Daniel Klan

Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN

Wireless sensor networks are powerful, distributed, self-organizing systems used for event and environmental monitoring. In-network query processors like TinyDB offer a user friendly SQL-like application development. Due to the sensor nodes’ resource limitations, monolithic approaches often support only a restricted number of operators. For this reason, complex processing is typically outsourced to the base station. Nevertheless, previous work has shown that complete or partial in-network processing can be more efficient than the base station approach. In this paper, we introduce AnduIN, a system for developing, deploying, and running complex in-network processing tasks. In particular, we present the query planning and execution strategies used in AnduIN, a system combining sensor-local in-network processing and a data stream engine. Query planning employs a multi-dimensional cost model taking energy consumption into account and decides autonomously which query parts will be processed within the sensor network and which parts will be processed at the central instance.

Data3 -- A Kinect Interface for OLAP Using Complex Event Processing

Motion sensing input devices like Microsofts Kinect offer an alternative to traditional computer input devices like keyboards and mouses. Daily new applications using this interface appear. Most of them implement their own gesture detection. In our demonstration we show a new approach using the data stream engine Andu IN. The gesture detection is done based on Andu INs complex event processing functionality. This way we build a system that allows to define new and complex gestures on the basis of a declarative programming interface. On this basis our demonstration data3 provides a basic natural interaction OLAP interface for a sample star schema database using Microsofts Kinect.

Adaptive burst detection in a stream engine

Detecting bursts in data streams is an important and challenging task. Due to the complexity of this task, usually burst detection cannot be formulated using standard query operators. Therefore, we show how to integrate burst detection for stationary as well as non-stationary data into query formulation and processing, from the language level to the operator level. Afterwards, we present fundamentals of threshold-based burst detection. We focus on the applicability of time series forecasting techniques in order to dynamically identify suitable thresholds for stream data containing arbitrary trends and periods. The proposed approach is evaluated with respect to quality and performance on synthetic and real-world sensor data using a full-fledged DSMS.

Distributed Data Summaries for Approximate Query Processing in PDMS

Evolving from heterogeneous database systems one of the main problems in peer data management systems (PDMS) is distributed query processing. With the absence of global knowledge such strategies have to focus on routing the query efficiently to only those peers that are most likely to contribute to the final result. Using routing indexes is one possibility to achieve this. Since data may change over time these structures have to be updated and maintained which can be very expensive. In this paper, we present a novel kind of routing indexes that enables efficient query routing. Furthermore, we propose a threshold based update strategy that can help to reduce maintenance costs by far. We exemplify the benefit of these indexes using a distributed skyline strategy as an example. Finally, we show how relaxing exactness requirements, that are usually posed on results, can compensate the use of slightly outdated index information

Power-aware data analysis in sensor networks

Sensor networks have evolved to a powerful infrastructure component for event monitoring in many application scenarios. In addition to simple filter and aggregation operations, an important task in processing sensor data is data mining - the identification of relevant information and patterns. Limited capabilities of sensor nodes in terms of storage and processing capacity, battery lifetime, and communication demand a power-efficient, preferably sensor-local processing. In this paper, we present AnduIN, a system for developing, deploying, and running in-network data mining tasks. The system consists of a data stream processing engine, a library of operators for sensor-local processing, a box-and-arrow editor for specifying data mining tasks and deployment, a GUI providing the user with current information about the network and running queries, and an alerter notifying the user if a better query execution plan is available. At the demonstration site, we plan to show our system in action using burst detection as example application.

When is it time to rethink the aggregate configuration of your OLAP server

OLAP servers based on relational backends typically exploit materialized aggregate tables to improve response times of complex analytical queries. One of the key problems in this context is the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper, we present a system that continuously monitors the workload and raises a quantified alert, when a better configuration is available. We address the tasks of query monitoring and view selection at the OLAP level instead of the SQL level, which simplifies the containment checks as well as rewriting and in this way helps to reduce the complexity of the backend system. At the demo we plan to show how our system works, i.e., how the system reacts upon arbitrary (interactive) workloads and how the user is alerted that a better configuration is available.

Online Tuning of Aggregation Tables for OLAP

Materializing results from complex aggregation queries helps to significantly improve response times in OLAP servers. This problem is known as the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper we present an online approach for adjusting the configuration dynamically to the current workload. This approach is implemented as part of an open source OLAP server and acts on the level of multidimensional MDX queries. The work presents the details of cost estimation and optimization of the system demonstrated in [10] and extends it by an online tuning strategy.

Developing and deploying sensor network applications with AnduIN

Wireless sensor networks have become important architectures for many application scenarios, e.g., traffic monitoring or environmental monitoring in general. As these sensors are battery-powered, query processing strategies aim at minimizing energy consumption. Because sending all sensor readings to a central stream data management system consumes too much energy, parts of the query can already be processed within the network (in-network query processing). An important optimization criterion in this context is where to process which intermediate results and how to route them efficiently. To overcome these problems, we propose AnduIN, a system addressing these problems and offering an optimizer that decides which parts of the query should be processed within the sensor network. It also considers optimization with respect to complex data analysis tasks, such as burst detection. Furthermore, AnduIN offers a Web-based frontend for declarative query formulation and deployment. In this paper, we present our research prototype and focus on AnduINs components alleviating deployment and usability.

Decentralized managing of replication objects in massively distributed systems

Data replication is a central technique to increase availability and performance of distributed systems. While offering many advantages it also requires more effort for ensuring data consistency in case of updates. In the research literature various approaches for replication management in distributed databases have been presented, but they are mostly limited either in scalability or in the consistency guarantees they provide. On the other hand, P2P systems usually provide replication support but ignore the update problem. In this paper we present a new approach for managing replicated data in wide area distributed networks. Our solution is orthogonal to the underlying infrastructure and managed in a decentralized manner. It guarantees single-master consistency and allows updates at any node of the system by combining traditional replication techniques with ideas known from P2P systems.

Comparing and Refining Gossip Protocols for Fault Tolerance in Wireless P2P Systems

As a special type of wireless P2P systems, sensor networks are often deployed for detecting events caused by disasters. The peer-to-peer mode of the sensor system itself gets challenged either directly by damages of the disaster or by unreliable wireless links. This work explores possible failure models and compares the performance of several gossip protocols corresponding to the failures models. With further refinement of the gossip protocols, the performance in the failure modes caused by disasters is improved. The evaluation of our simulation results shows that using refined gossip protocols in correspondence to the failure models, the information aggregation and dissemination speed, communication cost and the accuracy of the aggregated data can be improved.