Andreas Maximilian Wahl

• M.Sc.
• Friedrich-Alexander-University Erlangen-Nürnberg

Complex data analysis scenarios often require discovering and combining multiple data sources. Data scientists usually formulate a series of SQL queries building on each other, also called a session, to iteratively derive results. However, due to a lack of familiarity with data sources or the complexity of query results, it can be a hard task to de...

SQL queries encapsulate the knowledge of their authors about the usage of the queried data sources. This knowledge also contains aspects that cannot be inferred by analyzing the contents of the queried data sources alone. Due to the complexity of analytical SQL queries, specialized mechanisms are necessary to enable the user-friendly formulation of...

Analytical SQL queries are a valuable source of information. Query log analysis can provide insight into the usage of datasets and uncover knowledge that cannot be inferred from source schemas or content alone. To unlock this potential, flexible mechanisms for meta-querying are required. Syntactic and semantic aspects of queries must be considered...

Data-stream processing has continuously risen in importance as the amount of available data has been steadily increas- ing over the last decade. Besides traditional domains such as data-center monitoring and click analytics, there is an increasing number of network-enabled production machines that generate continuous streams of data. Due to their c...

Writing effective analytical queries requires data scientists to have in-depth knowledge of the existence, semantics, and usage context of data sources. Once gathered, such knowledge is informally shared within a specific team of data scientists, but usually is neither formalized nor shared with other teams. Potential synergies remain unused. We in...

We introduce Query-driven Knowledge-Sharing Systems (QKSS), which extend data management systems with knowledge-sharing capabilities to facilitate collaboration among different teams of data scientists. Relevant tacit knowledge about data sources is extracted from SQL query logs and externalized to support data source discovery and data integration...

In larger organizations, multiple teams of data scientists have to integrate data from heterogeneous data sources as preparation for data analysis tasks. Writing effective analytical queries requires data scientists to have in-depth knowledge of the existence, semantics, and usage context of data sources. Once gathered, such knowledge is informally...

Measuring the completeness of a data population often requires either expert knowledge or the presence of reference data. If neither is available, measuring population completeness becomes nontrivial. We present the ForCE approach (Forecasting for Completeness Estimation), a method to estimate the completeness of timestamped data using time series...

In the medical domain, data quality is very important. Since requirements and data change frequently, continuous and sustainable monitoring and improvement of data quality is necessary. Working together with managers of medical centers, we developed an architecture for a data quality monitoring system. The architecture enables domain experts to ada...

When using simulations for decision making, no matter the domain, the uncertainty of the simulations' output is an important concern. This uncertainty is traditionally estimated by propagating input uncertainties forward through the simulation model. However, this approach requires extensive data collection before the output uncertainty can be esti...

Configurable publish-subscribe middleware provides efficient support for the diverse Quality-of-Service (QoS) requirements of large-scale distributed applications. However, choosing the optimal middleware configuration to suit a specific application primarily remains a manual task within the responsibility of application developers. Existing config...

We present Massive Multiuser Event Infrastructure (M²etis), a configurable publish-subscribe middleware. M²etis uses discrete-event simulations and regression methods to translate declarative descriptions of event types in terms of the application domain to an optimal configuration. Configuration decisions are based on the Quality-of-Service (QoS)...

Distributed event-based systems have risen in significance over the last few years across many different application domains. Still, the configuration of available communication middleware solutions remains a tedious task driven by technical terms and manual performance optimization. We present the M 2 etis Quality-of-service-aware Semantics Modeli...

The alpha-OffSync project offers a synchronization concept for alpha-Flow, an electronic process support in heterogeneous inter-institutional scenarios in healthcare. A distributed case file is provided by alpha-Flow to represent workflow schemas as documents which are shared coequally to content documents. alpha-OffSync allows the detection and re...

The α-Flow project enables process support in heterogeneous and inter-institutional scenarios in healthcare. α-Flow provides a distributed case file and represents workflow schemas as documents which are shared coequally to content documents. The activity progress and data flow is controlled by process-related metadata. A use case will motivate use...