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In this paper we describe techniques for the discovery and construction of user profiles. Leveraging from the emergent data web, our system addresses the problem of sparseness of user profile information currently faced by both asserted and inferred profile systems. A profile mediator, that dynamically builds the most suitable user profile for a particular service or interaction in real-time, is employed in our prototype implementation.

We propose to deploy and exploit a large number of inexpensive sensors to obtain information or trigger actions over a wide geographic area. Sensors may be of diverse physical natures: acoustic, IR, seismic, chemical, magnetic, thermal, etc. We describe here three major issues:( 1 ) sensor distribution pattems, (2) local sensor frames, and (3) autonomous robot sensor snow exploitation techniques.

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In this paper we present a prototype for capturing retail related consumer intent using multiple devices and in multimodal input formats such as text, audio, and still images. The prototype was used in a longitudinal user study to analyze the process that consumers go through in order to make purchasing decisions. Based on these findings, we recommend desirable features for information management systems specifically designed for the retail environment.

Sensor systems are becoming ubiquitous throughout society, yet their design, construction, and operation are still more of an art than a science. In this paper, we define, develop, and apply a formal se mantics for sensor systems that provides a theoretical framework for an integrated software architecture for modeling sensor-based con trol systems. Our goal is to develop a design framework that allows the user to model, analyze, and experiment with different versions of a sensor system. This includes the ability to build and modify multisensor systems and to monitor and debug both the output of the system and the effect of any modification in terms of robustness, efficiency, and error measures. The notion of Instrumented Logi cal Sensor Systems (ILSS) that are derived from this modeling and design methodology is introduced. The instrumented sensor ap proach is based on a sensori-computational model that defines the components of the sensor system in terms of theirfunctionality, accu racy, robustness, and efficiency. This approach provides a uniform specification language to define sensor systems as a composition of smaller, predefined components. From a software-engineering standpoint, this addresses the issues of modularity, reusability, and reliability for building complex systems. An example is given that compares vision and sonar techniques for the recovery of wall pose.

application management, auto-discovery, modeling, model-based discovery, software agentsThe increasing complexity of enterprise applications, the expanding number of networked machines, and the rapid deployment of Internet-based business applications (e-commerce), emphasize the importance and value of application management. One of the main problems in current application management products is the amount of time and effort needed to install and customize them. Application auto-discovery is a key technology for solving this problem. In this report, we present a generic approach to application auto-discovery along with some examples. Our approach is to create a model-based discovery engine that is driven by an application template model. While the application template model captures the variation from one application to another, the auto-discovery engine uses sophisticated mechanisms such as scoping to execute an invariant auto-discovery process. © Copyright Hewlett-Packard Company 1999Internal Accession Date Only AbstractThe increasing complexity of enterprise applications, the expanding number of networked machines, and the rapid deployment of Internet-based business applications (e-commerce), emphasize the importance and value of application management. One of the main problems in current application management products is the amount of time and effort needed to install and customize them. Application auto-discovery is a key technology for solving this problem. In this report, we present a generic approach to application auto-discovery along with some examples. Our approach is to create a model-based discovery engine that is driven by an application template model. While the application template model captures the variation from one application to another, the auto-discovery engine uses sophisticated mechanisms such as scoping to execute an invariant auto-discovery process.

2 Hewlett Packard Labs, USA Sensor fusion involves a wide spectrum of areas, ranging from hardware for sensors and data acquisition, through analog and digital processing of the data, up to symbolic analysis all within a theoretical framework that solves some class of problem. We review recent work on major problems in sensor fusion in the areas of theory, architecture, agents, robotics, and navigation. Finally, we describe our work on major architectural techniques for designing and developing wide area sensor network systems and for achieving robustness in muttisensor systems. I n t r o d u c t i o nMultiple sensors in a control system can be used to provide: -m o r e i n f o r m a t i o n , -r o b u s t n e s s , and -c o m p l e m e n t a r y i n f o r m a t i o n .In this chapter, we emphasize the first two of these. In particular, some recent work on wide area sensor systems is described, as well as tools which permit empirical performance analysis of sensor systems.By m o r e i n f o r m a t i o n we mean that the sensors are used to monitor wider aspects of a system; this may mean over a wider geographical area (e.g. a power grid, telephone system, etc.) or diverse aspects of the system (e.g. air speed, attitude, acceleration of a plane). Quite extensive systems can be monitored, and thus, more informed control options made available. This is achieved through a higher level view of the interpretation of the sensor readings in the context of the entire set.R o b u s t n e s s has several dimensions to it. First, statistical techniques can be applied to obtain better estimates from multiple instances of the same type sensor, o1" multiple readings from a single sensor [15]. Fault tolerance is another aspect of robustness which becomes possible when replacement sensors exist. This brings up another issue which is the need to monitor sensor activity and the ability to make tests to determine the state of the system (e.g. camera failed) and strategies to switch to alternative methods if a sensor is compromised.