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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...
With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture...
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...
This book constitutes the refereed proceedings of the 10th International Conference on Extending Database Technology, EDBT 2006, held in Munich, Germany, in March 2006. The 60 revised research papers presented together with eight industrial application papers, 20 software demos, and three invited contributions were carefully reviewed and selected from 352 submissions. The papers are organized in topical sections.
The 9th East-European Conference on Advances in Databases and Information Systems was held on September 12–15, 2005, in Tallinn, Estonia. It was organized in a cooperation between the Institute of Cybernetics at Tallinn University of Technology, the Department of Computer Engineering of Tallinn University of Technology, and the Moscow chapter of ACM SIGMOD. The main objective of the ADBIS series of conferences is to provide a - rum for the disseminationof excellent researchaccomplishmentsand to promote interaction and collaboration between the Database and Information Systems research communities from Central and East European countries and the rest of the world. The ADBIS conferences prov...
An extraordinary prodigy of Mozartean abilities, Felix Mendelssohn Bartholdy was a distinguished composer and conductor, a legendary pianist and organist, and an accomplished painter and classicist. Lionized in his lifetime, he is best remembered today for several staples of the concert hall and for such popular music as "The Wedding March" and "Hark, the Herald Angels Sing." Now, in the first major Mendelssohn biography to appear in decades, R. Larry Todd offers a remarkably fresh account of this musical giant, based upon painstaking research in autograph manuscripts, correspondence, diaries, and paintings. Rejecting the view of the composer as a craftsman of felicitous but sentimental, sac...
Cyber-physical systems achieve sophisticated system behavior exploring the tight interconnection of physical coupling present in classical engineering systems and information technology based coupling. A particular challenging case are systems where these cyber-physical systems are formed ad hoc according to the specific local topology, the available networking capabilities, and the goals and constraints of the subsystems captured by the information processing part. In this paper we present a formalism that permits to model the sketched class of cyber-physical systems. The ad hoc formation of tightly coupled subsystems of arbitrary size are specified using a UML-based graph transformation sy...
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...
IT systems for healthcare are a complex and exciting field. One the one hand, there is a vast number of improvements and work alleviations that computers can bring to everyday healthcare. Some ways of treatment, diagnoses and organisational tasks were even made possible by computer usage in the first place. On the other hand, there are many factors that encumber computer usage and make development of IT systems for healthcare a challenging, sometimes even frustrating task. These factors are not solely technology-related, but just as well social or economical conditions. This report describes some of the idiosyncrasies of IT systems in the healthcare domain, with a special focus on legal regulations, standards and security.
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.