Matthias Grossmann

Efficiently Managing Context Information for Large-Scale Scenarios

In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify major classes of context data and detail on their characteristics relevant for efficiently managing large amounts of it. Based on that, we argue that for large-scale systems it is beneficial to have special-purpose servers that are optimized for managing a certain class of context data. In the Nexus project we have implemented five servers for different classes of context data and a very flexible federation middleware integrating all these servers. For each of them, we highlight in which way the requirements of the targeted class of data are tackled and discuss our experiences

Benefits of Integrating Meta Data into a Context Model

Meta data - data about data - improves the value of the operational data by giving applications and users additional information on the datas origin, its precision or staleness. We outline the benefits of modeling meta data in context models: it can be used for resource finding, enhanced data selection, trust and data quality issues and sensor fusion. We show how meta data included into an object-based context model influences the data modeling and the selection process in the query language. Finally, we describe our implementation of the presented functionality in the Nexus platform

Adding High-level Reasoning to Efficient Low-level Context Management: A Hybrid Approach

Rule-based context reasoning is an expressive way to define situations, which are crucial for the implementation of many context-aware applications. Along the scenario of the Conference Guard application we show how this reasoning can be done both by leveraging an efficient context management (realized by the Nexus platform) and by a generic rule based service. We present the architecture of the Nexus semantic service, which uses the underlying definition of a low-level context model (the Nexus Augmented World Model) to carry out rules given in first order logic. We realize this service in a straight forward manner by using state-of-the-art software components (the Jena 2 framework [1]) and evaluate the number of instances this approach can handle. Our first experiences show that a pre-selection of instances is necessary if the semantic service should work on a large-scale context model.

Making the World Wide Space happen: New challenges for the Nexus context platform

Context-aware applications rely on models of the physical world. Within the Nexus project, we envision a World Wide Space which provides the conceptual and technological framework for integrating and sharing such context models in an open, global platform of context providers. In our ongoing research we tackle important challenges in such a platform including distributed processing of streamed context data, situation recognition by distributed reasoning, efficient management of context data histories, and quality of context information. In this paper we discuss our approach to cope with these challenges and present an extended Nexus architecture.

Usability analysis of compression algorithms for position data streams

With the increasing use of sensor technology, the compression of sensor data streams is getting more and more important to reduce both the costs of further processing as well as the data volume for persistent storage. A popular method for sensor data compression is to smooth the original measurement curve by an approximated curve, which is bounded by a given maximum error value. Measurement values from positioning systems like GPS are an interesting special case, because they consist of two spatial and one temporal dimension. Therefore various standard techniques for approximation calculations like regression or line simplification algorithms cannot be directly applied. In this paper, we portray our stream data management system NexusDS and an operator for compressing sensor data. For the operator, we implemented various compression algorithms for position data streams. We present the required adaptations and the different characteristics of the compression algorithms as well as the results of our evaluation experiments, and compare them with a map matching approach, specifically developed for position data.

NexusDS: a flexible and extensible middleware for distributed stream processing

Techniques for efficient and distributed processing of huge, unbound data streams have made some impact in the database community. Sensors and data sources, such as position data of moving objects, continuously produce data that is consumed, e.g., by location-aware applications. Depending on the domain of interest, e.g. visualization, the processing of such data often depends on domain-specific functionality. This functionality is specified in terms of dedicated operators that may require specialized hardware, e.g. GPUs. This creates a strong dependency which a data stream processing system must consider when deploying such operators. Many data stream processing systems have been presented so far. However, these systems assume homogeneous computing nodes, do not consider operator deployment constraints, and are not designed to address domain-specific needs. In this paper, we identify necessary features that a flexible and extensible middleware for distributed stream processing of context data must satisfy. We present NexusDS, our approach to achieve these requirements. In NexusDS, data processing is specified by orchestrating data flow graphs, which are modeled as processing pipelines of predefined and general operators as well as custom-built and domain-specific ones. We focus on easy extensibility and support for domain-specific operators and services that may even utilize specific hardware available on dedicated computing nodes.

Preprocessing Position Data of Mobile Objects

We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piece wise functions over time that approximate the real object movement and significantly reduce the initial data volume such that efficient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation - an input parameter of our algorithms - of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.

NexusScout: an advanced location-based application on a distributed, open mediation platform

This demo shows several advanced use cases of location-based services and demonstrates how these use cases are facilitated by a mediation middleware for spatial information, the Nexus Platform. The scenario shows how a mobile user can access location-based information via so called Virtual Information Towers, register spatial events, send and receive geographical messages or find her friends by displaying other mobile users. The platform facilitates these functions by transparently combining spatial data from a dynamically changing set of data providers, tracking mobile objects and observing registered spatial events.

On efficiently processing nearest neighbor queries in a loosely coupled set of data sources

We propose a family of algorithms for processing nearest neighbor (NN) queries in an integration middleware that provides federated access to numerous loosely coupled, autonomous data sources connected through the internet. Previous approaches for parallel and distributed NN queries considered all data sources as relevant, or determined the relevant ones in a single step by exploiting additional knowledge on object counts per data source. We propose a different approach that does not require such detailed statistics about the distribution of the data. It iteratively enlarges and shrinks the set of relevant data sources. Our experiments show that this yields considerable performance benefits with regard to both response time and effort. Additionally, we propose to use only moderate parallelism instead of querying all relevant data sources at the same time. This allows us to trade a slightly increased response time for a lot less effort, hence maximizing the cost profit ratio, as we show in our experiments. Thus, the proposed algorithms clearly extend the set of NN algorithms known so far.

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.