The rapid proliferation of online forums has made it possible for people to share their intentions, wishes and experiences by posting comments with the aim of getting advice from other members of the forum. Extracting intentions from these comments provides valuable insight for companies who can exploit it to get a competitive edge. However, given the very large amount of this kind of online comments, manually extracting intentions is impractical, time consuming and expensive. Companies need tools that analyze the text to extract intentions and details about them. In this paper we propose to demo one such tool called Intention Insider which has been developed at HP Labs in close collaboration with business units and a few selected customers. The tool can ingest content from online forums or from uploaded files and quickly sift through very large amounts of comments to extract intention information. This information is loaded into a data warehouse to be correlated with other structured data and queried to produce interactive reports and dynamic visualizations that facilitate its exploration at detailed and aggregate levels.

The rise of Web 2.0 with its increasingly popular social sites like Twitter, Facebook, blogs and review sites has motivated people to express their opinions publicly and more frequently than ever before. This has fueled the emerging field known as sentiment analysis whose goal is to translate the vagaries of human emotion into hard data. LCI is a social channel analysis platform that taps into what is being said to understand the sentiment with the particular ability of doing so in near real-time. LCI integrates novel algorithms for sentiment analysis and a configurable dashboard with different kinds of charts including dynamic ones that change as new data is ingested. LCI has been researched and prototyped at HP Labs in close interaction with the Business Intelligence Solutions (BIS) Division and a few customers. This paper presents an overview of the architecture and some of its key components and algorithms, focusing in particular on how LCI deals with Twitter and illustrating its capabilities with selected use cases.

The mobile shopping assistant (MSA) is a mobile application platform to deliver real-time, in-store, and personalized services, such as personalized product offerings and in-store customer advisory support, to improve the shopping experiences of in-store customers. The service delivery network that powers the MSA involves retail stores and their business partners such as manufacturers. This paper presents the core technologies that we developed in this cross-organizational service network to support the MSA and its personalized services, with focus on service delivery, customer behavior understanding and information sharing. Our event-based techniques allow customers, stores and manufacturers to deliver and consume the services in a loosely coupled manner, thus solving a critical store-specific real-time engagement problem in a seamless way. Service response tracking enables the stores to construct a comprehensive view of a customerpsilas in-store shopping behavior. Finally, the cross-organizational authorization-based access control mechanism effectively enforces information sharing between the stores and their partners.

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.

In this paper we describe an exploratory study of a mobile cosmetic advisory system that enables women to select appropriate colors of cosmetics. This system is intended for commercial use to address the problem of foundation color selection. Although women are primarily responsible for making most purchasing decisions in the US, we found very few studies to assess the adoption of retail related mobile services by women. Based on surveys, semi-structured interviews, and focus groups, we have identified a number of design factors that should be considered when designing mobile services for women consumers. The results of our study indicate that while usefulness is an important factor, other design aspects such as mobile vs. kiosk, installed vs. existing software, technical comfort vs. social comfort, social vs. individual, privacy and trust should also be accounted for.

In this paper, we discuss challenges and provide solutions for capturing and maintaining accurate models of user profiles using semantic web technologies, by aggregating and sharing distributed fragments of user profile information spread over multiple services. Our framework for profile management allows for evolvable, extensible and expressive user profiles. We have implemented a prototype, targeting the retail domain, on the HP Labs Retail Store Assistant.

It is extremely hard for a global organization with services over multiple channels to capture a consistent and unified view of its data, services, and interactions. While SOA and web services are addressing integration and interoperability problems, it is painful for an operational organization with legacy systems to quickly switch to service-based methods. We need methods to combine advantages of traditional (i.e. web, desktop, or mobile) application development environments and service-based deployments.In this paper, we focus on the design and implementation of session management as a core service to support business processes and go beyond application-specific sessions and web sessions. We develop local session components for different platforms and complement them with a remote "session service" that is independent of applications and platforms. We aim to close the gap between the two worlds by combining their performance, availability and interoperability advantages.

The 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 paper, 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.

In previous work (1993,1996), we introduced the notion of instrumented logical sensor systems (ILSS) that are derived from a modeling and design methodology. The instrumented sensor approach is based on a sensori-computational model which defines the components of the sensor system in terms of their functionality, accuracy, 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 multisensor systems. In this paper, we demonstrate the practicality of this approach and discuss several design and implementation aspects in the context of mobile robot applications.

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 three major issues: (1) sensor distribution patterns, (2) local sensor frames, and (3) autonomous robot sensor snow exploitation techniques.

Develops theoretical and practical techniques to recover the pose of planar surfaces with minimal sensor readings and motion. A proof is given that two sonar readings suffice to precisely locate a single planar surface that returns the sonar's signal. An implementation is described that takes into account the physical restrictions on actual Polaroid sensors and experiments are described based on actual Polaroid sensor data.

Developing an environment that enables optimal and flexible design of robot manipulators using reconfigurable links, joints, actuators, and sensors is an essential step for efficient robot design and prototyping. Such an environment should have the right "mix" of software and hardware components for designing the physical parts and the controllers, and for the algorithmic control of the robot modules (kinematics, inverse kinematics, dynamics, trajectory planning, analog control and digital computer control). Specifying object-based communications and catalog mechanisms between the software modules, controllers, physical parts, CAD designs, and actuator and sensor components is a necessary step in the prototyping activities. In this paper the authors propose a flexible prototyping environment for robot manipulators with the required subsystems and interfaces between the different components of this environment.

In designing robot manipulators, the interaction between several modules (S/W, VLSI, CAD, CAM, robotics, and control) illustrates an interdisciplinary prototyping environment that includes different types of information that are radically different but combined in a coordinated way. This paper describes the analysis and design of a 3-link robot manipulator prototype as part of a research project for building a prototyping environment for electromechanical systems. This prototype robot can be used as an educational tool in robotics and control classes.>

We address the problem of intelligent sensing in this work. In particular, we use discrete event dynamic systems (DEDS) to guide the sensing of mechanical parts for industrial inspection and reverse engineering.>

A perception framework for intelligent inspection and reverse engineering is addressed. The use of discrete event dynamic systems (DEDS) to guide the sensing of mechanical parts is investigated. Dynamic recursive finite state machines (DRFSMs) are introduced as a new DEDS tool for utilizing the recursive nature of the mechanical parts under consideration. The proposed framework uses DRFSM DEDS for constructing an observer for exploration and inspection purposes. A sensing /spl rarr/ CAD interface is constructed for the automatic reconstruction of parts from visual data. A graphical interface for designing DRFSM DEDS controllers is implemented.>