A survey of semantic web technology for agriculture

Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Despite this lack of application,agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization(FAO).This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems,by making available a self contained reference that provides:a comprehensive review of preexisting semantic resources and their construction methods,data interchange standards,as well as a survey of the current applications of semantic web technologies.

Research of Web-based Workflow Management System

The state of the art of workflow management techniques in research is introduced. The research and development trends of Workflow Management System (WFMS) are presented. On basis of analysis and comparison of kinds of WFMSs, a WFMS based on Web technology and distributed object management is proposed. Finally, the application of the WFMS in supply chain management is described in detail.

Internet Application Technologies in Web 2.0 Era

The Internet has stepped into Web 2.0 era. Web 2.0 application technologies and services are rapidly developing, accompanied by the innovation and revolution of business models. This article analyzes the development of Web 2.0 technologies and their promotion role in the development of Internet services, discusses the implementation of Web 2.0 core concepts (including user participation, resource sharing and platform) by the multiple Internet application technologies, and gives the development trends of Internet application technologies.

Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.