Nicola Hönle

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

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.

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.

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.

DCbot: exploring the Web as value-added service for location-based applications

Location-based services (LBS) are typically mobile applications that adapt their behavior to the spatial context of the user, e.g. by providing maps and navigational information of the users current position. Existing location-based applications rely on spatial data that is gathered and preprocessed especially for them and that is stored by particular data providers. Location-based applications can benefit from World Wide Web and additional information source, if, in a preprocessing step, Web pages are mapped to locations. A model for this is virtual information towers (VIT), spatial Web portals with a location and a visibility area that represents the region where the information is relevant. DCbot processes HTML pages in the WWW like a crawler of a search engine. It analyses the pages using pre-defined rules and spatial knowledge and maps them to locations.

Reference Management in a Loosely Coupled, Distributed Information System

References between objects in loosely coupled distributed information systems pose a problem. On the one hand, one tries to avoid referential inconsistencies like, e.g., dangling links in the WWW. On the other hand, using strict constraints as in databases may restrict the data providers severely. We present the solution to this problem that we developed for the Nexus system. The approach tolerates referential inconsistencies in the data while providing consistent query answers to users. For traversing references, we present a concept based on return references. This concept is especially suitable for infrequent object migrations and provides a good query performance. For scenarios where object migrations are frequent, we developed an alternative concept based on a distributed hash table.

Federating location-based data services

With the emerging availability of small and portable devices which are able to determine their position and to communicate wirelessly, mobile and spatially-aware applications become feasible. These applications rely on information that is bound to locations and managed by so-called location-based data services. Large-scale location-based systems have to cope efficiently with different types of data (mostly spatial or conventional). Each type poses its own requirements to the data server that is responsible for management and provisioning of the data. In addition to efficiency, it is overly important to provide for a combined and integrated usage of that data by the applications. In this paper we discuss various basic technologies to achieve a flexible, extensible, and scalable management of the context model and its data organized and managed by the different data servers. Based on a classification of location-based data services we introduce a service-oriented architecture that is built on a federation approach to efficiently support location-based applications. Furthermore, we report on the Nexus platform that realizes a viable implementation of that approach.

Integration mehrfach repräsentierter Straßenverkehrsdaten für eine föderierte Navigation (Integration of Road Data in Multiple Representations for Federated Navigation)

Die Forschergruppe neXus entwickelt eine offene, verteilte Plattform für Anwendungen mit Ortsbezug. Dieser Artikel beschreibt, wie Straßenverkehrsdaten aus unterschiedlichen Quellen in das gemeinsame Datenmodell der Plattform integriert werden können, um Navigationsanwendungen zu ermöglichen. Die Abbildung mehrfach repräsentierter Daten in einem einheitlichen Schema ist notwendig, um Anfragen auf Quellen zu verteilen, die Ergebnisse zusammenzufassen und so bestehende Daten weiter nutzen zu können.