Carlos Lübbe

An abstract processing model for the quality of context data

Data quality can be relevant to many applications. Especially applications coping with sensor data cannot take a single sensor value for granted. Because of technical and physical restrictions each sensor reading is associated with an uncertainty. To improve quality, an application can combine data values from different sensors or, more generally, data providers. But as different data providers may have diverse opinions about a certain real world phenomenon, another issue arises: inconsistency. When handling data from different data providers, the application needs to consider their trustworthiness. This naturally introduces a third aspect of quality: trust. In this paper we propose a novel processing model integrating the three aspects of quality: uncertainty, inconsistency and trust.

DiSCO: A Distributed Semantic Cache Overlay for Location-Based Services

Location-based services (LBS) have gained tremendous popularity with millions of simultaneous users daily. LBS handle very large data volumes and face enormous query loads. Both the data and the queries possess high locality: spatial data is distributed very unevenly around the globe, query load is different throughout the day, and users often search for similar things in the same places. This causes high load peaks at the data tier of LBS, which may seriously degrade performance. To cope with these load peaks, we present DiSCO, a distributed semantic cache overlay for LBS. DiSCO exploits the spatial, temporal and semantic locality in the queries of LBS and distributes frequently accessed data over many nodes. Based on the Content-Addressable Network (CAN) peer-to-peer approach, DiSCO achieves high scalability by partitioning data using spatial proximity. Our evaluation shows that DiSCO significantly reduces queries to the underlying data tier.

Exploiting constraints to build a flexible and extensible data stream processing middleware

A wide range of real-time applications process stream-based data. To process this stream-based data in an application-independent manner, many stream processing systems have been built. However, none of them reached a huge domain of applications, such as databases did. This is due to the fact that they do not consider the specific needs of real-time applications. For instance, an application which visualizes stream-based data has stringent timing constraints, or may even need a specific hardware environment to smoothly process the data. Furthermore, users may even add additional constraints. E.g., for security reasons they may want to restrict the set of nodes that participates in processing. Thus, constraints naturally arise on different levels of query processing. In this work we classify constraints that occur on different levels of query processing. Furthermore we propose a scheme to classify the constraints and show how these can be integrated into the query processing of the distributed data stream middleware NexusDS.

Elastic Load-Balancing in a Distributed Spatial Cache Overlay

Location-based services (LBS) have to cope with increasing query loads at their data tier. Yet, the data access patterns of LBS typically possess spatial locality. Therefore, a dedicated spatial cache which provides efficient access to the data currently needed may considerably reduce this load. To ensure high throughput throughout the entire execution, previous work introduced an elastic load-balancing mechanism for multiple cache nodes that collaborate in a distributed spatial cache overlay. However, calibrating such a load-balancing mechanism is a non-trivial task, as several parameters influence such a system. We demonstrate a simulation platform (SimPl) for elastic load-balancing. SimPl enables a network administrator to set up several overlay topologies and calibrate their system parameters using different spatial data access patterns. A live visualization of the simulated overlay enables intuitive comparison of overlay topologies and their load-balancing abilities.

NexusVIS: A distributed visualization toolkit for mobile applications

Many mobile pervasive applications need to visualize information about the users geographic surroundings combined with data from sensors, which determine the users context. We demonstrate NexusVIS, a distributed visualization toolkit for mobile applications. By building upon an existing data stream processing system we enable applications to define distributed visualization processes as continuous queries. This allows applications to define visualization semantics descriptively. Moreover, NexusVIS is capable of adapting the visual query at runtime, and thus allows to navigate in the visualized scene both automatically and manually through user control.

Holistic Load-Balancing in a Distributed Spatial Cache

A steadily growing number of people using location based services (LBS) inflict massive query loads on the data tier of an LBS. As such queries usually possess considerable overlap, multiple cache nodes collaborating in a distributed spatial cache can provide scalable access to frequently used data. To preserve high throughput throughout the complete execution process, it is necessary to balance the accumulating load among the participating cache nodes. In this work, we identify three key-indicators to improve resource utilization during the load-balancing process: data skew, anticipated data access patterns and dynamic load peaks. For this reason, we introduce a comprehensive mathematical model to express the key-indicators as probability distribution functions. We fuse the different key-indicators into a single holistic distribution model. In the course of this, we devise a methodology from our holistic distribution model towards a distributed spatial cache offering improved load-balancing facilities.

SimPl: A Simulation Platform for Elastic Load-Balancing in a Distributed Spatial Cache Overlay

Location-based services (LBS) have to cope with increasing query loads at their data tier. Yet, the data access patterns of LBS typically possess spatial locality. Therefore, a dedicated spatial cache which provides efficient access to the data currently needed may considerably reduce this load. To ensure high throughput throughout the entire execution, previous work introduced an elastic load-balancing mechanism for multiple cache nodes that collaborate in a distributed spatial cache overlay. However, calibrating such a load-balancing mechanism is a non-trivial task, as several parameters influence such a system. We demonstrate a simulation platform (SimPl) for elastic load-balancing. SimPl enables a network administrator to set up several overlay topologies and calibrate their system parameters using different spatial data access patterns. A live visualization of the simulated overlay enables intuitive comparison of overlay topologies and their load-balancing abilities.