Service-based Knowledge Monitoring of Collaborative Environments for User-context Sensitive Enhancement Savas Ziplies1, Sebastian Scholze1, Dragan Stokic1, Karl Krone2 1
Institute for Applied Systems Technology Bremen GmbH, Wiener Str. 1, 28359 Bremen, Germany, {ziplies,scholze,dragan}@atb-bremen.de 2
OAS AG, Linzer Str. 7, 28359 Bremen, Germany,
[email protected]
Abstract This paper presents an approach to correlate user interactions with knowledge in a collaborative environment to enhance information recommendations given by as well as the environment itself. It premises that in a user-sensitive context the important information to use is coherent with the actions made by an individual as well as by the group. The following description shows how to utilize this monitorable information of interactions with existing systems in a collaborative environment to enrich and focus the recommendations of knowledge for the individual. The approach focuses on the implicit and explicit knowledge given by a user in a dynamic bi-directional feedback system that unobtrusively monitors the active and passive knowledge. The described process is supported by a modular serviceoriented architecture which allows docking onto different systems to monitor and analyze the user’s interactions and support the systems through monitoring data, based on configurable rules allowing adaptation to different scenarios. Keywords Networked Enterprise, Knowledge Monitoring, Collaborative Environment, Implicit and Explicit Feedback, SOA
1 Introduction Technologies focusing on the provision of user-specific knowledge to enable the user to better manage the current situation or problem are nowadays indispensable, especially in intraenterprise solutions. Employees need to quickly react and collaborate with different systems, users and information repositories but forfeit time when searching for appropriate knowledge. The main topic presented in this paper is to enable Knowledge Management Systems (KMS) to be enhanced by implicitly gathered user-context sensitive knowledge. The approach presented in this work proposes that a specific user-centred context can be evaluated without the need to explicitly express it, but by monitoring the Interaction Knowledge given by any user within a collaborative working environment in a Networked Enterprise. Through a set of service-oriented features monitoring different Collaborative Environments and harmonizing the user’s interaction with a system in a consequent process of mapping the implicit actions onto explicit expressions, Collaborative Working Environments gain the ability to enrich and present appropriate knowledge based on the user’s context. The presented approach is thereby elaborated in a collaborative application scenario depicting a practical Networked Enterprise.
2 Relation to existing theories and work Knowledge Management as well as ICT systems are a common foundation in today’s working environments [Weber, et. al. 2002] in Networked Enterprises. The systems comprehend collaborative intra-enterprise solutions and offer functionalities to utilize and communicate the knowledge stored and actively produced by users. They vary from file repositories to visually supported communication solutions, e.g. Web-Sites or Voice Chats. Several resources have
shown how integrated systems can efficiently enrich the collaborative work and resolve problems [Ergazakis, et. al. 2002]. The importance of collaboration within a working environment has been researched in several projects, e.g. [Correia 2008]. Most systems support the user through one comprehensive Collaborative Environment and allow for asynchronous/synchronous interaction with resources and other users to resolve specific situations. Especially the personal experience and therefore the single actor is hard to dismiss in these environments. The Interaction Knowledge given by a user defines the current activity and can be further resolved into the personal context. With the advent of Web2.0 and term-indexing, the management of such information has become more important [Wright 2005] and has risen to match collaboration environments and situations. Today’s socially engaging systems allow users to explicitly specify what information could be appropriate based on user schemata. Furthermore the system recommends knowledge accordingly and tries to simulate a user’s context, but based on the prior inputted information. More and more systems try to elaborate the probably most relevant information to present by gaining the implicit knowledge out of the user’s interactions with different systems. The previously explicitly expressed e.g. preferences, like a preferred movie genre, are tried to be derived by the interaction with the overall information. The most common example nowadays are recommender systems, which provide the user with possibly matching information based on monitored behaviour, most often clicks and views [Middleton, et. al. 2001]. A practical example of an in-use system is Amazon’s recommendation system, which offers other products the user could be interested in, based on the monitored browsing and viewing history [Linden, et. al. 2003]. A main constriction that follows most of these systems is the relevance resolution based on mainly click and view behaviour, often seen in Amazon’s system as a simple search over several different articles. But in the event of inappropriate results, which do not match the current preference, the recommendation system misinterprets the click as relevant. Also an inconvenience of many systems is to focus on “one” solution instead of fully integrating existing systems. In combination with the need to explicitly input the user preferences, much compatibility and usability is lost. The request to explicitly provide user “needs” is inconvenient because of the spent time for gained time disprofit. To resolve the need of specific standalone solutions, new systems are in research and development [Campos 2008]. These focus on the extension of existing systems by extracting the enhancement process in an external process loop, allowing it to utilize information of the legacy systems. This paper presents a solution on how to monitor the information needed to fill the reference architectural idea without the request to explicitly input user “needs”, but by matching the implicit interactions onto a standardized action matrix, comprehending all information for further knowledge enhancing.
3 Research Approach In correlation with the described problems, the presented solution elaborates a service-oriented approach, extending different collaborative legacy systems by docking onto through its monitoring services, and enhancing these into one enriched environment effectively supporting knowledge management within Networked Enterprises. The main focus lies on the implicit Knowledge Monitoring from these knowledge-provision systems. To ensure the reusability of different inputs, the monitored information is standardized by a common action matrix [Kelly, Teevan 2003]. This is exemplary shown by the direct monitoring of the user interaction with a Web-Interface but applicable to any other system (e.g. file systems with according action mapping).
3.1
Monitoring of User Interaction
The monitoring of the user’s interaction with a system in a collaborative environment is based on gathering the implicit actions and maps them onto a “sense-giving” matrix. Networked Enterprise
Monitoring
Parser
Monitoring Data
Analyzer
Figure 1: The Monitoring Process
The process separates into four main services (refer to Figure 1): The Monitoring Services dock onto collaborative legacy systems (e.g. user interfaces, file systems) and monitor the interactions made to the digital system. Hereby it gathers the user’s interaction with the knowledge and starts the process of processing and analyzing the possible meaning of the user’s actions. The Parser converts the data, depending on the source system and formats it to get machine readable data for the Analyzer to interpret. It matches the information onto the action matrix and adds the captured values on the keys to result in an interconnected graph of categorized keys and values. Furthermore the information is constructed into the standardized Monitoring Data matrix containing instanced information, based on the schemata for later processes to rely on. Resulting is a recurring cycle of Knowledge Monitoring that monitors and maps different sources of information on one user-sensitive action matrix.
3.2
Implicit and Explicit User Feedback
To interpret the information monitored from the user’s interaction within the collaborative environment, it is needed to map the actions onto the standardized matrix. Referring to the basis action matrix, the corresponding monitorable features are semi-automatically matched onto the structure. This mapping process is based on an a priori analysis of the information available from common systems. The mapping of user interaction without the need to force an actor to explicitly convey his information is based on the principles of implicit knowledge [Schanz 2006] in correlation with the events monitorable from legacy systems, e.g. [Zagorodnov 2006]. The importance of explicit user interests mapped onto implicit actions of Web-Applications as shown in [Claypool, et. al. 2001] is further standardized into the common action matrix and extended by the use of accessory resources and file systems as additional exemplary proof of matching implicit interpretation according to the relevance [Harman 1992]. The action matrix mentioned earlier in [Kelly, Teevan 2003] serves as a foundation for the map and monitorable user interactions. There, the actions have been separated into five Behaviour Categories and three Scopes, which are interpreted as a coned hierarchy of the actions according to the related objects. All actions in the matrix are atomic but may be interconnected to achieve a situational context by a graph representation. In extension to the classical and strict separation and grouping, the actions are further classified into explicit and implicit dimensions as proposed by [Claypool, et. al. 2001]. Claypool extends the system by a second dimension of characterization. This indicates “whether
the interest indication comes from a characterization of the structure or content of the item, such as the Web page layout or font colour”. For the monitoring of user interactions of different systems and to map the action matrix onto the two-dimensional coordinate system, the content characterization is switched to the actions applicable with the content as displayed in Figure 2. Active
Scroll
Click/Type
Explicit
Implicit
View/Listen
Dispose/Blur
Passive
Figure 2: Two-dimensional coordinate system of user Actions related to Content
The definitions used for the axes in Figure 2 are as follows:
Explicit defines a precise and clearly expressed kind of meaning/Information. Explicit information should be finite in the context of a user.
Implicit specifies an implied/interpreted kind of meaning/Information. Implicit information may never be finite.
Active or actively means the performing or causing of an Action.
Passive specifies the receiving or submitting of an Action without a direct relation. The actions are interpreted into this relational system and offer, in coherence with possibility of interconnected sets of actions, the user’s interaction context with information in the collaborative environment. Example actions from the matrix like Click/Type are self-explanatory and similar actions can be added to the set, as well as to “active implicit” and “passive explicit”. Important factors mostly unrecognized by systems are the “passive implicit” actions of a user. The example of Dispose/Blur represents e.g. the leaving of a whole website by discarding it or changing the active application. This action category incorporates important information for the system: such actions on a whole item complete the situation in which the monitored actions are interconnected and summed up, e.g. the leaving or moving from a webpage to another. Therefore it can provide informal knowledge about acceptance or irrelevance of information, but is therefore constrained to other actions like view time, clicks or scrolls as otherwise it would be undecidable. Hence this most implicit information monitorable off the user’s interaction with a system can also be related to another monitored system, e.g. utilizing the previously found information. The correlation of monitoring the passive user interaction with different systems and the resulting surplus is the integration of bi-directional Feedback Services, which allow the user as well as the system to interact. An example for a combinatory implicit and explicit feedback service is the TagCloud, commonly used in Web2.0 applications as a visually weighted map of terms [Hassan-Monteroa, Herrero-Solanaa 2006]. A bi-directional TagCloud integrates into a Web-Application and updates according to the monitored information (active implicit). By the utilization as e.g. recursive refinement from the system to the user and vice versa, the TagCloud gains the ability to combine the prior implicit information with possible explicit interactions, increasing the importance of implicit and explicit mappings in the user’s context, allowing the system to learn.
3.3
Service-oriented Monitoring Architecture
To support the premise of integrating and monitoring existing systems in a collaborative working environment to unobtrusively enhance the existing information with the user’s interaction knowledge, it is required to easily integrate and compose the needed monitoring services applicable to existing systems. Furthermore the services must comply with a common networked enterprise and therefore should base on a loosely coupled but orchestration capable architecture. A service-oriented approach has been chosen to comply with all these requirements of monitoring in a networked enterprise, as well as the need of a configurable and adaptable service composition. Hence the overall structure to comprehend the described solution is a Serviceoriented Monitoring Architecture (SoMA) (see Figure 3). The modularity of the SoMA allows easy adaptation to other external legacy systems through the Access Layer and the Generic Monitoring Services that can be extended by additional services, according to specific business cases. The same applies to the Parser and Analyzer which allow to be extended accordingly and in future works broadening the action matrix. All services rely on interchangeable interfaces inbetween to comply with the architecture and to apprehend the standardized Monitoring Data generation. Because of the loosely coupled structure of the services inside the layered architecture, all parts rely on an external XML configuration orchestrating the Monitoring, Parser and Analyzer process and configure the action and monitored data matrix. This offers a high compatibility and adaptability to different collaborative solutions and business cases. Networked Enterprise
Monitor Interface
Monitor File System
Monitor …
Access Layer
Generic Monitoring Services Process Event XML Config
Analyze Content
Identify Parser
Parse and Analyze Parse raw Data
App-specific Services Analyze Meta-Data
Build standardized “Monitored Data” Matrix
Figure 3: Service-oriented Monitoring Architecture (SoMA)
From the whole implemented SoMA, the most important focus lies on the Monitoring Services and according Analyzing Services developed. The Monitoring Services comprehend general solutions to monitor basic systems and parts of these like e.g. the DOM of a webpage or classic file systems as both are most often part of or a mediator foundation for collaborative environments. These basic services can then be further extended hence adapted to Applicationspecific services, which monitor either special system not accessible by common means or specify a generic service by e.g. templates referencing a specific structure. An example could be a generic website, specified through a template defining what content can be found in which location. These (specific) services are the foundation for the monitoring process (Figure 1) and further operated by the Parser and Analyzer. Especially the Analyzer serves as an element directly correlated to the monitored data. The Monitoring Services provide the access/interface to the systems and react to actions whereas the Analyzer gives “sense” to the monitored data in the overall action and content context of the user. It analyzes the action itself and puts it into context to the content it refers to (e.g. a whole webpage or a file). Furthermore it enriches the information with specific Metadata which can be extracted from the monitored source. For example a click on a component in a webpage may be extracted by a general or configured structure to gather all
knowledge related to the action. This process is applicable to several other systems in a collaborative working environment like e.g. a file system. The change of a file can be contextually interpreted by the location, name, date, time and several other configurable properties, besides the information about the interacting user. This modular and configurable combination of services which can be easily orchestrated to appropriate compositions is the main foundation of the SoMA and allows a high reusability between different application scenarios. Besides the modular composition to comply to different scenarios especially the Monitoring Services underlie a strict configuration to comprise organisation as well as national laws for Information Security and Privacy. By monitoring the user’s interaction, trying to elaborate his context, a set of critical knowledge is gathered. Therefore the configuration is accessible to every organisation and user to individually depict what information may be gathered. 3.3.1 Monitoring Data The important outcome of the SoMA comprising all described monitored knowledge of the user’s interaction is the so called “Monitoring Data” Matrix. It comprehends all data of the actions made and, if applicable, the content the action was made with. The information is timed and sequentially added into one data set, defining one situation. As interchangeable format of the information provided through the SoMA system the Extensible Metadata Platform was chosen [Adobe 2005]. The XML-based Metadata format comprises mainly the Meta-information of different file formats in its data model, but is able to be extended based on its modular structure. The main advantage is the use of the Resource Description Framework (RDF) and its XML-dialect to specify the data. It already correlates other RDF Metadata sets like the Dublin Core (DC) and is capable to be easily extended by other existing standards like e.g. Friend-of-a-Friend (FOAF) as well as the described interconnected action matrix. This offers the description of the action matrix in relation to the coordinate system (Figure 2) in a commonly used and meaningful format, supporting the modelling of entity and concept relationships in possible triples. With many tools for querying and utilizing RDF/S or OWL files it supports easy integration into existing data structures for creation, alter and search operations, and may therefore be used by different existing systems without the need to establish substantial new processes. An exemplary output for a possible website monitoring would contain a reference ns:action with an id according to an action and the monitored information for it. This may be further subbed through e.g. rdfs:subClassOf and all other applicable descriptive elements supported.
4 Findings The presented work extends collaborative KMS by a monitoring solution gathering the user’s interaction with the systems to correlate a contextual profile. The solution is especially aimed at SME driven Networked Enterprises. To fit the needs of SMEs, which have to rely on an agile and changing working structure, the presented work elaborated the following results, which are currently examined within several SMEs:
A methodology and matrix to map the user’s interaction with different systems onto a standardized action matrix, representing the implicit context-sensitive interaction, providing explicit descriptions for further enhancement.
The Service-oriented Monitoring Architecture, which allows existing solutions to benefit from knowledge enhancements, is easy to adapt onto different collaborative software systems and extendable by other monitoring, parsing or analyzing services.
Monitoring services able to gather the user information from Web-Applications and common file systems integrated into the presented architecture and convertible onto generic enterprise use cases.
The innovative platform is applied and constantly verified in a real extended scenario serving as a feature concept elaboration for the modularity of the SoMA as well as the foundation for the general and application-specific Monitoring services, Parser and Analyzer. The application scenario depicts a classic collaborative working environment with several knowledge systems offering monitorable user knowledge to enhance the information. This also allows testing and verifying the user behaviour in terms of Information Security and Privacy and to what extend interaction knowledge may be monitored. This particular scenario offers several systems providing the information employees in the enterprise work with. They comprehend file systems, web interfaces, archiving software and databases. Employees are depicted as users by a central server which is utilized by the SoMA as identification foundation. The architecture docks onto the several different knowledge management systems without specific client dependent software installations and therefore represents an unobtrusive way of monitoring the daily work process in the networked enterprise. In its basic system the SoMA currently provides the Monitoring Services for file systems and web interfaces based on common communication formats in-between as well as Ajax-based solutions to capture and unobtrusively submit the monitored information. Especially web applications are applicable to be monitored by the modular JavaScript foundation library. The monitoring is supported by a set of Parser and Analyzer comprehending all common formats applicable in a classic enterprise scenario like e.g. word processor (e.g. Microsoft Word, Adobe PDF) as well as XHTML-compliant services to interpret the content of a webpage. An important factor to retrieve the user’s context based on the monitored information is the correlation of the systems. As every user interaction is marked with a timestamp of its occurrence it is possible to recreate an action flow. An application scenario for example:
The user searches for information through a monitored web application o The query, the results and all selected results are monitored as well as the implicit and passive information about e.g. scrolling and viewing time.
The user blurs the web application by changing the task to his file explorer o He selects a file and opens the file o The desired information is noted and minor changes are made to the file
The monitored file system, comprising the file the user accessed, notices the changed file and processes it as altered file o The file is processed (parsed and analyzed) o The constructed Monitoring Data is inside the SoMA compared to the users previous interactions, hence the querying o The resulting matches (e.g. same files) are marked as more important for the user
The current user context is enriched by the collected matching information as well as by the query and the content of the document as it can depict information for later processes This identified and assessed interconnected monitoring and analyzing based on the modular SoMA provides the capabilities to include most common collaborative working environments. It is achieved with the innovative monitoring services of existing collaboration systems which have not been used in such an unobtrusive way, but try to ding new systems onto the user.
5 Conclusions The presented approach addresses the correlation of implicit user feedback with the knowledge present in a collaborative working environment, based on the users’ interaction with it, as it has not been done before. These actions are interpreted to express the possible user intention, their context based on implicit knowledge monitored. This allows an enhancement of information in a
collaborative system without the need to explicitly ask the user for their knowledge or information ratings. It lowers the acceptance threshold most systems struggle with as it serves as an unobtrusive extension to existing systems and does not need the “expensive” introduction of new structures or processes. Based on a service-oriented approach to ensure compatibility and interoperability, the solution offers an innovative platform, enhancing legacy systems by Knowledge Monitoring and enables subsequent systems to utilize the monitored user-context and enhance the information/knowledge provided to the users in the Networked Enterprise. Although the presented results are applied and tested in the SME manufacturing domain, they can also be applied in larger companies as well as in other domains based on the modular architecture and the configuration ability which allows the inclusion of case-specifics. Acknowledgement This work has been partly funded by the European Commission through ICT Project K-NET: Services for Context Sensitive Enhancing of Knowledge in Networked Enterprises (No. FP7-ICT-1-215584). The authors wish to acknowledge the Commissions support. We also wish to acknowledge our gratitude and appreciation to all K-NET project partners for their contribution during the development of various ideas and concepts presented in this paper. Disclaimer This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content. References Adobe Systems Incorporated (2005) XMP Specification, San Jose, CA, http://partners.adobe.com/public/developer/en/xmp/sdk/XMPspecification.pdf Campos, A. et. al. (2008) Services for context sensitive enhancing of knowledge in networked enterprises, Proceedings of the 14th International Conference on Concurrent Enterprising 2008, Lisbon, Portugal Claypool, M. et. al. (2001) Inferring User Interest. IEEE Internet Computing, Vol. 5 (6) 2001, pp. 32-39 Correia, A. et. al. (2008) Collaborative environment for intelligent monitoring in manufacturing industry. Proceedings of the 14th International Conference on Concurrent Enterprising 2008, Lisbon, Portugal Ergazakis, K. et. al. (2002) Knowledge Management in Enterprises: A Research Agenda. Lecture Notes in Artificial Intelligence 2569. pp. 37-48, Springer Harman, D. (1992) Relevance Feedback Revisited. Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval 1992, pp. 1-10, Copenhagen, Denmark Hassan-Monteroa, Y.; Herrero-Solanaa, V. (2006) Improving Tag-Clouds as Visual Information Retrieval Interfaces. I International Conference on Multidisciplinary Information Sciences and Technologies 2006. Spain Kelly, D.; Teevan, J. (2003) Implicit Feedback for Inferring User Preferences: A Bibliography. SIGIR Forum, Vol. 37 (2), pp. 18-28 Linden, G.; Smith, B.; York, J. (2003) Amazon.com Recommendations: Item-to-Item Collaborative Filtering. In Internet Computing, IEEE, Volume 7, Issue 1, pp. 76 – 80 Middleton, S. E.; Roure D. D.; Shadbolt, N. R. (2001) Capturing Knowledge of User Preferences: ontologies on recommender systems, In Proceedings of the First International Conference on Knowledge Capture, pp. 100107 Schanz, G. (2006) Implizites Wissen. Rainer Hampp Verlag, Mering Weber, F. et. al. (2002) Standardisation in Knowledge Management – Towards a Common KM Framework in Europe. Proceedings of UNICOM Seminar “Towards Common Approaches & Standards in KM” Wright, K. (2005) Personal knowledge management: supporting individual knowledge worker performance. Knowledge Management, Research and Practice. 3. pp. 156–165 Zagorodnov, D. (2006) WAIFR: Web-Browsing Attention Recorder Based on a State-Transition Model. International ACM Workshop on Contextualized Attention Metadata: Collecting, Managing and Exploiting of Rich Usage Information 2006, Arlington, VA
