Bernd Michelberger

A framework for the intelligent delivery and user-adequate visualization of process information

A continuously increasing amount of data makes it difficult for knowledge-workers to identify the information they need to perform their tasks in the best possible way. Particularly challenging in this context is the alignment of process-related information (e.g., working instructions, best practices) with business processes. In fact, process-related information (process information for short) and business processes are usually handled separately. On one hand, shared drives, databases, and information systems are used to manage process information, on the other, process management technology provides the basis for managing business processes. In practice, enterprises often establish (Intranet) portals to connect both perspectives. However, such portals are not sufficient. Reasons are that process information is usually delivered without considering the current work context and business processes are presented to process participants in a rather static manner. Therefore, enterprises crave for new ways of making process information available. This paper picks up this challenge and presents the niPRO framework. niPRO is based on semantic technology and enables the intelligent delivery and user-adequate visualization of comprehensive process information.

A Context Framework for Process-Oriented Information Logistics

A continuously increasing data overload makes it a challenging task for knowledge-workers and decision-makers to quickly identify relevant information, i.e., information they need when executing business processes. To tackle this challenge, process-oriented information logistics is a promising approach. The basic idea is to provide the right process information, in the right format and quality, at the right place, at the right point in time, and to the right people. To achieve this, it becomes particularly important to take the work context of process participants into account. In fact, knowing and utilizing context information is a prerequisite to effectively provide relevant process information to process participants. This paper provides a sophisticated context framework for enabling context-awareness in process-oriented information logistics.

Process-oriented Information Logistics: Aligning Enterprise Information with Business Processes

Today, enterprises are confronted with a continuously increasing amount of data. Examples of such data include office files, e-mails, process descriptions, and data from process-aware information systems. This data overload makes it difficult for knowledge-workers to identify the information they need to perform their tasks in the best possible way. Particularly challenging is the alignment of process-related information with business processes. In fact, process-related information and business processes are usually managed separately. On the one hand, enterprise content management systems, shared drives, and Intranet portals are used for organizing information, on the other hand, process management technology is used to design and enact business processes. With process-oriented information logistics (POIL) this paper presents an approach for bridging this gap. POIL enables the process-oriented and context-aware delivery of process-related information to knowledge-workers. We also present a clinical use case and a proof-of-concept prototype to demonstrate the application and benefits of POIL.

On Handling Process Information: Results from Case Studies and a Survey

An increasing data overload makes it difficult to provide the needed information to knowledge-workers and decision-makers in today’s process-oriented enterprises. The main problem is to identify the information being relevant in a given process context. Moreover, there are new ways of collaboration in the context of distributed processes (e.g., automotive engineering, patient treatment). The goal is to provide the right process information, in the right format and quality, at the right place, at the right point in time to the right people. Picking up this goal, enterprises crave for an intelligent and process-oriented information logistics. In this paper we investigate fundamental issues enabling such information logistics based on two exploratory case studies in the automotive and the clinical domain. Additionally, we present results of an online survey with 219 participants supporting our case study findings. Our research does not only reveal different types of process information, but also allows for the derivation of factors determining its relevance. Understanding these factors, in turn, is a fundamental prerequisite to realize effective process-oriented information logistics.

A Literature Survey on Information Logistics

The notion of information logistics (IL) has been introduced as a new information management paradigm. Goal is to enable the effective and efficient delivery of needed information in the right format, granularity and quality, at the right place, at the right point in time to the right actors. IL has received much attention in recent years, both from researchers and practitioners. In order to better understand the state-of-the-art and current research trends in the research field of IL, this paper presents a comprehensive IL literature survey. In total, we identified 53 scientific articles discussing IL concepts and approaches. These articles were systematically analyzed and finally classified in ten research clusters. Based on these clusters, a more comprehensive understanding of past, current, and future IL developments becomes possible.

Determining the Link and Rate Popularity of Enterprise Process Information

Today’s knowledge workers are confronted with a high load of heterogeneous information making it difficult for them to identify the information relevant for performing their tasks. Particularly challenging is thereby the alignment of process-related information (process information for short), such as e-mails, office files, forms, checklists, guidelines, and best practices, with business processes. In previous work, we introduced the concept of process-oriented information logistics (POIL) to bridge this gap. POIL allows for the process-oriented and context-aware delivery of relevant process information to knowledge workers. So far, we have introduced concepts to integrate business processes with process information. A remaining challenge is to identify the process information relevant for a given process context. This paper tackles this challenge and extends our POIL approach with techniques and algorithms for identifying relevant process information. More specifically, we introduce two algorithms for determining the relevance of process information based on their link and rate popularity. We use a scenario from the automotive domain to demonstrate and validate the applicability of our approach.

Navigating in process model repositories and enterprise process information

Although process-aware information systems have been adopted in enterprises for many years, they still do not properly link the business processes they implement with related enterprise process information (e.g., guidelines, checklists, templates, and e-mails). On one hand, process management technology is used to design, implement, enact, and monitor processes. On the other, enterprise process information is spread over various sources like shared drives, databases, and enterprise information systems. As a consequence, users often manually link process information with particular process objects (e.g., using process portals). What is needed instead, however is an integrated access to both processes and related enterprise process information. This paper establishes such a link by introducing an integrated navigation space for process model collections and related enterprise process information. In particular, this navigation space allows process participants to flexibly navigate within process model collections, single process models, and related process information. In turn, this enables advanced end-user support for process repositories.

On the Precision of Search Engines: Results from a Controlled Experiment

Handling the growing amount of digital information is one of the major challenges when dealing with the World Wide Web (WWW). In particular, users crave for an effective and efficient retrieval of needed information. In this context, search engines adopt a key role. Besides conventional search engines such as Google, semantic search engines have emerged as an alternative approach in recent years. The quality of search results delivered by search engines is influenced by many criteria. This paper picks up one specific issue, the precision, and investigates and compares the precision of current both conventional (i.e., non-semantic) and semantic search engines based on a controlled experiment with 77 participants. Specifically, Google, AltaVista, MetaGer, Hakia, Kngine, and WolframAlpha are investigated and compared.

Enabling a User-Friendly Visualization of Business Process Models

Enterprises are facing increasingly complex business processes. Engineering processes in the automotive domain, for example, may comprise hundreds or thousands of process tasks. In such a scenario, existing modeling notations do not always allow for a user-friendly process visualization. In turn, this hampers the comprehensibility of business processes, especially for non-experienced process participants. This paper tackles this challenge by suggesting alternative ways of visualizing large and complex process models. A controlled experiment with 22 subjects provides first insights into how users perceive these approaches.

Maintaining Semantic Networks: Challenges and Algorithms

Knowledge workers are confronted with a massive load of data from numerous heterogeneous sources, making it difficult for them to identify the information relevant for performing their tasks. Particularly challenging is the alignment of information with business processes. In previous work, we introduced a Semantic Network (SN) for bridging this gap, i.e., for identifying relations between information and business processes. What has been neglected so far is the maintenance of an SN in order to keep the SN consistent, complete, and up-to-date. This paper tackles this issue and extends our approach with algorithms dealing with the maintenance of an SN. For this purpose, we identify and classify properties of objects and relations captured in an SN and show how these properties can be maintained. We use a case from the automotive domain in order to demonstrate and validate the feasibility and applicability of our maintenance framework.