Reliable distributed data stream management in mobile environments

The proliferation of sensor technology, especially in the context of embedded systems, has brought forward novel types of applications that make use of streams of continuously generated sensor data. Many applications like telemonitoring in healthcare or roadside traffic monitoring and control particularly require data stream management (DSM) to be provided in a distributed, yet reliable way. This is even more important when DSM applications are deployed in a failure-prone distributed setting including resource-limited mobile devices, for instance in applications which aim at remotely monitoring mobile patients. In this paper, we introduce a model for distributed and reliable DSM. The contribution of this paper is threefold. First, in analogy to the SQL isolation levels, we define levels of reliability and describe necessary consistency constraints for distributed DSM that specify the tolerated loss, delay, or re-ordering of data stream elements, respectively. Second, we use this model to design and analyze an algorithm for reliable distributed DSM, namely efficient coordinated operator checkpointing (ECOC). We show that ECOC provides lossless and delay-limited reliable data stream management and thus can be used in critical application domains such as healthcare, where the loss of data stream elements can not be tolerated. Third, we present detailed performance evaluations of the ECOC algorithm running on mobile, resource-limited devices. In particular, we can show that ECOC provides a high level of reliability while, at the same time, featuring good performance characteristics with moderate resource consumption

Efficient and reliable data stream management

The proliferation of sensor technology, especially in the context of embedded systems, and the progress of ubiquitous computing strongly supports new types of applications that make use of streams of continuously generated sensor data. Applications like telemonitoring in healthcare or roadside traffic management systems urgently require reliable data stream management (DSM) in a failure-prone distributed setting including resource-limited mobile and embedded devices. In order to motivate and illustrate our considerations, we investigate an application in the field of telemonitoring for e-health in detail. Telemonitoring applications in healthcare are demanding the key issue of this thesis, namely efficient and reliable data stream management. Due to its importance for applicability, effectiveness and flexibility is also considered in this work. The main contribution of this thesis is threefold. First, in analogy to the SQL isolation levels, we define a model for reliable DSM based on levels of reliability and describe necessary consistency constraints for distributed DSM. Second, we present and analyze a novel algorithm for reliable distributed DSM, namely efficient coordinated operator checkpointing (ECOC) based on this model. We show that ECOC provides lossless and delay-limited reliable data stream management and thus can be used in critical application domains such as healthcare, where the loss of data stream elements cannot be tolerated. The ECOC approach considers fine-grained backups at operator level, which allows for the flexible and efficient usage of available resources in a network. Moreover, ECOC is optimized to reduce the overhead of checkpointing and to support complex stream process execution graphs, which include joins, splits and even cycles within data stream flows. Third, we present detailed performance evaluations of the ECOC algorithm running in a network of both stationary server nodes and mobile, resource-limited devices. Finally, the applicability of our approach is presented by an e-Health telemonitoring demo prototype developed with real-world sensors within this thesis. All evaluations and the demo application are based on the distributed DSM infrastructure prototype OSIRIS-SE. The Java implementation allows for running the same software on both mobile and stationary devices

Hyperdatabases for Peer-to-Peer Data Stream Processing

New sensor technologies, powerful mobile devices, and wireless communication standards strongly proliferate ubiquitous and pervasive computing. This is particularly true for healthcare applications. Modern non-invasive unobtrusive sensors enable sophisticated monitoring of the human body. As a result of this monitoring a growing amount of elderly people suffering from chronic diseases will benefit from an enhanced quality of life and improved treatment. Healthcare applications process a vast amount of continuously generated data, this has become a serious challenge. Data stream management addresses this problem in general. However, healthcare applications have additional requirements, e.g., flexibility and reliability. As a consequence, we propose the combination of a sophisticated information infrastructure, called hyperdatabase, and data stream management. Hyperdatabases already allow for reliable process management in a peer-to-peer fashion. In this paper, we elaborate the close relation between distributed process management and data stream management. Further, we show that data stream processing can significantly benefit from an infrastructure for reliable process management. We present the architecture of our prototype infrastructure that supports processing of continuous data streams with a high degree of flexibility and reliability

Extending the DelosDLMS by the FAST Annotation Service

DelosDLMS [9] is a prototype of a next-generation Digital Library Management System (DLMS), jointly developed by partners of the EU-funded project DELOS 1 (a Network of Excellence on Digital Libraries). The goal of DelosDLMS is t