Flexible Task Management Support for Knowledge-Intensive Processes

Knowledge-intensive processes (KiPs) are driven by knowledge workers utilizing their skills, experiences and expertise. As KiPs are emergent and unpredictable by nature, their operational support is challenging. For coordinating and synchronizing their work, usually, knowledge workers rely on simple task lists like to-do lists or checklists. Though these instruments are intuitive and prevalent, their current implementations tend to be ineffective and error-prone. Tasks are neither made explicit nor are they synchronized. In addition, no task lifecycle support is provided and media disruptions aggravate task management. As a consequence, the efforts knowledge workers spent in task management are not exploited for optimizing future KiPs. This work presents the proCollab approach, focusing on its stateful and customizable components of processes, task trees, and tasks. proCollab processes may constitute KiPs in the shape of projects and cases, while generic task trees and tasks support required digital task lists of any kind. To enable domain-specific task support, the proCollab state management allows to integrate domain-specific procedure models (e.g., Scrum) and to enrich proCollab components with customized states. Finally, this customizable task management support fosters knowledge workers' coordination, increases work awareness, reduces media disruptions, and enables the reuse of valuable coordination efforts and knowledge

Demonstrating Flexible Support for Knowledge-Intensive Processes with proCollab

Knowledge-intensive processes (KiPs) are driven by knowledge workers utilizing their skills, experiences, and expertise. As these processes are emergent and unpredictable, their support constitutes a big challenge. For coordinating and synchronizing the various tasks of a KiPs, knowledge workers still rely on simple task lists like, e.g., to-do list or checklists. Though task lists are intuitive, their current implementations are very ineffective: tasks are neither made explicit nor are they personalized or synchronized. In addition, no task management lifecycle support is provided and media disruptions frequently occur. This tool demonstration presents the proCollab framework supporting a wide range of KiPs (e.g., projects and cases) through integrated, lifecycle-based task management. In particular, proCollab task trees support the provision of task list templates and to constitute digital task lists of any kind. For customization, it further allows integrating domain-specific methodologies as well as configuring task lists at design and run time. Overall, the proCollab framework shall improve coordination among knowledge workers, increase work awareness, and record valuable coordination efforts

Object-specific Role-based Access Control

The proper management of privacy and security constraints in information systems in general and access control in particular constitute a tremendous, but still prevalent challenge. Role-based access control (RBAC) and its variations can be considered as the widely adopted approach to realize authorization in information systems. However, RBAC lacks a proper object-specific support, which disallows establishing the fine-grained access control required in many domains. By comparison, attribute-based access control (ABAC) enables a fine-grained access control based on policies and rules evaluating attributes. As a drawback, ABAC lacks the abstraction of roles. Moreover, it is challenging to engineer and to audit the granted privileges encoded in rule-based policies. This paper presents the generic approach of object-specific role-based access control (ORAC). On one hand, ORAC enables information system engineers, administrators and users to utilize the well-known principle of roles. On the other, ORAC allows realizing the access to objects in a fine-grained way where required. The approach was systematically established according to well-elicited key requirements for fine-grained access control in information systems. For the purpose of evaluation, the approach was applied to real-world scenarios and implemented in a proof-of-concept prototype demonstrating its feasibility and applicability

Abbildbarkeit unstrukturierter Prozessmodelle auf strukturierte Workflows - Eine Untersuchung am Beispiel BPMN und ADEPT

Die letzten Jahre haben eine Vielzahl an unterschiedlichen Notationen zur Modellierung von Prozessen oder Workflows hervorgebracht. Die Notationen unterscheiden sich neben ihrer unterschiedlichen Symbolik besonders in der Ausdrucksmächtigkeit. So existieren Notationen ohne Einschränkungen bis hin zu Notationen mit sehr weitreichenden syntaktischen Restriktionen, wie z.B. die Blockstruktur. Das Voraussetzen von strukturellen Einschränkungen vereinfacht es Workflow-Management-Systemen (WfMS) Analysen durchzuführen und z.B. Deadlocks zu erkennen. Die zur Modellierung benötigten Notationen weisen durch syntaktische Restriktionen im Vergleich zu syntaktisch uneingeschränkten Notationen eine höhere Komplexität und oftmals eine geringere Ausdrucksmächtigkeit auf. Daher existiert eine Konkurrenzsituation zwischen den Ausdrucksmöglichkeiten und der Ausdrucksmächtigkeit der Notation, sowie den Anforderungen der WfMS um unabdingbare Eigenschaften des Workflows zu sichern. Das Ziel dieser Arbeit ist es, die Abbildbarkeit unstrukturierter Prozessmodelle auf strukturierte Workflows wissenschaftlich zu untersuchen und den Stand der bereits publizierten Verfahren zu erfassen. Es werden Muster innerhalb eines unstrukturierten Prozess-Modells identifiziert, die eine Abbildung auf strukturierte Modelle ausschließen. Darüber hinaus soll versucht werden, die Abbildungen in automatische, semiautomatische und manuelle zu unterteilen. Zur Veranschaulichung wurden die Business Process Modeling Notation (BPMN) der Object Management Group (OMG) als unstrukturierte Notation und ADEPT1 des Instituts für Datenbanken und Informationssysteme (DBIS) als strukturierte Notation ausgewählt

Demonstrating Context-aware Process Injection with the CaPI Tool

Today's enterprises face individual customer expectations, high product variability, and an abundance of regulations. Consequently, they must cope with numerous business process variants, whose design and execution depend on a multiplicity of influencing factors, like, e.g., customer requests, resource availability, compliance rules, or process data. Moreover, already running processes need to be also adjustable to respond to contextual changes, emerging regulations, or new customer requests. With the goal to provide support for process variant management at both design and run time, this demo paper presents the prototype of the context-aware process framework (CaPI). The latter, in particular, enables the sophisticated modeling of process variants based on the context-aware injection of process fragments into a base process. Thus, executed process variants may dynamically evolve during run time, considering the current context of the respective process instance. The CaPI tool was developed based on existing adaptive process management technology. Overall, CaPI enables context-aware process injection, and, thus, the specification of varying processes while providing high process flexibility at run time

Towards a System Support of Collaborative Knowledge Work

Knowledge work is becoming the predominant type of work in developed countries. Leveraging their expertise, skills, and experiences, knowledge workers daily deal with demanding situations. Therefore, they widely work autonomously, but usually collaborate in multiple contexts. Further, their work is influenced by dynamic factors like time constraints, costs, and available resources, and thereby it cannot be pre-specfied like routine work. The lack of an appropriate context and process support, in turn, reduces their productivity and hinders the reuse as well as the continuous improvement of elaborated solutions. This paper structures collaborative knowledge work and presents its characteristics and dimensions. Moreover, we introduce a lifecycle methodology to support collaborative knowledge workers holistically