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Über dieses Buch

This book constitutes the thoroughly refereed post-conference proceedings of the Third International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, IC3K 2011, held in Paris, France, in October 2011. This book includes revised and extended versions of a strict selection of the best papers presented at the conference; 39 revised full papers together with one invited lecture were carefully reviewed and selected from 429 submissions. According to the three covered conferences KDIR 2011, KEOD 2011, and KMIS 2011, the papers are organized in topical sections on knowledge discovery and information retrieval, knowledge engineering and ontology development, and on knowledge management and information sharing.

Inhaltsverzeichnis

Frontmatter

Invited Paper

Frontmatter

Accelerating Scientists’ Knowledge Turns

Abstract
A “knowledge turn” is a cycle of a process by a professional, including the learning generated by the experience, deriving more good and leading to advance. The majority of scientific advances in the public domain result from collective efforts that depend on rapid exchange and effective reuse of results. We have powerful computational instruments, such as scientific workflows, coupled with widespread online information dissemination to accelerate knowledge cycles. However, turns between researchers continue to lag. In particular method obfuscation obstructs reproducibility. The exchange of “Research Objects” rather than articles proposes a technical solution; however the obstacles are mainly social ones that require the scientific community to rethink its current value systems for scholarship, data, methods and software.
Carole Goble, David De Roure, Sean Bechhofer

Part I: Knowledge Discovery and Information Retrieval

Frontmatter

Mining Graphs of Prescribed Connectivity

Abstract
Many real-life data sets, such as social, biological and communication networks are naturally and easily modeled as large labeled graphs. Finding patterns of interest in these graphs is an important task, but due to the nature of the data not all of the patterns need to be taken into account. Intuitively, if a pattern has high connectivity, it implies that there is a strong connection between data items. In this paper, we present a novel algorithm for finding frequent graph patterns with prescribed connectivity in large single-graph data sets. We also show how this algorithm can be adapted to a dynamic environment where the data changes over time. We prove that the suggested algorithm generates no more candidate graphs than any other algorithm whose graph extension procedure we employ.
Natalia Vanetik

Concept Discovery and Automatic Semantic Annotation for Language Understanding in an Information-Query Dialogue System Using Latent Dirichlet Allocation and Segmental Methods

Abstract
Efficient statistical approaches have been recently proposed for natural language understanding in the context of dialogue systems. However, these approaches are trained on data semantically annotated at the segmental level, which increases the production cost of these resources. This kind of semantic annotation implies both to determine the concepts in a sentence and to link them to their corresponding word segments. In this paper, we propose a two-step automatic method for semantic annotation. The first step is an implementation of the latent Dirichlet allocation aiming at discovering concepts in a dialogue corpus. Then this knowledge is used as a bootstrap to infer automatically a segmentation of a word sequence into concepts using either integer linear optimisation or stochastic word alignment models (IBM models). The relation between automatically-derived and manually-defined task-dependent concepts is evaluated on a spoken dialogue task with a reference annotation.
Nathalie Camelin, Boris Detienne, Stéphane Huet, Dominique Quadri, Fabrice Lefèvre

Spectral Clustering: An Explorative Study of Proximity Measures

Abstract
Spectral clustering algorithms recently gained much interest in research community. This surge in interest is mainly due to their ease of use, their applicability to a variety of data types and domains as well as the fact that they very often outperform traditional clustering algorithms. These algorithms consider the pair-wise similarity between data objects and construct a similarity matrix to group data into natural subsets, so that the objects located in the same cluster share many common characteristics. Objects are then allocated into clusters by employing a proximity measure, which is used to compute the similarity or distance between the data objects in the matrix. As such, an early and fundamental step in spectral cluster analysis is the selection of a proximity measure. This choice also has the highest impact on the quality and usability of the end result. However, this crucial aspect is frequently overlooked. For instance, most prior studies use the Euclidean distance measure without explicitly stating the consequences of selecting such measure. To address this issue, we perform a comparative and explorative study on the performance of various existing proximity measures when applied to spectral clustering algorithm. Our results indicate that the commonly used Euclidean distance measure is not always suitable, specifically in domains where the data is highly imbalanced and the correct clustering of boundary objects are critical. Moreover, we also noticed that for numeric data type, the relative distance measures outperformed the absolute distance measures and therefore, may boost the performance of a clustering algorithm if used. As for the datasets with mixed variables, the selection of distance measure for numeric variable again has the highest impact on the end result.
Nadia Farhanaz Azam, Herna L. Viktor

Comparing the Macroeconomic Responses of US and Japan through Time Series Segmentation

Abstract
In this paper we performed time series segmentation on the high-frequency time series data of various US and Japanese financial market indices, and found that for both economies, the time series segments can be very naturally grouped into four to six classes, corresponding roughly with economic growth, economic crisis, market correction, and market crash. With this classification of the time series segments, we discovered that the US economy recovered completely in one year six months, whereas the Japanese economy recovered incompletely in two years three months from the 2000 Technology Bubble Crisis. In contrast to the slow recovery, the US and Japanese economies succumbed to the 2007 Subprime Crisis in two months and 21 days respectively. Minimal spanning tree analysis of the time series segments points to signs of recovery as early as Sep 2009 for the US, but no signs for recovery as late as Jun 2010 for Japan.
Jian Cheng Wong, Gladys Hui Ting Lee, Yiting Zhang, Woei Shyr Yim, Robert Paulo Fornia, Danny Yuan Xu, Chong Eu Lee, Jun Liang Kok, Siew Ann Cheong

A Block Coclustering Model for Pattern Discovering in Users’ Preference Data

Abstract
This paper provides a principled probabilistic co-clustering framework for missing value prediction and pattern discovery in users’ preference data. We extend the original dyadic formulation of the Block Mixture Model(BMM) in order to take into account explicit users’ preferences. BMM simultaneously identifies user communities and item categories: each user is modeled as a mixture over user communities, which is computed by taking into account users’ preferences on similar items. Dually, item categories are detected by considering preferences given by similar minded users. This recursive formulation highlights the mutual relationships between items and user, which are then used to uncover the hidden block-structure of the data. We next show how to characterize and summarize each block cluster by exploiting additional meta data information and by analyzing the underlying topic distribution, proving the effectiveness of the approach in pattern discovery tasks.
Nicola Barbieri, Gianni Costa, Giuseppe Manco, Ettore Ritacco

Learning Attack Features from Static and Dynamic Analysis of Malware

Abstract
Malware detection is a major challenge in today’s software security profession. Works exist for malware detection based on static analysis such as function length frequency, printable string information, byte sequences, API calls, etc. Some works also applied dynamic analysis using features such as function call arguments, returned values, dynamic API call sequences, etc. In this work, we applied a reverse engineering process to extract static and behavioral features from malware based on an assumption that behavior of a malware can be revealed by executing it and observing its effects on the operating environment. We captured all the activities including registry activity, file system activity, network activity, API Calls made, and DLLs accessed for each executable by running them in an isolated environment. Using the extracted features from the reverse engineering process and static analysis features, we prepared two datasets and applied data mining algorithms to generate classification rules. Essential features are identified by applying Weka’s J48 decision tree classifier to 1103 software samples, 582 malware and 521 benign, collected from the Internet. The performance of all classifiers are evaluated by 5-fold cross validation with 80-20 splits of training sets. Experimental results show that Naïve Bayes classifier has better performance on the smaller data set with 15 reversed features, while J48 has better performance on the data set created from the API Call data set with 141 features. In addition, we applied a rough set based tool BLEM2 to generate and evaluate the identification of reverse engineered features in contrast to decision trees. Preliminary results indicate that BLEM2 rules may provide interesting insights for essential feature identification.
Ravinder R. Ravula, Kathy J. Liszka, Chien-Chung Chan

Improving Text Retrieval Accuracy by Using a Minimal Relevance Feedback

Abstract
In this paper we have demonstrated that the accuracy of a text retrieval system can be improved if we employ a query expansion method based on explicit relevance feedback that expands the initial query with a structured representation instead of a simple list of words. This representation, named a mixed Graph of Terms, is composed of a directed and an a-directed subgraph and can be automatically extracted from a set of documents using a method for term extraction based on the probabilistic Topic Model. The evaluation of the method has been conducted on a web repository collected by crawling a huge number of web pages from the website ThomasNet.com. We have considered several topics and performed a comparison with a baseline and a less complex structure that is a simple list of words.
Francesco Colace, Massimo De Santo, Luca Greco, Paolo Napoletano

Using Distant Supervision for Extracting Relations on a Large Scale

Abstract
Most of Information Extraction (IE) systems are designed for extracting a restricted number of relations in a specific domain. Recent work about Web-scale knowledge extraction has changed this perspective by introducing large-scale IE systems. Such systems are open-domain and characterized by a large number of relations, which makes traditional approaches such as handcrafting rules or annotating corpora for training statistical classifiers difficult to apply in such context. In this article, we present an IE system based on a weakly supervised method for learning relation patterns. This method extracts without supervision occurrences of relations from a corpus and uses them as examples for learning relation patterns. We also present the results of the application of this system to the data of the 2010 Knowledge Base Population evaluation campaign.
Ludovic Jean-Louis, Romaric Besançon, Olivier Ferret, Adrien Durand

Learning Effective XML Classifiers Based on Discriminatory Structures and Nested Content

Abstract
Supervised classification aims to learn a model (or a classifier) from a collection of XML documents individually marked with one of a predefined set of class labels. The learnt classifier isolates each class by the content and structural regularities observed within the respective labeled XML documents and, thus, allows to predict the unknown class of unlabeled XML documents by looking at their content and structural features. The classification of unlabeled XML documents into the predefined classes is a valuable support for more effective and efficient XML search, retrieval and filtering.
We discuss an approach for learning intelligible XML classifiers. XML documents are represented as transactions in a space of boolean features, that are informative of their content and structure. Learning algorithms induce compact associative classifiers with outperforming effectiveness from the transactional XML representation. A preprocessing step contributes to the scalability of the approach with the size of XML corpora.
Gianni Costa, Riccardo Ortale, Ettore Ritacco

A Fast Method for Web Template Extraction via a Multi-sequence Alignment Approach

Abstract
The increased richness of the page contents and the diffusion of content management systems are responsible for the impressive changes happened in the last decade in a typical Web site layout. In fact, most of the Web sites are endowed with a template which gives them a uniform graphical and functional structure. Templates, by themselves, do not change the informative content of the pages, but they are typically designed to enhance the usability by uniformly organizing the contents following a standardized arrangement of functional blocks and by providing navigation tools, like menus or banners. However, the additional information provided by the template can worsen the performances of many algorithms for automatic Web processing. In fact, templates are designed for human users and provide redundant information that is marginally correlated with the main contents of a given page. These additional parts act as a noise source for many automated tasks such as web crawling, indexing, page classification and clustering. Hence, a preprocessing step to detect and strip the parts related to the template is needed to extract only the specific contents of each page. The critical part for the automation of this process is the accurate detection of the template, given a minimal set of pages from a given site.
The template consists in parts of the HTML tag structure that are shared by all the pages from the site, and its detection is made difficult by the variable parts intermixed with them. We propose an algorithm for template extraction that is based on the alignment of the HTML sequences of a set of pages. This approach is quite fast since it exploits efficient alignment algorithms proposed in bioinformatics and does not require complex tree matching or visual layout analysis. The algorithm aligns the HTML tag streams from pairs of pages and extracts a set of candidate templates that are merged following a binary tree consensus schema to increase the algorithm precision. The experimental evaluation shows that 16 sample pages are enough to extract the site template with good accuracy. The effects of the template stripping on a clustering task are also investigated, showing that the clustering quality can be effectively improved.
Filippo Geraci, Marco Maggini

TermWatch II: Unsupervised Terminology Graph Extraction and Decomposition

Abstract
We present a symbolic and graph-based approach for mapping knowledge domains. The symbolic component relies on shallow linguistic processing of texts to extract multi-word terms and cluster them based on lexico-syntactic relations. The clusters are subjected to graph decomposition based on inherent graph theoretic properties of association graphs of items (multi-word terms and authors). This includes the search for complete minimal separators that can decompose the graphs into central (core topics) and peripheral atoms. The methodology is implemented in the TermWatch system and can be used for several text mining tasks. In this paper, we apply our methodology to map the dynamics of terrorism research between 1990-2006. We also mined for frequent itemsets as a mean of revealing dependencies between formal concepts in the corpus. A comparison of the extracted frequent itemsets and the structure of the central atom shows an interesting overlap. The main features of our approach lie in the combination of state-of-the-art techniques from Natural Language Processing (NLP), Clustering and Graph Theory to develop a system and a methodology adapted to uncovering hidden sub-structures from texts.
Eric SanJuan

Learning to Classify Text Using a Few Labeled Examples

Abstract
It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. In this paper we demonstrate that a way to obtain a high accuracy, when the number of labeled examples is low, is to consider structured features instead of list of weighted words as observed features. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model.
Francesco Colace, Massimo De Santo, Luca Greco, Paolo Napoletano

Part II: Knowledge Engineering and Ontology Development

Frontmatter

A System to Support Legal Case Building and Reasoning

Abstract
We have designed a system to support collaborative legal case reasoning and building. The design is based on our understanding of the corporate litigation domain acquired through analysis of the literature, interviews of various parties involved in corporate litigation processes, and studies of the commercial tools already available. In this paper we illustrate the designed system and in particular the interaction modes that it supports that we believe address a number of the requirements that emerged through our analysis. We also describe its main components and their integration, including a knowledge model that represents the domain, and a natural language processing component for extracting semantic information. A description of a prototype system is also provided.
Stefania Castellani, Nikolaos Lagos, Nicolas Hairon, Antonietta Grasso, David Martin, Frederique Segond

Different Approaches to Build Brief Ontologies

Abstract
This paper introduces the problem of building brief ontologies in order to use only a relevant portion of an ontology. It describes the procedure of generation that preserves partially the original taxonomy. Therefore, concepts definitions, ranges and properties domains are processed in order to be generalized. The paper also presents a methodology consisting of seven steps to build a brief ontology. This method is applied to an ontology for environmental assessment in order to build a specific brief ontology for floods.
Julián Garrido, Stefano Mambretti, Ignacio Requena

Graph-Based Semi-supervised Clustering for Semantic Classification of Unknown Words

Abstract
This paper presents a method for semantic classification of unknown verbs including polysemies into Levin-style semantic classes. We propose a semi-supervised clustering, which is based on a graph-based unsupervised clustering technique. The algorithm detects the spin configuration that minimizes the energy of the spin glass. Comparing global and local minima of an energy function, called the Hamiltonian, allows for the detection of nodes with more than one cluster. We extended the algorithm so as to employ a small amount of labeled data to aid unsupervised learning, and applied the algorithm to cluster verbs including polysemies. The distributional similarity between verbs used to calculate the Hamiltonian is in the form of probability distributions over verb frames. The result obtained using 110 test polysemous verbs with labeled data of 10% showed 0.577 F-score.
Fumiyo Fukumoto, Yoshimi Suzuki

Ontology Engineering for the Autonomous Systems Domain

Abstract
Ontologies provide a common conceptualisation that can be shared by all stakeholders in an engineering development process. They provide a good means to analyse the domain, allowing to separate descriptive from problem–solving knowledge. Our research programme on autonomous systems considered an ontology as the adequate mechanism to conceptualise the autonomous systems domain, and the software engineering techniques applied to such systems. This paper describes the ontological engineering process of such an ontology: OASys (Ontology for Autonomous Systems). Its development considered different stages: the specification of the requirements to be fulfilled by the ontology; the extraction of the actual features needed to implement the desired requirements; the conceptualisation phase with the design decisions to integrate the different domains, theories and techniques addressed by the ontological elements; and finally, the implementation of the ontology, which integrates both ontology engineering and software engineering approaches by using UML as the implementation language.
Julita Bermejo–Alonso, Ricardo Sanz, Manuel Rodríguez, Carlos Hernández

Cloud Services Composition Support by Using Semantic Annotation and Linked Data

Abstract
Cloud computing is not only referred as synonym of on-demand usage of computing resources and services, but as the most promising paradigm to provide infinite scalability by using virtual infrastructures. In the other hand mobile technologies are scaling up to encompass every day a growing number of real and virtual objects in order to provide large-scale data applications, e.g. sensor-based intelligent communications networks, smart grid computing applications, etc. In those complex scenarios, cloud-based computing systems need to cope with diverse service demands in order to enable dynamic composition based on particular user’s demands, variations in collected data broadband, fluctuation of data quality and to satisfy ad-hoc usage for personalized applications. Thus essential characteristics from cloud-native systems i.e. elasticity and multi-tenancy are fundamental requirements into large-scale data processing systems. In this paper we have investigated common practices on information sharing and domain ontological modelling to enable service composition of cloud computing service provisioning. This approach exploits the potential of semantic models in supporting service and application linkage by studying links between the complementary services. By using semantic modelling and knowledge engineering we can enable the composition of services. We discuss what implications this approach imposes on architectural design terms and also how virtual infrastructures and cloud-based systems can benefit from this ontological modelling approach. Research results about information sharing and information modelling by using semantic annotations are discussed. An introductory application scenario is depicted.
Martín Serrano, Lei Shi, Mícheál Ó Foghlú, William Donnelly

Enterprise Architecture Executable Patterns: Enterprise Architecture – Replacing Models with Executable Patterns

Abstract
Using executable Enterprise Architecture patterns replaces the traditional engineering approach of using models to guide software development. This is accomplished by combining Enterprise Architecture principles, ontology reasoning, and Service Component Architecture into an executable environment. Creating this environment is the motivation for the OTTER project (Ontology Technology That Executes Real-time). This environment sets the Enterprise Architecture as the foundation for component service development and execution. In using this environment, Enterprise Architecture cannot be overlooked or bypassed during information systems development. This results in reducing the complications of application integration and data-sharing which reduces costs and problems.
Protégé is used to define the layers of the Enterprise Architecture. These layers are mapped to Service Component Architecture standards to provide real-time execution of processes. Information access and service component access are both provided by OTTER using OWL data expressions. This use of OWL data expressions is an alternative to using XML web services for service access and SQL for relational database access.
Thomas A. Tinsley

Identifying Services from a Service Provider and Customer Perspectives

Abstract
Despite the remarkable growing of the services industry in the world economy, the services quality is still affected by gaps identified two decades ago. One of these gaps occurs when the service provider has a perception of what the customer expects that diverges from the real expected service. This difference can be caused by a poor service identification process and, more precisely, by who should be included in this process. Current solutions to identify services still have drawbacks, since they are not customer driven, are web services driven or lack specific processes. In this paper, we propose a service identification process based on the Design & Engineering Methodology for Organizations (DEMO). The proposal was evaluated by comparing two lists of services provided by a Human Resources department: one based on a description given by the head of the department and another based on the customers that use the department services. The differences between the two lists show the gap between the customers’ expectations and the provider perceptions of those expectations. We conclude that both client and service provider should be included in the service identification process.
Carlos Mendes, João Ferreira, Miguel Mira da Silva

Metropolitan Ecosystems among Heterogeneous Cognitive Networks: Issues, Solutions and Challenges

Abstract
Cognitive Networks working on large scale are experimenting an increasing popularity. The interest, by both a scientific and commercial perspective, in the context of different environments, applications and domains is a fact. The natural convergence point for these heterogeneous disciplines is the need of a strong advanced technologic support that enables the generation of distributed observations on large scale as well as the intelligent process of obtained information. Focusing mostly on cognitive networks that generate information directly through sensor networks, existent solutions at level of metropolitan area are mainly limited by the use of obsolete/static coverage models as well as by a fundamental lack of flexibility respect to the dynamic features of the virtual organizations. Furthermore, the centralized view at the systems is a strong limitation for dynamic data processing and knowledge building.
Salvatore F. Pileggi, Carlos Fernandez-Llatas, Vicente Traver

Code Quality Cultivation

Abstract
Two of the meanings of the word “cultivation” that are rather unrelated show a strong dependency, when applied to the domain of code quality:
The existing code in an evolving software system could be seen as the soil in which new code and new functionality is growing. While working this “soil” developers benefit from unobtrusively presented automatic feedback about the quality of their code. There are tools that verify the correct usage of good code structures (“design pattern”) and other tools that highlight improvement opportunities (“bad smells”).
As design patterns and bad smells are usually presented and discussed separately it has not been observed, that they partially contradict with each other. We will show that even well chosen design patterns can lead to bad smells. Thus, design quality is relative, which does not mean that it is arbitrary. Design quality knowledge has to be rendered more precisely. We suggest to co-evolve the quality knowledge specifications together with the code in a process of cultivation. Bad smell definitions can then easily be extended by taking existing design patterns into account.
When the design knowledge is cultivated together with the code, specific knowledge like typical method names can be incorporated. A case study explored unjustified “intensive coupling”-smells in ArgoUML: While a previously suggested generic structural criterion identified 13% unjustified warnings, taking the specific names into account, identified 90%.
Daniel Speicher

Misbehavior Discovery through Unified Software-Knowledge Models

Abstract
UML statecharts are a widely accepted standard for modeling software behavior. But, despite the increasing importance of semantics for software behavior, semantics has been treated within UML as mere reasoning add-ons. We propose fully integration of UML statecharts with behavioral knowledge obtained from novel behavioral ontologies into a Unified Software-Knowledge model. These unified models have two important characteristics: first, misbehaviors are explicitly represented; second, behavioral ontologies generate graphs isomorphic to UML statecharts, by construction. This approach is applicable to run time measurements, to check the actual software behavior correctness and efficiency. Measurement discrepancies may trigger knowledge discovery mechanisms to update the unified models. The approach is illustrated with statechart examples from the domain of GOF software design patterns.
Iaakov Exman

Structured Knowledge: An Universal Indexing System Approach

Abstract
Knowledge is one of the main assets that humans have, the knowledge achieved in one area may be applied in another different area; all that you need is to remember it and adapt it to the new area or problem. If we apply this concept in computer science, knowledge could be a powerful asset to store (as remember) and reuse (as adapt). Knowledge could be structured using different kinds of Knowledge Organization Systems (KOS), but in all the cases the metamodel is important to be known in order to match with consistency the diverse kinds of knowledge. If it is possible to index any kind of knowledge stored as a KOS in a repository, it means that knowledge coming from diverse sources could be merged in a unique repository. The merge activity is important in the Reuse process because it makes possible to trace different pieces of knowledge at the end it will be retrieved improving the Reuse process and reducing costs at last. A method for indexing structured knowledge is described as well as the algorithms and practical examples in case of the metamodel describing the knowledge is not available.
Anabel Fraga, Juan Llorens, Karina Robles

Part III: Knowledge Management and Information Sharing

Frontmatter

The Role of External Information Awareness and Proactiveness of Innovation Strategy in Employees’ Innovation Behavior: A Psychological Perspective

Abstract
Due to innovation is highly knowledge intensive, employees’ innovation behavior plays a central role in knowledge creation and distribution in organizations. Therefore, in knowledge management initiatives, it is important to encourage employees’ IB, which involves developing, promoting, judging, distributing and implementing new ideas at work. From a psychological perspective, this study applies the theory of planned behavior (TPB) to better understand employees’ IB, and also extends TPB by considering the effects of two unexamined yet important organizational factors: external information awareness (EIA) and proactiveness of innovation strategy (PIS). Results from a survey of employees in Japanese organizations indicate that EIA and PIS are positively related with employees’ attitude towards innovation, subjective norm about innovation, and perceived behavioral control to innovation, which is, in turn, significantly influence employees’ IB. Employees’ attitude, subjective norm, and perceived behavior control mediate partially the effects of EIA and completely the influence of PIS to employees’ IB. These findings provide directions for more efficient employees’ IB encouragement, by focusing on improving perceived behavior control, EIA and PIS.
Jing Tang, Loo Geok Pee, Junichi Iijima

Determining the Collaboration Maturity of Organizational Teams: A Study in the Automotive Industry

Abstract
Many researchers argue that the quality of collaboration directly affects the quality of an organization’s outcomes and performance. This paper reports on the first field application of a Collaboration Maturity Model (Col-MM) through an automotive field study. This model was empirically developed during a series of Focus Group meetings with professional collaboration experts to maximize its relevance and practical applicability. Col-MM is intended to be sufficiently generic to be applied to any type of collaboration and useable to assess the collaboration maturity of a given team holistically through self-assessments performed by practitioners. The purpose of the study reported in this paper was to apply and evaluate the use of the Col-MM in practice. The results should be of interest to academic researchers and information systems practitioners interested in collaboration maturity assessment. The paper further serves as a starting point for future research in this area.
Imed Boughzala, Gert-Jan de Vreede

Open Source Tools for Enterprise 2.0: A Feature-Based Adaptable Analysis

Abstract
When introducing Enterprise 2.0 tools to support knowledge workers working together on cognitive tasks and sharing information, companies and organizations face the problem of choosing the right tools from a huge market of systems. In particular for SMEs, open source tools for Enterprise 2.0 offer a good alternative to commercial systems, but the diversity of systems makes this marketplace quite confusing. Therefore we present a study of the growing market for Enterprise 2.0 systems that focuses entirely on systems available under an open source license. We use a set of 97 individual features and criteria that are grouped around the central functionalities of communication, coordination, collaboration and connection, to analyze the suitability of a representative sample of open source Enterprise 2.0 tools for the average knowledge worker. The evaluation matrix can be easily adapted to get more specific evaluation results for more particular company requirements. Our results show that there are many technically mature solutions with a broad range of functionality available from the market of open source tools for Enterprise 2.0.
Bettina Schauer, Michael Zeiller, Robert Matzinger

Assessing the Impact of In-government Cooperation Dynamics: A Simluation-Based Systems Inquiry

Abstract
We coined the term “government extended enterprise” (GEE) to describe sets of effectively autonomous government organizations that must cooperate voluntarily to achieve desired GEE-level outcomes. A GEE is, by definition, a complex dynamical system of systems (SoS). Our continuing research investigates the proposition that the interaction of four “canonical forces” affects both internal GEE cooperation and SoS-level operational effectiveness, changing the GEE’s status as indicated by the "SoS differentiating characteristics" detailed by Boardman and Sauser. Three prior papers have described the concepts involved, postulated the relationships among them, discussed the n-player, iterated "Stag Hunt" methodology applied to execute a real proof-of-concept case (the U.S. Counterterrorism Enterprise’s response to the Christmas Day Bomber) in an agent-based model, and presented preliminary conclusions from testing of the simulation. This paper adds key insights gleaned from additional in-depth review of relevant literature and data analysis.
Lawrence John, Patricia M. McCormick, Tom McCormick, Greg McNeill, John Boardman

Using Work Context and Levels of Expertise to Foster Person-to-Person Knowledge Exchange: A Concept for Connecting Knowledge Workers Based on (Socio-) Psychological Theories

Abstract
Knowledge within organizations is increasingly distributed, which raises the challenge to connect the right individuals for knowledge exchange. This contribution analyzes this challenge and proposes a concept to connect the right individuals. We first investigate relevant theoretical models namely transactive memory theory, social capital theory for knowledge exchange and a model based on socio-motivational and problem solving theory to find relevant constructs. We then analyze the relevant state-of-the-art to find that all approaches have some limitations with respect to the theoretical models. Our proposed solution builds on task histories for the matching, and we show how it can be used to determine contextual overlap and level of expertise. We then describe a case study in which our concept was employed in a three month timeframe with 93 individuals. A survey after the case study showed that our assumptions concerning the relevance and benefit of context overlap are substantiated.
Jörg Schmidl, Helmut Krcmar

Thinking Out of the Box: Discovering the Relevance of External Context to Business Processes

Abstract
Successful organizations are those able to identify and respond appropriately to changes in their internal and external environments. The search for flexibility is linked to the need for the organization to adapt to frequent and exceptional changes in scenarios imposed to them. Those disruptions in routine should be reflected in business processes, in a sense that processes must be adjusted to such variations, taking into account both internal and external variables, typically referred in the literature as the context of the process. In particular, defining the relevance of external context for the execution of a process is still an open research issue. We propose a method to identify and prioritize external variables that impact the execution of specific activities of a process, applying competitive intelligence concepts and data mining techniques. We have evaluated the method in a case study, which showed how the discovered variables influenced specific activities of the process.
Eduardo Costa Ramos, Flavia Maria Santoro, Fernanda Baião

Backmatter

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