Context/Background: To achieve high software quality, particularly in the context of agile software development, organizations need tools to continuously analyze software quality. Several quality management (QM) tools have been developed in recent years. However, there is a lack of evidence regarding the quality of QM tools, standardized definitions of such quality, and reliable instruments for measuring it. This, in turn, impedes proper selection and improvement of QM tools. Goals: We aimed at operationalizing the quality of a research QM tool, namely the ProDebt prototype, and evaluating its quality. The goal of the ProDebt prototype is to provide practitioners with support for managing software quality and technical debt. Method: We performed interviews, workshops, and a mapping study to operationalize the quality of the ProDebt prototype and to identify reliable instruments to measure it. We designed a mixed-method study aimed at formative evaluation, i.e., at assessing the quality of the ProDebt prototype and providing guidance for its further development. Eleven practitioners from two German companies evaluated the ProDebt prototype. Results: The participants assessed the information provided by the ProDebt prototype as understandable and relevant. They considered the ProDebt prototype's functionalities as easy to use but of limited usability. They identified improvement needs, e.g., that the analysis results should be linked to other information sources. Conclusions: The evaluation design was of practical value for evaluating the ProDebt prototype considering the limited resources such as the practitioners' time. The evaluation results provided the developers of the ProDebt prototype with guidance for its further development. We conclude that it can be used and tailored for replication or evaluation of other QM tools.

This paper introduces two novel techniques that improve document classification while reducing the amount of manual work by the user. The first technique applies uncertainty sampling as a metric for batch-mode active learning to suggest only the most interesting documents for the manual labeling process, resulting in a steep improvement even for small training sets. This addresses the problem of creating and improving an initial training set. The second technique focuses on cleaning an existing large set of weakly labeled documents by active noise correction. The classifier's self-assessment is used to detect mislabeled documents which are then reclassified. For active noise correction, two approaches are explored: one based on a human expert and one that automatically corrects the assigned labels.

Form Classification has not been focused on for the last decade. Unfortunately the algorithms published mainly in the 80s and 90s do not meet the requirements in our present commercial document analysis projects. There we are confronted with conditions and requirements unanticipated by that research, such as fax distortions and - even worse - form variations. In this work we introduce a new color-coded pixel-based form classification method using Earth Mover's Distance (EMD) that is robust against fax distortions and content variations. Experimental results prove the effectiveness of the presented method. It achieved more than 90% classification accuracy on a real-world business forms dataset, which is significantly better than the competing state-of-the-art methods.

smartFIX is a product portfolio for knowledge based extraction of data from any document format. The system automatically determines the document type and extracts all relevant data for the respective business process. Data that is unreliably recognized is forwarded to a verification workplace for manual checking. In general, users have no difficulties to interpret the document data and wonder why the system needs additional input. For that reason, we implemented an explanation component that is used to justify extraction results, thus, increasing confidence of users. The component is using a semantic log making it possible to provide understandable explanations. We illustrate the benefits of that kind of technology in contrast to the current smartFIX Log Viewer by means of a preliminary user experiment.

The analysis of table structures and the retrieval of table contents is widely agreed to be a difficult challenge in the area of document analysis systems. Instead of extracting the layout of tables, we are interested in understanding their content. In this paper, we present and discuss the smartFIX approach to table recognition and content extraction. Rather than relying on layout features only, we recognize tables by taking into account the presence and semantics of data entities that we expect to find contained in a table. The relationship of a document, including a table, to a specific business process aids in shaping helpful knowledge and expectations about the table's content. smartFIX is a commercial document analysis system complying with the complete bandwidth of industrial requirements. Therefore, smartFIX must locate the tables and extract its business process relevant information with high reliability.

smartFIX is a product portfolio for knowledge-based extraction of data from any document format. smartFIX automatically determines the document type and extracts all relevant data for the respective business process. Data that is uncertainly recognized is forwarded to a verification workplace for manual checking. In general, users have no difficulties to interpret the document data and wonder why the system needs additional input. For that reason, we will integrate an explanation component that will be used to justify uncertain extraction results, thus, increasing confidence of users. The component will be based on semantic technologies in general and on a semantic log in particular. The log will contain all process relevant information enabling the explanation facility to generate customized and understandable explanations. In this paper, we will discuss the benefits of that kind of technology with reference to DAS.

Before buying a document analysis system, companies typically perform an intensive evaluation project whereby several candidates are invited to process a test set of selected representative documents. Moreover, after a system was purchased, it should continuously be optimized regarding the given customer-specific document appearance.

This paper deals with the transfer of knowledge on invoice document layout and extraction strategies, collected by users of the invoice recognition software smartFIX over several years of productive use, to other user's systems. The results of a project analyzing this 'treasure' of knowledge and putting it to use in the smartFIX system are presented. The evaluation shows that this transfer of knowledge using state-of-the-art techniques in transfer learning achieves significantly higher initial recognition rates than the unaugmented system, delivering instant economic advantages by reducing accountant personnel workload.

Workflow management systems (WFMS) that are geared for the orchestration of business processes across multiple organizations are complex distributed systems: they consist of multiple workflow engines, application servers, and communication middleware servers such as ORBs, where each of these server types can be replicated on multiple computers for scalability and availability.Finding an appropriate system configuration with guaranteed application-specific quality of service in terms of throughput, response time, and tolerable downtime is a major challenge for human system administrators. This paper presents a tool that largely automates the task of configuring a distributed WFMS. Based on a suite of mathematical models, the tool derives the necessary degrees of replication for the various server types in order to meet specified goals for performance and availability as well as "performability" when service is degraded due to outages of individual servers. The paper describes the configuration tool, with emphasis on how to capture the load behavior of workflows in a realistic manner. We also present extensive experiments that evaluate the accuracy of the tool's underlying models and demonstrate the practical feasibility of automating the task of configuring a distributed WFMS. The experiments use a detailed simulation which in turn has been validated through measurements with the Mentor-lite prototype system.

The Mentor-lite prototype has been developed within the research project "Architecture, Configuration, and Administration of Large Workflow Management Systems" funded by the German Science Foundation (DFG). In this paper, we present the architecture of Mentor-lite and our approach towards customizability. The feasibility of the presented approach is shown.

The Mentor-lite prototype has been developed within the research project “Architecture, Configuration, and Administration of Large Workflow Management Systems” funded by the German Science Foundation (DFG). It has evolved from its predecessor Mentor [1], but aims at a simpler architecture. The main goal of Mentor-lite has been to build a light-weight, extensible, and tailorable workflow management system (WFMS) with small footprint and easy-to-use administration capabilities. Our approach is to provide only kernel functionality inside the workflow engine, and consider system components like history management and worklist management as extensions on top of the kernel. The key point to retain the light-weight nature is that these extensions are implemented as workflows themselves.

Large-scale data warehousing, data mining and scientific applications require the analysis of terabytes of factual data accumulated over long periods of time. Tape libraries are suitable for storing such mass data. The online analytical processing (OLAP) of this data typically leads to long-running aggregation queries joining the tape-resident fact relations with disk-resident dimension relations. During the join execution, the disks storing the dimension relations are often not dedicated to the join. They are subject to reads and writes invoked by concurrently running applications. In many cases, the performance of these concurrent applications should not be degraded too much by the processing of the join. We present an accurate model for analysing the performance of three different tape-disk join strategies in multi-query systems. The major contributions are: (a) a cost model considering tape and disk bandwidth, tape and disk latencies, available buffer sizes, CPU costs and the selectivity of filters on tape data; (b) disk queueing effects due to concurrent reads and writes at the disk; and (c) two disk scheduling strategies. We show the superiority of a disk scheduling strategy that gives preference to the servicing of the concurrent disk load. We present a strategy for dynamically selecting the most beneficial join algorithm and its parameters at run time. We have implemented the join strategies in a prototype system based on detailed simulations of secondary and tertiary storage devices. Our evaluations confirm that the model is very accurate and a suitable basis for run-time strategy decisions.

Workflow management systems (WfMSs) support the efficient, largely automated execution of business processes. However, using a WfMS typically requires implementing the application's control flow exclusively by the WfMS. This approach is powerful if the control flow is specified and implemented from scratch, but it has severe drawbacks if a WfMS is to be integrated within environments with existing solutions for implementing control flow. Usually, the existing solutions are too complex to be substituted by the WfMS all at once. Hence, the WfMS must support an incremental integration, i.e. the reuse of existing implementations of control flow as well as their incremental substitution. Extending the WfMS's functionality according to future application needs, e.g. by worklist and history management, must also be possible. In particular, at the beginning of an incremental integration process, only a limited amount of a WfMS's functionality is actually exploited by the workflow application. Later on, as the integration proceeds, more advanced requirements arise and demand the customization of the WfMS to the evolving application needs. In this paper, we present the architecture and implementation of a light-weight WfMS, coined Mentor-lite, which aims to overcome the above-mentioned shortcomings of conventional WfMSs. Mentor-lite supports an easy integration of workflow functionality into an existing environment, and can be tailored to specific workflow application needs.