Showing papers on "Information integration published in 2010"

Multimodal fusion for multimedia analysis: a survey

Abstract: This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

Abstract: 1. MOTIVATION AND GOALS In recent years, increasing attention has been given to finding ways for combining, integrating and mediating heterogeneous sources of information for the purpose of providing better personalized services in many information seeking and ecommerce applications. Information heterogeneity can indeed be identified in any of the pillars of a recommender system: the modeling of user preferences, the description of resource contents, the modeling and exploitation of the context in which recommendations are made, and the characteristics of the suggested resource lists.

Intuition in Judgment and Decision Making: Extensive Thinking Without Effort

Abstract: We claim that intuition is capable of quickly processing multiple pieces of information without noticeable cognitive effort. We advocate a component view stating that intuitive processes in judgment and decision making are responsible for information integration and output formation (e.g., preference, choice), whereas analytic thinking mainly guides input formation such as search, generation, and change of information. We present empirical evidence corroborating this notion and show that integration of information and preference formation works without cognitive control and is unconstrained by the amount of encoded information and cognitive capacity. We discuss the implications of our findings for the bounded rationality perspective and the multiple strategy approach to judgment and decision making. Finally we outline a connectionist framework for integrating intuitive and analytic thought processes.

Audiovisual Information Fusion in Human–Computer Interfaces and Intelligent Environments: A Survey

Abstract: Microphones and cameras have been extensively used to observe and detect human activity and to facilitate natural modes of interaction between humans and intelligent systems. Human brain processes the audio and video modalities, extracting complementary and robust information from them. Intelligent systems with audiovisual sensors should be capable of achieving similar goals. The audiovisual information fusion strategy is a key component in designing such systems. In this paper, we exclusively survey the fusion techniques used in various audiovisual information fusion tasks. The fusion strategy used tends to depend mainly on the model, probabilistic or otherwise, used in the particular task to process sensory information to obtain higher level semantic information. The models themselves are task oriented. In this paper, we describe the fusion strategies and the corresponding models used in audiovisual tasks such as speech recognition, tracking, biometrics, affective state recognition, and meeting scene analysis. We also review the challenges and existing solutions and also unresolved or partially resolved issues in these fields. Specifically, we discuss established and upcoming work in hierarchical fusion strategies and cross-modal learning techniques, identifying these as critical areas of research in the future development of intelligent systems.

Knowledge-Based Interactive Postmining of Association Rules Using Ontologies

Abstract: In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the postprocessing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the postprocessing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.

Systems and methods for information integration through context-based entity disambiguation

Abstract: Described within are systems and methods for disambiguating entities, by generating entity profiles and extracting information from multiple documents to generate a set of entity profiles, determining equivalence within the set of entity profiles using similarity matching algorithms, and integrating the information in the correlated entity profiles. Additionally, described within are systems and methods for representing entities in a document in a Resource Description Framework and leveraging the features to determine the similarity between a plurality of entities. An entity may include a person, place, location, or other entity type.

Hierarchical Information Integration Applied to Residential Choice Behavior

Abstract: The method of hierarchical information integration can be applied to the study of complex decision-making processes in which preference formation is influenced by many attributes of choice alternatives. In this paper, we extend Louviere's (1984) original approach to include the modeling choice processes in addition to judgment processes. Assumptions underlying the original and our extension are discussed and the new method is applied in an analysis of residential choice behavior.

Information integration: its relevance to brain function and consciousness.

Abstract: A proper understanding of cognitive functions cannot be achieved without an understanding of consciousness, both at the empirical and at the theoretical level. This paper argues that consciousness has to do with a system's capacity for information integration. In this approach, every causal mechanism capable of choosing among alternatives generates information, and information is integrated to the extent that it is generated by a system above and beyond its parts. The set of integrated informational relationships generated by a complex of mechanisms--its quale--specify both the quantity and the quality of experience. As argued below, depending on the causal structure of a system, information integration can reach a maximum value at a particular spatial and temporal grain size. It is also argued that changes in information integration reflect a system's ability to match the causal structure of the world, both on the input and the output side. After a brief review suggesting that this approach is consistent with several experimental and clinical observations, the paper concludes with some prospective remarks about the relevance of understanding information integration for analyzing cognitive function, both normal and pathological.

OpenII: an open source information integration toolkit

Abstract: OpenII (openintegration.org) is a collaborative effort to create a suite of open-source tools for information integration (II). The project is leveraging the latest developments in II research to create a platform on which integration tools can be built and further research conducted. In addition to a scalable, extensible platform, OpenII includes industrial-strength components developed by MITRE, Google, UC-Irvine, and UC-Berkeley that interoperate through a common repository in order to solve II problems. Components of the toolkit have been successfully applied to several large-scale US government II challenges.

Supporting ranking queries on uncertain and incomplete data

Abstract: Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes introduces new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records' scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. In this paper, we present a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty. Under this model, we formulate several ranking query types with different semantics. We describe and analyze a set of efficient query evaluation algorithms. We show that our techniques can be used to solve the problem of rank aggregation in partial orders under two widely adopted distance metrics. In addition, we design sampling techniques based on Markov chains to compute approximate query answers. Our experimental evaluation uses both real and synthetic data. The experimental study demonstrates the efficiency and effectiveness of our techniques under various configurations.

Input information complexity, perceived time pressure, and information processing in GSS-based work groups: An experimental investigation using a decision schema to alleviate information overload conditions

Abstract: Collaboration technology enhances the ability of work groups to acquire and share large volumes of information within a short period. The processing of voluminous information is challenging and may lead to conditions of information overload. The issue of complexity of information processing in collaboration technology supported group work, and the mechanisms to overcome the information overload conditions have not received sufficient attention in the past. In this paper, we attempt to address this gap by building a theoretical model and validating it through a laboratory experiment. Based on prior research on information processing at individual level, we propose that information processing in groups that use group support systems (GSS) is shaped by input information complexity and time pressure. We examine information processing of GSS-supported groups to perform tasks involving cognitive conflict. We demonstrate that information processing has an inverted U-shaped relationship with input information complexity and a positive relationship with time pressure. The study also demonstrates that the inclusion of a decision schema that incorporates aggregate level information gleaned from the work of prior groups engaged in a similar decision situation alleviates the information overload, enabling groups to process larger and more complex information.

Biana: a software framework for compiling biological interactions and analyzing networks

Abstract: Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. Results: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. Conclusions: BIANA’s approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Quality of service in healthcare units

Abstract: Healthcare systems have to be understood in terms of a wide variety of heterogeneous, distributed and ubiquitous systems, speaking different languages, integrating medical equipment and being customised by different entities, which in turn were set by people living in different contexts and aiming at different goals. Therefore, architecture has been envisaged to support the medical applications in terms of an agency for integration, diffusion and archiving of medical information and the electronic medical record, a form of a web spider of intelligent information processing system, its major subsystems, their functional roles and the flow of information and control among them, with adjustable autonomy. With such web-based simulated systems, quality of service will be improved (e.g., the available knowledge may be used for educational and training purposes).

Conceptual Frameworks of accounting from an information perspective

Abstract: This paper analyses the benefits of accounting regulation and a conceptual framework using an information economics approach that allows consideration of uncertainty, multiple agents, demand for in...

Approaches to Uncertainty in Spatial Data

Abstract: Geographical information systems (GIS) are designed to handle large amounts of information about the natural and built environments. Any such large collection of observational information is prone to uncertainty in a number of forms. If that uncertainty is ignored there may be anything from slightly incorrect predictions or advice, to analyses that are completely logical, but fatally flawed. In either case, future trust in the work of the system or the operator can be undermined. It is therefore of crucial importance to all users of GIS that awareness of uncertainty should be as widespread as possible. Fundamental to that understanding is the nature of the uncertainty – the subject of this chapter.

Human-Centered Information Fusion: Artech House Electronic Warfare Library

Abstract: Information fusion refers to the merging of information from disparate sources with differing conceptual, contextual and typographical representations. Rather than focusing on traditional data fusion applications which have been mainly concerned with physical military targets, this unique resource explores new human-centered trends, such as locations, identity, and interactions of individuals and groups (social networks). Moreover, the book discusses two new major sources of information: human observations and web-based information. This cutting-edge volume presents a new view of multi-sensor data fusion that seeks to address these new developments, explicitly considering the active role of a human user/analyst. Professionals become knowledgeable about the key inputs into this innovative information fusion process, including traditional sensing resources (S-space), dynamic communities of human observers (H-space), and resources such as archived sensor data, blogs, and dynamic news reports from citizen reporters via the Internet (I-space).

Actor Model of Computation: Scalable Robust Information Systems

Abstract: The Actor model is a mathematical theory that treats "Actors" as the universal primitives of concurrent digital computation. The model has been used both as a framework for a theoretical understanding of concurrency, and as the theoretical basis for several practical implementations of concurrent systems. Unlike previous models of computation, the Actor model was inspired by physical laws. It was also influenced by the programming languages Lisp, Simula 67 and Smalltalk-72, as well as ideas for Petri Nets, capability-based systems and packet switching. The advent of massive concurrency through client-cloud computing and many-core computer architectures has galvanized interest in the Actor model. Actor technology will see significant application for integrating all kinds of digital information for individuals, groups, and organizations so their information usefully links together. Information integration needs to make use of the following information system principles: * Persistence. Information is collected and indexed. * Concurrency: Work proceeds interactively and concurrently, overlapping in time. * Quasi-commutativity: Information can be used regardless of whether it initiates new work or become relevant to ongoing work. * Sponsorship: Sponsors provide resources for computation, i.e., processing, storage, and communications. * Pluralism: Information is heterogeneous, overlapping and often inconsistent. * Provenance: The provenance of information is carefully tracked and recorded The Actor Model is intended to provide a foundation for inconsistency robust information integration

Biological Information as Set-Based Complexity

Abstract: The significant and meaningful fraction of all the potential information residing in the molecules and structures of living systems is unknown. Sets of random molecular sequences or identically repeated sequences, for example, would be expected to contribute little or no useful information to a cell. This issue of quantitation of information is important since the ebb and flow of biologically significant information is essential to our quantitative understanding of biological function and evolution. Motivated specifically by these problems of biological information, a class of measures is proposed to quantify the contextual nature of the information in sets of objects, based on Kolmogorov's intrinsic complexity. Such measures discount both random and redundant information and are inherent in that they do not require a defined state space to quantify the information. The maximization of this new measure, which can be formulated in terms of the universal information distance, appears to have several useful and interesting properties, some of which we illustrate with examples.

Exploring online social activities for adaptive search personalization

Abstract: The web has largely become a very social environment and will continue to become even more so. People are not only enjoying their social visibility on the Web but also increasingly participating in various social activities delivered through the Web. In this paper, we propose to explore a user's public social activities, such as blogging and social bookmarking, to personalize Internet services. We believe that public social data provides a more acceptable way to derive user interests than more private data such as search histories and desktop data. We propose a framework that learns about users' preferences from their activities on a variety of online social systems. As an example, we illustrate how to apply the user interests derived by our system to personalize search results. Furthermore, our system is adaptive; it observes users' choices on search results and automatically adjusts the weights of different social systems during the information integration process, so as to refine its interest profile for each user. We have implemented our approach and performed experiments on real-world data collected from three large-scale online social systems. Over two hundred users from worldwide who are active on the three social systems have been tested. Our experimental results demonstrate the effectiveness of our personalized search approach. Our results also show that integrating information from multiple social systems usually leads to better personalized results than relying on the information from a single social system, and our adaptive approach further improves the performance of the personalization solution.

Mining knowledge from databases: an information network analysis approach

Abstract: Most people consider a database is merely a data repository that supports data storage and retrieval. Actually, a database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, interconnected, heterogeneous information networks. Much knowledge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this tutorial, we introduce database-oriented information network analysis methods and demonstrate how information networks can be used to improve data quality and consistency, facilitate data integration, and generate interesting knowledge. This tutorial presents an organized picture on how to turn a database into one or a set of organized heterogeneous information networks, how information networks can be used for data cleaning, data consolidation, and data qualify improvement, how to discover various kinds of knowledge from information networks, how to perform OLAP in information networks, and how to transform database data into knowledge by information network analysis. Moreover, we present interesting case studies on real datasets, including DBLP and Flickr, and show how interesting and organized knowledge can be generated from database-oriented information networks.

Active knowledge: dynamically enriching RDF knowledge bases by web services

Abstract: The proliferation of knowledge-sharing communities and the advances in information extraction have enabled the construction of large knowledge bases using the RDF data model to represent entities and relationships. However, as the Web and its latently embedded facts evolve, a knowledge base can never be complete and up-to-date. On the other hand, a rapidly increasing suite of Web services provide access to timely and high-quality information, but this is encapsulated by the service interface. We propose to leverage the information that could be dynamically obtained from Web services in order to enrich RDF knowledge bases on the fly whenever the knowledge base does not suffice to answer a user query. To this end, we develop a sound framework for appropriately generating queries to encapsulated Web services and efficient algorithms for query execution and result integration. The query generator composes sequences of function calls based on the available service interfaces. As Web service calls are expensive, our method aims to reduce the number of calls in order to retrieve results with sufficient recall. Our approach is fully implemented in a complete prototype system named ANGIE1. The user can query and browse the RDF knowledge base as if it already contained all the facts from the Web services. This data, however, is gathered and integrated on the fly, transparently to the user. We demonstrate the viability and efficiency of our approach in experiments based on real-life data provided by popular Web services.

Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach

Abstract: This paper presents a Bayesian learning framework for adapting information extraction wrappers with new attribute discovery, reducing human effort in extracting precise information from unseen Web sites. Our approach aims at automatically adapting the information extraction knowledge previously learned from a source Web site to a new unseen site, at the same time, discovering previously unseen attributes. Two kinds of text-related clues from the source Web site are considered. The first kind of clue is obtained from the extraction pattern contained in the previously learned wrapper. The second kind of clue is derived from the previously extracted or collected items. A generative model for the generation of the site-independent content information and the site-dependent layout format of the text fragments related to attribute values contained in a Web page is designed to harness the uncertainty involved. Bayesian learning and expectation-maximization (EM) techniques are developed under the proposed generative model for identifying new training data for learning the new wrapper for new unseen sites. Previously unseen attributes together with their semantic labels can also be discovered via another EM-based Bayesian learning based on the generative model. We have conducted extensive experiments from more than 30 real-world Web sites in three different domains to demonstrate the effectiveness of our framework.

Integration of acoustical information in the perception of impacted sound sources: The role of information accuracy and exploitability.

Abstract: Sound sources are perceived by integrating information from multiple acoustical features. The factors influencing the integration of information are largely unknown. We measured how the perceptual weighting of different features varies with the accuracy of information and with a listener’s ability to exploit it. Participants judged the hardness of two objects whose interaction generates an impact sound: a hammer and a sounding object. In a first discrimination experiment, trained listeners focused on the most accurate information, although with greater difficulty when perceiving the hammer. We inferred a limited exploitability for the most accurate hammer-hardness information. In a second rating experiment, listeners focused on the most accurate information only when estimating sounding-object hardness. In a third rating experiment, we synthesized sounds by independently manipulating source properties that covaried in Experiments 1 and 2: sounding-object hardness and impact properties. Sounding-object hardness perception relied on the most accurate acoustical information, whereas impact-properties influenced more strongly hammer hardness perception. Overall, perceptual weight increased with the accuracy of acoustical information, although information that was not easily exploited was perceptually secondary, even if accurate.

Information Integration Theory Applied to Student Apartment Selection Decisions

Abstract: The purpose of this paper is to apply theory and methodology developed in the study of information integration in psychology to the modeling of individual apartment decisions. In particular, two models that express the trade-off relationships among three variables hypothesized to be influential in apartment selection are examined. Although it is not claimed that the variables included in the study are the only ones relevant to apartment selection, evidence is provided that they are considered to be the three most important in a preliminary analysis. The paper also discusses potential benefits of controlled experimentation and model estimation strategies.

Overcoming Information Overload in the Enterprise: The Active Approach

Abstract: Knowledge workers are central to an organization's success, yet their information management tools often hamper their productivity. This has major implications for businesses across the globe because their commercial advantage relies on the optimal exploitation of their own enterprise information, the huge volumes of online information, and the productivity of the required knowledge work. The Active project addresses this challenge through an integrated knowledge management workspace that reduces information overload by significantly improving the mechanisms for creating, managing, and using information. The project's approach follows three themes: sharing information through tagging, wikis, and ontologies; prioritizing information delivery by understanding users' current-task context; and leveraging informal processes that are learned from user behavior.

Automatic Face Annotation in Personal Photo Collections Using Context-Based Unsupervised Clustering and Face Information Fusion

Abstract: In this paper, a novel face annotation framework is proposed that systematically leverages context information such as situation awareness information with current face recognition (FR) solutions. In particular, unsupervised situation and subject clustering techniques have been developed that are aided by context information. Situation clustering groups together photos that are similar in terms of capture time and visual content, allowing for the reliable use of visual context information during subject clustering. The aim of subject clustering is to merge multiple face images that belong to the same individual. To take advantage of the availability of multiple face images for a particular individual, we propose effective FR methods that are based on face information fusion strategies. The performance of the proposed annotation method has been evaluated using a variety of photo sets. The photo sets were constructed using 1385 photos from the MPEG-7 Visual Core Experiment 3 (VCE-3) data set and approximately 20000 photos collected from well-known photo-sharing websites. The reported experimental results show that the proposed face annotation method significantly outperforms traditional face annotation solutions at no additional computational cost, with accuracy gains of up to 25% for particular cases.

Information interaction in molecular medicine: integrated use of multiple channels

Abstract: Task-based information access is a significant context for studying information interaction and for developing information retrieval (IR) systems Molecular medicine (MM) is an information-intensive and rapidly growing task domain, which aims at providing new approaches to the diagnosis, prevention and treatment of various diseases The development of bioinformatics databases and tools has led to an extremely distributed information environment There are numerous generic and domain-specific tools and databases available for online information access This renders MM as a fruitful context for research in task-based IR The present paper examines empirically task-based information access in MM and analyzes task processes as contexts of information access and interaction, integrated use of resources in information access and the limitations of (simple server-side) log analysis in understanding information access, retrieval sessions in particular We shed light on the complexity of the between-systems interaction The findings suggest that the system development should not be done in isolation as there is considerable interaction between them in real world use We also classify system-level strategies of information access integration that can be used to reduce the amount of manual system integration by task performers