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Wissenschaftliche Vorträge

Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing

Verteidigung im Promotionsverfahren von M.C.S. Yuanzhen Ji (Institut für Systemarchitektur, Professur für Systems Engineering)

28.11.2017, 9:00 Uhr, APB 1004 (Ratssaal)

Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data stream with continuous queries, which are issued once and return query results to users continuously as new tuples arrive. For stream-based applications, both the query-execution performance (in terms of, e.g., throughput and end-to-end latency) and the quality of produced query results (in terms of, e.g., accuracy and completeness) are important. However, a DSPS often needs to make tradeoffs between these two requirements, either because of the data imperfection within streams, or because of the limited computation capacity of the DSPS itself. Performance versus result-quality tradeoffs caused by data imperfection are inevitable, because the quality of the incoming data is beyond the control of the system, whereas tradeoffs caused by system limitations can be alleviated, even erased, by enhancing the system itself. This dissertation seeks to advance the state of the art on handling the performance versus result-quality tradeoffs in data stream processing caused by the above two aspects of reasons. For tradeoffs caused by data imperfections, this dissertation focuses on the typical data-imperfection problem of stream-disorder and proposes the concept of quality-driven disorder handling (QDDH). QDDH enables making flexible and user-configurable tradeoffs between the end-to-end latency and the query-result quality in face of stream disorder. Moreover, compare to existing disorder handling approaches, QDDH can significantly reduce the end-to-end latency, and at the same time provide users with desired query-result quality. In this dissertation, a generic buffer-based QDDH framework and three instantiations of the generic framework for distinct query types are presented. For tradeoffs caused by system limitations, this dissertation proposes a system-enhancement approach that combines the row-oriented and the column-oriented data layout and processing techniques in data stream processing to improve the throughput. To fully exploit the potential of such hybrid execution of continuous queries, a static cost-based optimizer is introduced, which works at the operator level and takes the unique property of execution plans of continuous queries, feasibility, into account.

Goal-based Workflow Adaptation for Role-based Resources in the Internet of Things

Verteidigung im Promotionsverfahren von Dipl.-Medieninf. Steffen Huber (Hochschule Karlsruhe)

28.11.2017, 14:00 Uhr, APB 1004 (Ratssaal)

In recent years, the Internet of Things (IoT) has increasingly received attention from the Business Process Management (BPM) community. The integration of sensors and actuators into Process-Aware Information Systems (PAIS) enables the collection of real-time data about physical properties and the direct manipulation of real-world objects. In a broader sense, IoT-aware workflows provide means for context-aware workflow execution involving virtual and physical entities. However, IoT-aware workflow management imposes new requirements on workflow modeling and execution that are outside the scope of current modeling languages and workflow management systems. Things in the IoT may vanish, appear or stay unknown during workflow execution, which renders their allocation as workflow resources infeasible at design time. Besides, capabilities of Things are often intended to be available only in a particular real-world context at runtime, e.g., a service robot inside a smart home should only operate at full speed, if there are no residents in direct proximity. Such contextual restrictions for the dynamic exposure of resource capabilities are not considered by current approaches in IoT resource management that use services for exposing device functionalities. With this work, we aim at providing the modeling and runtime support for defining such restrictions on workflow resources at design time and enabling the dynamic and context-sensitive runtime allocation of Things as workflow resources. Therefore, we contribute an ontology for the modeling of Things, Roles, capabilities, physical entities, and their context-sensitive interrelations. A Thing can play a specific Role only under certain contextual restrictions defined by Semantic Web Rule Language (SWRL) rules. At runtime, the existing relations between the individuals of the ontology represent the current state of interactions between the physical and the cyber world. Through the dynamic activation and deactivation of Roles at runtime, the behavior of a Thing can be adapted to the current physical context. Also, we allow workflow modelers to define the explicit goal of a workflow activity either by using semantic queries or by specifying high-level goals from a Tropos goal model. From a runtime perspective, we contribute the Semantic Access Layer (SAL) middleware to enable the transformation of activity goals into semantic queries as well as their execution on the ontology for role-based Things. The SAL enables the discovery of fitting Things, their allocation as workflow resources, the invocation of referenced IoT services, and the continuous monitoring of the allocated Things as part of the ontology.

Query Answering in Probabilistic Data and Knowledge Bases

Verteidigung im Promotionsverfahren von M. Sc. Ismail Ilkan Ceylan (Institut für Theoretische Informatik, Automatentheorie)

29.11.2017, 9:15 Uhr, APB 1004 (Ratssaal)

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of in/tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from database theory and logic for probabilistic query answering and related inference tasks.

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