Levent Bolelli

Enabling collaborative geoinformation access and decision‐making through a natural, multimodal interface

Current computing systems do not support human work effectively. They restrict human–computer interaction to one mode at a time and are designed with an assumption that use will be by individuals (rather than groups), directing (rather than interacting with) the system. To support the ways in which humans work and interact, a new paradigm for computing is required that is multimodal, rather than unimodal, collaborative, rather than personal, and dialogue‐enabled, rather than unidirectional. To address this challenge, we have developed an approach for designing natural, multimodal, multiuser dialogue‐enabled interfaces to geographic information systems that make use of large‐screen displays and integrated speech–gesture interaction. After outlining our goals and providing a brief overview of relevant literature, we introduce the Dialogue‐Assisted Visual Environment for Geoinformation (DAVE_G). DAVE_G is being developed using a human‐centred systems approach that contextualizes development and assessment in the current practice of potential users. In keeping with this human‐centred approach, we outline a user task analysis and associated scenario development that implementation is designed to support (grounded in the context of emergency response), review our own precursors to the current prototype system and discuss how the current prototype extends upon past work, provide a detailed description of the architecture that underlies the current system, and introduce the approach implemented for enabling mixed‐initiative human–computer dialogue. We conclude with a discussion of goals for future research.

Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation

Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in various domains. In this paper, we propose a generative model based on latent Dirichlet allocation that integrates the temporal ordering of the documents into the generative process in an iterative fashion. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. Our experimental results on a collection of academic papers from CiteSeer repository show that segmented topic model can effectively detect distinct topics and their evolution over time.

Finding topic trends in digital libraries

We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. We conduct experiments on the collection of academic papers from CiteSeer repository. We augment the text corpus with the addition of user queries and tags and integrate the citation graph to boost the weight of the topical terms. The experiment results show that segmented topic model can effectively detect distinct topics and their evolution over time.

Clustering scientific literature using sparse citation graph analysis

It is well known that connectivity analysis of linked documents provides significant information about the structure of the document space for unsupervised learning tasks. However, the ability to identify distinct clusters of documents based on link graph analysis is proportional to the density of the graph and depends on the availability of the linking and/or linked documents in the collection. In this paper, we present an information theoretic approach towards measuring the significance of individual words based on the underlying link structure of the document collection. This enables us to generate a non-uniform weight distribution of the feature space which is used to augment the original corpus-based document similarities. The experimental results on the collection of scientific literature show that our method achieves better separation of distinct groups of documents, yielding improved clustering solutions.

Multimodal interface platform for geographical information systems (GeoMIP) in crisis management

A novel interface system for accessing geospatial data (GeoMIP) has been developed that realizes a user-centered multimodal speech/gesture interface for addressing some of the critical needs in crisis management. In this system we primarily developed vision sensing algorithms, speech integration, multimodality fusion, and rule-based mapping of multimodal user input to GIS database queries. A demo system of this interface has been developed for the Port Authority NJ/NY and is explained here.

CiteSeer χ : a scalable autonomous scientific digital library

CiteSeer is a scientific literature digital library and search engine which automatically crawls and indexes scientific documents in the fields of computer and information science. Since its inception in 1997 CiteSeer has grown to index over 730,000 documents and serves over 800,000 requests daily, pushing the limits of the current systems capabilities. In addition, CiteSeers monolithic architecture inconveniences system maintenance and reduces the flexibility of the system in terms of new feature development, algorithm updates, and system interoperability. In this paper, we discuss the problems of the current CiteSeer architecture and propose a new architecture for a next generation CiteSeer application. The new architecture is based on modular web services and pluggable service components. Preliminary results based on a prototype system show the new architecture enhances flexibility, scalability, and performance for CiteSeer. In addition, new services in development for the next generation CiteSeer system are discussed.

Multimodal interaction for distributed collaboration

We demonstrate a same-time different-place collaboration system for managing crisis situations using geospatial information. Our system enables distributed spatial decision-making by providing a multimodal interface to team members. Decision makers in front of large screen displays and/or desktop computers, and emergency responders in the field with tablet PCs can engage in collaborative activities for situation assessment and emergency response.