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Data Profiling
Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More...
The effect of tangible media on individuals in business process modeling
In current practice, business processes modeling is done by trained method experts. Domain experts are interviewed to elicit their process information but not involved in modeling. We created a haptic toolkit for process modeling that can be used in process elicitation sessions with domain experts. We hypothesize that this leads to more effective process elicitation. This paper brakes down "effective elicitation" to 14 operationalized hypotheses. They are assessed in a controlled experiment using questionnaires, process model feedback tests and video analysis. The experiment compares our approach to structured interviews in a repeated measurement design. We executed the experiment with 17 st...
Pattern Matching for an Object-oriented and Dynamically Typed Programming Language
Pattern matching is a well-established concept in the functional programming community. It provides the means for concisely identifying and destructuring values of interest. This enables a clean separation of data structures and respective functionality, as well as dispatching functionality based on more than a single value. Unfortunately, expressive pattern matching facilities are seldomly incorporated in present object-oriented programming languages. We present a seamless integration of pattern matching facilities in an object-oriented and dynamically typed programming language: Newspeak. We describe language extensions to improve the practicability and integrate our additions with the existing programming environment for Newspeak. This report is based on the first author’s master’s thesis.
Toward Bridging the Gap Between Formal Semantics and Implementation of Triple Graph Grammars
The correctness of model transformations is a crucial element for the model-driven engineering of high quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the employed implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches it is usually not really clear under which constraints particular implementations are really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. W...
Modeling and enacting complex data dependencies in business processes
Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this report, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability.
Making Databases Work
This book celebrates Michael Stonebraker's accomplishments that led to his 2014 ACM A.M. Turing Award "for fundamental contributions to the concepts and practices underlying modern database systems." The book describes, for the broad computing community, the unique nature, significance, and impact of Mike's achievements in advancing modern database systems over more than forty years. Today, data is considered the world's most valuable resource, whether it is in the tens of millions of databases used to manage the world's businesses and governments, in the billions of databases in our smartphones and watches, or residing elsewhere, as yet unmanaged, awaiting the elusive next generation of dat...
Data in Business Processes
Prozesse und Daten sind gleichermaßen wichtig für das Geschäftsprozessmanagement. Prozessdaten sind dabei insbesondere im Kontext der Automatisierung von Geschäftsprozessen, dem Prozesscontrolling und der Repräsentation der Vermögensgegenstände von Organisationen relevant. Es existieren viele Prozessmodellierungssprachen, von denen jede die Darstellung von Daten durch eine fest spezifizierte Menge an Modellierungskonstrukten ermöglicht. Allerdings unterscheiden sich diese Darstellungenund damit der Grad der Datenmodellierung stark untereinander. Dieser Report evaluiert verschiedene Prozessmodellierungssprachen bezüglich der Unterstützung von Datenmodellierung. Als einheitliche Grundlage entwickeln wir ein Framework, welches prozess- und datenrelevante Aspekte systematisch organisiert. Die Kriterien legen dabei das Hauptaugenmerk auf die datenrelevanten Aspekte. Nach Einführung des Frameworks vergleichen wir zwölf Prozessmodellierungssprachen gegen dieses. Wir generalisieren die Erkenntnisse aus den Vergleichen und identifizieren Cluster bezüglich des Grades der Datenmodellierung, in welche die einzelnen Sprachen eingeordnet werden.
The Hussite King
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Ingeborg Bachmann
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