Autonomous Information Sources Research Articles

Tourist Mental Health Drives Destination Choice, Marketing, and Matching

Leisure tourism, including destination choice, can be viewed as an investment in mental health maintenance. Destination marketing measures can thus be analyzed as mental health investment prospectuses, aiming to match tourist desires. A mental health framework is particularly relevant for parks and nature tourism destinations, since the benefits of nature for mental health are strongly established. We test it for one globally iconic destination, using a large-scale qualitative approach, both before and during the COVID-19 pandemic. Tourists’ perceptions and choices contain strong mental health and well-being components, derived largely from autonomous information sources, and differing depending on origins. Parks agencies emphasize factual cognitive aspects, but tourism enterprises and destination marketing organizations use affective approaches appealing to tourists’ mental health.

An Ontology-Based Approach for Marine Geochemical Data Interoperation

International deep seabed resource surveys have accumulated a large amount of valuable marine geochemical data. These data, however, derive from a number of autonomous heterogeneous information sources that show the characteristics of big data, i.e., multidisciplinary, multidimensional, multisemantic, and with strong correlations. The traditional database federation method is thus not applicable. To achieve marine geochemical data interoperation, an approach is proposed in this paper. The approach first builds a marine sample ontology (MSO) based on marine geochemical metadata standards. With support of the MSO ontology, which serves as a specification for the semantics and model of multisource heterogeneous data, the data integration and unified query APIs are accomplished across different databases. The experiment is applied with three databases: the ICP-AES database model in the ODP project, the PetDB marine petrology database model compiled at the Lamont–Doherty Earth Observatory, and the GEOROC database model operated by the German Max Planck Institute. The experimental results show that this method not only improves the efficiency of the marine geochemical data integration but also realizes the reuse of data and models under the premise of ensuring the independence, security, and timeliness of the data sources.

Requirements Engineering for a User Centric Spatial Data Warehouse

Spatial data warehouses (SDW) are used for executing analytical multidimensional queries along with spatial analysis. With recent boom in technology, access and integration of multiple, distributed, heterogeneous and autonomous information sources storing spatial or non-spatial data has become the need. For this purpose, we have proposed a simple star schema to store spatial and non-spatial information in data warehouse. We have also given a framework of activities for collecting data from information sources and organizing it in a geo-spatial data store for querying data and visualizing results. The paper has also discussed the most basic requirements for handling spatial data that will help to develop a user-centric spatial data warehouse.

Integration of Heterogeneous Data Sources

Access to distributed, heterogeneous and autonomous information sources, becomes possible with the Internet. These information sources are distinguished by the nature of information, namely, the ontological domain to which they belong but also by the type of media they are issues, such as image, text, video, etc. With the advent of semantic web, new opportunities in multi-sources integration are emerging and many approaches are revisited with taking into account the new requirements. Also, there is the use or reuse of datawarehouses, mediators and peer-to-peer systems. Our project aim to propose a distributed and open system for indexing and searching multimedia content (DIOSYS) and especially an integration system based on the Peer-to-peer paradigm. In this study, we propose a state of the art of the integration problem by examining the most representative approaches of the three currents and we will try to summarize this study with use of tables after having presented and justified a set of criteria.

A tool for data cube construction from structurally heterogeneous XML documents

AbstractData cubes for OLAP (On‐Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path‐oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real‐world data integration tasks. Several Lowest Common Ancestor (LCA)‐based XML query evaluation strategies have recently been introduced to provide a more structure‐independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high‐level data extraction primitive that utilizes the purpose‐built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

A FLEXIBLE MEDIATION PROCESS FOR LARGE DISTRIBUTED INFORMATION SYSTEMS

We consider distributed information systems that are open, dynamic and provide access to large numbers of distributed, heterogeneous, autonomous information sources. Most of the work in data mediator systems has dealt with the problem of finding relevant information providers for a request. However, finding relevant requests for information providers is another important side of the mediation problem which has not received much attention. In this paper, we address these two sides of the problem with a flexible mediation process. Once the qualified information providers are identified, our process allows them to express their interest in a request via a bidding mechanism. It also requires to set up a requisition policy, because a request must always be answered if there are qualified providers. This work does not concern pure market mechanisms because we counter-balance the providers' bids by considering their quality wrt a request. We validate our process on a set of simulations in the context of load balancing, which is a good indicator of the system's overall performance. The results show that the mediation process provides a very good long-run regulation of the system, in particular when providers can leave the system. However, load balancing is not the natural application of the flexible mediation and additional testing is required to show the generality of the approach to non-depletable resources.

Image formation, information sources and an iconic Australian tourist destination

Tourist destination brand image is a major influencing factor in traveller destination choice. This exploratory research into the information sources from which destination brand image evolves provides insights which have the potential to improve tourism destination brand development strategies. In-depth interviews with 21 international backpackers on Australia’s northern New South Wales coast indicate that mainstream media play little or no part in the formation of respondents’ image of the coastal destination of Byron Bay. Rather, word of mouth and autonomous independent information sources were the key media through which respondents formed their image of this iconic Australian tourist destination. Findings also show the importance of destination experience in the image formation process and suggest that destination marketing organizations (DMOs) can and should go beyond traditional mainstream media to communicate their brand message. Additionally, findings suggest the need for DMOs to provide leadership within the destination’s tourism industry to improve service delivery and exceed the ‘brand promise’.

Fusionplex: resolution of data inconsistencies in the integration of heterogeneous information sources

Fusionplex is a system for integrating multiple heterogeneous and autonomous information sources that uses data fusion to resolve factual inconsistencies among the individual sources. To accomplish this, the system relies on source features, which are meta-data on the merits of each information source; for example, the recentness of the data, its accuracy, its availability, or its cost. The fusion process is controlled with several parameters: (1) with a vector of feature weights, each user defines an individual notion of data utility; (2) with thresholds of acceptance, users ensure minimal performance of their data, excluding from the fusion process data that are too old, too costly, or lacking in authority, or numeric data that are too high, too low, or obvious outliers; and, ultimately, (3) in naming a particular fusion function to be used for each attribute (for example, average, maximum, or simply any) users implement their own interpretation of fusion. Several simple extensions to SQL are all that is needed to allow users to state these resolution parameters, thus ensuring that the system is easy to use. Altogether, Fusionplex provides its users with powerful and flexible, yet simple, control over the fusion process. In addition, Fusionplex supports other critical integration requirements, such as information source heterogeneity, dynamic evolution of the information environment, quick ad-hoc integration, and intermittent source availability. The methods described in this paper were implemented in a prototype system that provides complete Web-based integration services for remote clients.

Context Interchange Mediation for Semantic Interoperability and Dynamic Integration of Autonomous Information Sources in the Fixed Income Securities Industry

We examine semantic interoperability problems in the fixed income securities industry and propose a knowledge representation architecture for context interchange mediation to support dynamic integration of autonomous database, web, and procedural sources of information. For sources and receivers sharing a common subject domain, the mediator's reasoning engine can devise query plans integrating multiple sources and resolving semantic heterogeneity. Receiver applications can obtain the data they need in the form they need it without imposing changes on sources. The architecture includes: 1) data models for each source and receiver, 2) subject ontologies, containing abstract subject matter conceptualizations that would be known to experienced practitioners in the industry, and 3) context models for each source and receiver that explain how each data model implements the abstract concepts from a subject ontology.

STIRDAT: A system tightly integrating relational databases and a theorem prover

There has been a growing awareness of the need to share data and services among autonomous heterogeneous information sources. For obvious reasons, these sources are storing information in ways that meet their own needs and hence in different formats. However, for a combination of information from heterogeneous sources to be truly useful, a user needs to be able to use the shared information without having to learn the particular data format or the locations of the relevant data. This idea can be taken a step further as the importance of combining techniques from various areas in computer science becomes more obvious. There are a number of benefits that can be derived from combining, for example, some of the features of Artificial Intelligence systems, such as logical inference, with those of traditional database systems. This paper describes STIRDAT, a system tightly and transparently integrating data in a number of relational databases stored at different sites with a theorem prover. It provides a clear illustration of the benefits to be gained from a system combining techniques that have proven independently useful in different areas of Computer Science.

Ontologies are not the Panacea in Data Integration: A FlexibleCoordinator to Mediate Context Construction

Shared ontologies describe concepts and relationships to resolve semantic conflicts amongst users accessing multiple autonomous and heterogeneous information sources. We contend that while ontologies are useful in semantic reconciliation, they do not guarantee correct classification of semantic conflicts, nor do they provide the capability to handle evolving semantics or a mechanism to support a dynamic reconciliation process. Their limitations are illustrated through a conceptual analysis of several prominent examples used in heterogeneous database systems and in natural language processing. We view semantic reconciliation as a nonmonotonic query-dependent process that requires flexible interpretation of query context, and as a mechanism to coordinate knowledge elicitation while constructing the query context. We propose a system that is based on these characteristics, namely the SCOPES (Semantic Coordinator Over Parallel Exploration Spaces) system. SCOPES takes advantage of ontologies to constrain exploration of a remote database during the incremental discovery and refinement of the context within which a query can be answered. It uses an Assumption-based Truth Maintenance System (ATMS) to manage the multiple plausible contexts which coexist while the semantic reconciliation process is unfolding, and the Dempster-Shafer (DS) theory of belief to model the likelihood of these plausible contexts.