The pragmatic interoperability is to align the different aspects in order to solve conflicts of cohesiveness. In formal layer, business functions and procedures play dominant role that specifies on how functions should be carried out and how tasks should be performed. The pragmatic interoperability is to align procedures and rules in order to achieve higher efficiency. It defines business goals, model business processes and brings the collaboration of administrations that aims to exchange information and have different internal structures and processes. Besides, this layer also supports the upper layer by addressing the requirements of the user community such as making services available, accessible, identifiable and useroriented. The technical layer mostly refers to the technical computer systems and their technical functions. The systems and functions can be programmed according to norms and procedures. The pragmatic interoperability does not directly refer to this layer but undoubtedly requires continuous support from it which is to align technical functions and business processes in order to achieve higher system productivity. It supports seamless sharing of data, which is automated sharing of data between information systems based on a common exchange model. It also covers the technical issues of linking computer systems and services. A few key aspects are included such as interconnection services, data integration, open interface, data presentation and exchange, and accessibility will be dealt within this level.

Based on the results from interviews, the following summaries a list of key concerns of pragmatic interoperability when considering processes alignment and working collaboration. They are split into three levels as discussed previously.  Informal To enable collaboration between different processes/systems/organisations, a shared intended purpose plays a key role that supports perceiving of personal beliefs and organisational ground rules, whereas an un-shared purpose may be considerable conflicts between the organisational level and personal level. Issues like restriction to staff behaviour, information collaboration (information channels alignment), varieties of purchased information systems (different venders and services providers), and privacy and security concerns should be solved in this level. Taking one example from one of the interviewed hospital, the informal level is to concern with the understanding of the healthcare, regulatory, legislative and healthcare environment in which information systems need to be deployed to support healthcare delivery. It requires agreement on key organisational concepts such as policies, processes and roles; it also captures relevant patterns such as compliance, governance, legislative and change management. There are other concerns summarised in table 2. Policy is one the most important and most widely mentioned key concerns in discussion of interoperability at informal level. From its perspective, collaboration always faces obstacles, because it cuts across political boundaries, and causes amount of changes (e.g. internal control process, work flow, staff relationships, communication patterns) in organisations [START_REF] Kim | An examination of factors for the strategic use of information systems in the healthcare industry[END_REF]. For instance, in the healthcare, before the integration of RIS, the political issues between clinicians and radiologists have been raised for long due to the autonomous role of clinician. Comparing with the radiologists, clinicians have the right to choose and take responsibility for their treatments made, and they have direct access to the policy making. The integration of RIS will enable the information sharing between the clinicians and the radiologists, so that patient's information and treatments made by clinicians will be transparent for the radiologists. However, it is challenged as clinicians have concerns that the information they collect and the treatments they made are proprietary and thus unavailable for inclusion in the process of information sharing. The integration of RIS also solves a political issue that clinicians who are geographically separated from the hospital were having conflicts for decision making. A quote from one clinician is: "… All decisions have to be made with support of our staff… the integration has a chance to work here only if the staff can see the benefits… "

In line with the policy concern, another one comes from the medical staff is that they are seeing the collaboration of working processes as restriction that would control their behaviour. They are more interested in research than administration, and they await more benefits to patient care delivery from the information systems integration, rather than the improvements of communication with the staff. This is the decisive factor that will let them accept the collaboration. Besides there are also concerns regarding culture, privacy, and security, but there are not as highlighted as the previous concerns, thus we will elaborate them in this paper.

 Formal

As discussed previously, to meeting shared purposes at formal level, organisations have a more specific procedure to follow. The procedure begins with setting up several intended objectives for the shared intended purpose, then allocates these objectives to relevant organisation roles, and signs contracts which define explicit tasks. The tasks relate to accountabilities for the completeness, quality, integrity and security of information that originates in one party and is transmitted to and used by another. Finally, the completed tasks with their deliverables are evaluated by matching the original defined intended objectives. Besides the elements of this procedure, there are other concerns captured from the result of interviews (table 3). To assess pragmatic interoperability, we should not only concern the information exchanged between technical systems, but also the knowledge of the context that the information exists within for each system or process. As quoted from one IT project managers, "…It is important to articulate the requirement for context awareness that process representation begins. The context of the target system should also be made available to the origin system. Key questions such as what process will first operate on the information at the target system once it receives it, and what state of preceded processes are should be concerned…" By understanding this context, the system engineer and integration designer can ensure pragmatic interoperability is addressed for the needs of process integration. To define the context, one manager of the logistics department said that "Assume that the context is about internal workings of the process, in other words, the initialization state, the end state, the nature of data transformations, and details about the timing of the process are all considered, so that the receiving process can make better use of information it receives. This information is in context, but it also shows the dynamic nature of that context to the receiving system, because it now has specific information about the dynamic context within the originating system." For example, context is seen as a demand for more information between the Model Manufacturing Demand Service and the Rental Fulfillment Service, so that a specific understanding of the models requested has a deeper meaning. This could be a specific based on the timing of the data, the initialization state of the Rental Fulfillment Service, and the data transformations. This gives a more dynamic picture of the context for the information being produced by one process for another, and allows for a deeper understanding of the meaning of that context. Another example in the healthcare environment explains the concern of different data sources. The patient-centred care delivery is tailored to patient and requires collaboration of several information systems, and other involved team members also require the data of patient such as status and treatment. To deliver care for an individual patient, various activities need to be collaborative and to keep updating the database in real time. However, this collaboration is challenged duo to the complexity, it requires multiple data sources integrated to support the different requirements. "… Originally, our IT environment operated with costly point-to-point interfaces, and we also lacked control of troubleshooting while the messages transmission failed… we operated within a manual environment where we needed to manually enter orders with their images received from outside reading facilities into our system, and had to fax copies back to the outside facilities…" quoted from one clinician in radiology department. Information communication is also challenged due to the varieties of purchased systems for hospital. Those systems were provided by several small venders, who remained their competitiveness by selling only one type of systems or focusing on one specific function. This results in that the stakeholders get frustrated more often because the communication of patient's data/information failed among various systems. As one clinician stated: "We became accustomed to telling our patients that we cannot provide the information for them immediately because their information cannot be accessed, although we have those information in the database… I normally have to download the hard copy and then manually re-input the data for my patient." Furthermore, information communication is also challenged because the clinicians and the radiologist were located in different rooms, and therefore the collaboration takes place asynchronously at most of the time, which can be solved via management channel.

 Technical

The purpose of technical level is to make exchanged data available for supporting the processes at upper level. For example, in healthcare environment, it is concerned with the understanding of technical functionality for supporting information systems. It requires agreement on a core set of technical concepts, such as technical components and devices, the interactions between components, interface and technical services; it also captures relevant patterns such as technical architecture styles and styles of component interactions. However, the study at this level is not the focus of the assessment of pragmatic interoperability, although it is vital to support the understanding the concerns of pragmatic interoperability at upper levels. Therefore, during the interview, only few concerns regarding technical level are summarised below, but we still purpose a method for dealing with those concerns in the next chapter.

Concern Explanation Semantic heterogeneity

Refers to the variation of semantic meaning in information resources which will lead to the semantic conflicts and complication for data integration Ontology structure

Approaches that employ ontologies for information systems Operation/system language The language using in both systems Ambiguous terminology Differences in the use of terms across departments Next chapter will purpose a method for assessing pragmatic interoperability based on the results collected from this chapter. The method contains two parts: one gives all the elements of pragmatic interoperability that should be concerned when considering process alignment and working collaboration, and the other one is a supplementary method for dealing with technical concerns.

A Method for Assessing Pragmatic Interoperability

The nature of information communication relies on successful signs communication [START_REF] Li | Semiotic interoperability -a critical step towards systems integration[END_REF], which must start with understanding and modelling the organisation where information communication exists [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF]. The organisation onion [START_REF] Stamper | Information in business and administrative systems[END_REF] stresses the distinctions as well as the interdependent links between the business process and IT systems. The organisation morphology provides a useful modelling method for understandings the norm structure of organisation. Each organisation can be characterised as a structure of norms that allow functions to be coordinated for certain purposes [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF], and pragmatic interoperability, is to enable the purposes of each process can be understood and perceived during collaboration. Based on the results collected from the interviews and observations presented in previous chapter, we purpose a method for assessing pragmatic interoperability which contains two parts: one gives all the elements of pragmatic interoperability that should be concerned when considering process alignment and working collaboration, and the other one is a supplementary method for dealing with technical concerns. In formal level, meeting shared intended purposes is the goal of achieving pragmatic interoperability when two process/organisations working collaboratively. The procedure has been identified based on the interview results. It begins with setting up several intended objectives for the shared intended purpose, then allocates these objectives to relevant organisation roles, and signs contracts which define explicit tasks. The tasks relate to accountabilities for the completeness, quality, integrity and security of information that originates in one party and is transmitted to and used by another. Finally, the completed tasks with their deliverables are evaluated by matching the original defined intended objectives. After semantic disambiguation, different ontologies are compared by using defined metrics in order to identify and refine the similarities. As the matching process using at this level is not the focus of this paper, thus the elaboration of the process itself as well as its application and validation will be presented in future work.

Conclusion and Future Work

This paper is the extension of our previous research. It proposed a method for assessing pragmatic interoperability for process alignment in collaborative working environment. The study is conducted qualitatively by using techniques of interview, documentation, and observation. The results collected firstly summarised into three layers based on our previous developed pragmatic assessment model, and then categorised them into elements which constitutes the purposed method. The method contains two parts: one gives all the elements of pragmatic interoperability that should be concerned when considering process alignment in collaborative working environment, and the other one is a supplementary method for dealing with technical concerns.

Validation and application for the purposed method will be presented in future work.

Fig. 1 .

 1 Fig.1. Pragmatic Assessment Model[START_REF] Liu | Assessing Pragmatic Interoperability of Information Systems from a Semiotic Perspective[END_REF]

Fig. 2 .

 2 Fig. 2. Summarised procedure for meeting intended purpose from interview

Figure 3

 3 Figure3depicts the method purposed for assessing pragmatic interoperability. In the informal level, two key elements are defined: community participation refers to what parties in what roles are eligible to participate and what are the prerequisites for their participation, and policies refer to those policies within each party's jurisdiction that influence the interoperability of the organisation. Organisation may encode business rules that are not explicitly specified but cause incompatibilities in exchanged information. Aligning policies across jurisdictional boundaries is one of the most difficult tasks of collaboration. In formal level, meeting shared intended purposes is the goal of achieving pragmatic interoperability when two process/organisations working collaboratively. The procedure has been identified based on the interview results. It begins with setting up several intended objectives for the shared intended purpose, then allocates these objectives to relevant organisation roles, and signs contracts which define explicit tasks. The tasks relate to accountabilities for the completeness, quality, integrity and security of information that originates in one party and is transmitted to and used by another. Finally, the completed tasks with their deliverables are evaluated by matching the original defined intended objectives.

Fig. 3 .

 3 Fig. 3. Method for Assessing Pragmatic InteroperabilityIn technical level, we purpose a matching process that deal with the concerns summarised from the interview results such as semantic heterogeneity, ontology structure, and ambiguous terminology. The process contains seven steps (class items extraction, elimination of irrelevant semantics, class grammatical type classification, class hierarchical disambiguation, class relational disambiguation, similarity identification, and the refinement). In the class items extraction phase, the goal is to represent all the classes of the ontology in the same form. It is to represent each class like a set of terms. The characters are executed without meaning as so punctuation marks or special characters. When dealing with issues like too many corresponding senses for each class term, the steps of grammatical type classification, hierarchical disambiguation, and relational disambiguation are used for articulating semantics of the information.

Table 1 .

 1 Summary of interviews

	Sectors	Sample			Years of experience
			0-5	6-10	11-15	16-20	20+
	Industry	22	7	11	2	1	1
	Hospitals	9	3	2	1	1	2
	Total	31	10	13	3	2	3

Table 2 .

 2 Other concerns at informal level summarised from interview results

	Concern	Explanation
	Culture issue	Tacit knowledge has not been explicitly stated and shared
	Ethical issue	Appropriateness of taking actions on healthcare service delivery
	Behavioural factor	Willingness to be open and to share
	Management style	Leadership style influencing the degree of willingness of collabo-
		ration
	Policy and procedure	Internal control process, work flow, staff relationships, communi-
		cation patterns, cut-across political boundaries, etc.
	Restriction to staff behav-	Staff's fear on integrated working process as restriction that might
	iour	control their behaviour
	Privacy and security	Sensitive information of patient to be protected by law

Table 3 .

 3 Other concerns at formal level summarised from interview results

	Concern	Explanation
	Organisation structure	Centralised, decentralised, hierarchical, matrix, networked, etc.
	Harmonized strategy	Aligned operations to be applicable on the strategic level
	Performance constraints	Fewer investment but more effective collaboration
	Cost constraints	Unexpected budget
	Data source interoperability Multiple data sources used for supporting process
	Context awareness	Knowledge of context of both collaborative parties/processes
	Varieties of purchased	Purchased systems from various venders with low capability
	systems

Table 4 .

 4 Summarised concerns at technical level from interview results