Peter K. Schwab

Alpha-Adaptive: Evolutionary Workflow Metadata in Distributed Document-Oriented Process Management

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 user-defined and demand-driven status attributes that are not known at design-time. α-Adaptive demonstrates how to apply the EAV data design approach and prototype-based programming concepts in order to provide an adaptive-evolutionary status attribute model for document-oriented processes.

Query-Driven Knowledge-Sharing for Data Integration and Collaborative Data Science

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 introduce our novel approach of Query-driven Knowledge-Sharing Systems (QKSS). A QKSS extends a data management system with knowledge-sharing capabilities to facilitate user collaboration without altering data analysis workflows. Collective knowledge from the query log is extracted to support data source discovery and data integration. Knowledge is formalized to enable its sharing across data scientist teams.

A graph-based framework for analyzing SQL query logs

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 along with contextual information. We present an extensible framework for analyzing SQL query logs. Query logs are mapped to a multi-relational graph model and queried using domain-specific traversal expressions. To enable concise and expressive meta-querying, semantic analyses are conducted on normalized relational algebra trees with accompanying schema lineage graphs. Syntactic analyses can be conducted on corresponding query texts and abstract syntax trees. Additional metadata allows to inspect the temporal and social context of each query. In this demonstration, we show how query log analysis with our framework can support data source discovery and facilitate collaborative data science. The audience can explore an exemplary query log to locate queries relevant to a data analysis scenario, conduct graph analyses on the log and assemble a customized logmonitoring dashboard.

Crossing an OCEAN of queries: analyzing SQL query logs with OCEANLog

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 meta-queries over an existing query log. Currently existing approaches do not sufficiently consider syntactic and semantic aspects of queries along with contextual information. During our demonstration, conference participants learn how to use the latest release of OCEANLog, a framework for analyzing SQL query logs. Our demonstration encompasses several scenarios. Participants can explore an existing query log using domain-specific graph traversal expressions, set up continuous subscriptions for changes in the graph, create time-based visualizations of query results, configure an OCEANLog instance and learn how to choose a decide which specific graph database to use. We also provide them with access to the native meta-query mechanisms of a DBMS to further emphasize the benefits of our graph-based approach.

Query-Driven Data Profiling with OCEANProfile

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 decide on the next query iteration solely based on the results of the last one. While existing approaches provide mechanisms to assess the results of a specific query, support for analyzing results in the context of the respective session remains mostly absent. Such approaches do also not seamlessly integrate with established tools and workflows. To overcome these problems, we introduce OCEANProfile, a framework for session-based profiling of query results. Query results are intercepted at driver level and streamed into our framework for automated data profiling. Result profiles can be compared with those of previous queries and visualized in a companion app compatible with existing analysis tools. Visualizations are automatically ranked according to their usefulness in the context of the respective session.

We Can Query More than We Can Tell: Facilitating Collaboration Through Query-Driven Knowledge-Sharing

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. By studying this collaborative knowledge, data scientists are enabled to formulate effective analytical queries over unfamiliar data sources.