Andreas M. Wahl

Automated quality-of-service-aware configuration of publish-subscribe systems at design-time

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 configurable middleware approaches either do not allow extensive configuration or lack the possibility to specify QoS requirements in a domain-specific terminology. In this paper we present a novel methodology for the QoS-aware configuration of publish-subscribe middleware, which relies on discrete-event simulations and supervised learning techniques to automatically translate declarative descriptions of the application domain to an optimal configuration. The configuration is conducted at design-time to minimize possible run-time impacts. We implemented a design-time configurable middleware and evaluated the quality of the automated configuration workflow as well as the performance of event dissemination. Our results show that simulation experiments in conjunction with regression methods can be used to reliably derive optimal middleware configurations without further user interaction. We clarify that extensive design-time configurability does not compromise run-time performance and suits the requirements of demanding applications.

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 minimally-intrusive approach for query-driven data integration systems

Integrating data from very large, dynamic, heterogeneous and autonomous data sources is a key requirement to satisfy growing information needs. In order to allow for ad-hoc answering of analytical questions, necessary up-front integration effort must be minimized and data integration systems must be adapted to the expectations and requirements of their users. While existing approaches offer support for incremental data integration, they require significant usage effort. They usually rely on explicit user feedback or invade the proven analysis workflows of their users. We propose a minimally-intrusive approach for query-driven data integration systems that allows for ad-hoc analysis of different data sources and minimizes the alteration of established analysis workflows. We suggest mechanisms for data source discovery and incremental data integration that primarily rely on implicit user feedback collected by query log analysis. Our proposed system architecture supports different types of data sources and integrates with existing data analysis tools.

Adaptive Version Clocks and the OffSync Protocol

The α-OffSync project offers a synchronization concept for α-Flow, an electronic process support in heterogeneous inter-institutional scenarios in healthcare. A distributed case file is provided by α-Flow to represent workflow schemas as documents which are shared coequally to content documents. α-OffSync allows the detection and resolution of global concurrent modification conflicts on process artifacts based on locally available information by using logical timestamps. Further mechanisms are included to ease the management and dynamic extension of the group of participating actors.

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.

ForCE: Is Estimation of Data Completeness Through Time Series Forecasts Feasible?

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 forecasting. We evaluate the method’s feasibility using a medical domain real-world dataset, which we provide for download. The method is compared to three baselines. ForCE manages to surpass all three.

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.