Daniela Nicklas

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

A Model-Based, Open Architecture for Mobile, Spatially Aware Applications

With the emerging availability of small and portable devices that 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. In this paper we present NEXUS, a platform for such applications, which is open for both new applications and new information providers, similar to the World Wide Web. Distributed servers provide location-based information, which is federated to an integrated view for the applications. To achieve this goal, we present the concept of the Augmented World Model, which is a common data model for location-based information. We give an example to show how applications can use this platform.

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

On building location aware applications using an open platform based on the NEXUS Augmented World Model

How should the World Wide Web look like if it were for location-based information? And how would mobile, spatially aware applications deal with such a platform? In this paper we present the neXus Augmented World Model, an object oriented data model which plays a major role in an open framework for both providers of location-based information and new kinds of applications: the neXus platform. We illustrate the usability of the model with several sample applications and show the extensibility of this framework. At last we present a stepwise approach for building spatially aware applications in this environment.

Odysseus: a highly customizable framework for creating efficient event stream management systems

Odysseus is a flexible, feature-rich and extensible framework to design event stream management systems and was developed to support research in event stream processing. It provides a systematic approach to define sources and queries, execution and presentation of results. Odysseus offers basic functionality for fast deployment, but due to its modular architecture, users can easily configure and expand them to meet a large set of applications and research questions.

Deep integration of spatial query processing into native RDF triple stores

Semantic Web technologies, most notably RDF, are well-suited to cope with typical challenges in spatial data management including analyzing complex relations between entities, integrating heterogeneous data sources and exploiting poorly structured data, e.g., from web communities. Also, RDF can easily represent spatial relationships, as long as the location information is symbolic, i.e., represented by places that have a name. What is widely missing is support for geographic and geometric information, such as coordinates or spatial polygons, which is needed in many applications that deal with sensor data or map data. This calls for efficient data management systems which are capable of querying large amounts of RDF data and support spatial query predicates. We present a native RDF triple store implementation with deeply integrated spatial query functionality. We model spatial features in RDF as literals of a complex geometry type and express spatial predicates as SPARQL filter functions on this type. This makes it possible to use W3Cs standardized SPARQL query language as-is, i.e., without any modifications or extensions for spatial queries. We evaluate the characteristics of our system on very large data volumes.

Context-Aware Mashups for Mobile Devices

With the Web 2.0 trend and its participation of end-users more and more data and information services are online accessible, such as web sites, Wikis, or web services. So-called mashups--web applications that integrate data from more than one source into an integrated service--can be easily realized using scripting languages. Also, mobile devices are increasingly powerful, have ubiquitous access to the Web and feature local sensors, such as GPS. Thus, mobile applications can adapt to the mobile users current situation. n nWe examine how context-aware mashups can be created. One challenge is the provisioning of context data to the mobile application. For this, we discuss different ways to integrate context data, such as the users position, into web applications. Moreover, we assess different data formats and the overall performance. Finally, we present the Telar mashup platform, a client-server solution for location-based mashups for mobile devices such as the Nokia N810 Internet Tablet.

The TELAR mobile mashup platform for Nokia internet tablets

With the Web 2.0 trend and its participation of end-users more and more data and information services are online accessible, such as web sites, Wikis, or web services. The integration of this plethora of information is taken over by the community: so-called Mashups---web applications that combine data from more than one source into an integrated service---spring up like mushrooms, because they can be easily realized using script languages and web development platforms. Another trend is that mobile devices that get more and more powerful have ubiquitous access to the Web. Local sensors (such as GPS) can easily be connected to these devices. Thus, mobile applications can adapt to the current situation of the user, which can change frequently because of his or her mobility.n In this demonstration, we present the Telar Mashup platform, a client-server solution that facilitates the creation of adaptive Mashups for mobile devices such as the Nokia Internet Tablets. On the server side, wrappers allow the integration of data from web-based services. On the client side, a simple implementation of the DCCI specification is used to integrate context information of local sensors into the mobile Web browser, which adapts the Mashup to the users current location. We show an adaptive, mobile Mashup on the Nokia N810 Internet Tablet.

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.

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.