Information integration platform for CIMS

A new information integration platform for computer integrated manufacturing system (CIMS) is presented, which is based on agent and CORBA. CORBA enhances the system integration because it is an industry-standard for interoperable, distributed objects across heterogeneous hardware and software platform. Agent technology is used to improve intelligence of the integration system. In order to implement the information integration platform, we use a network integration server to integrate the network, design a generic database agent to integrate database, adopt multi-agent based architecture to integrate application, and utilize wrapper as a CORBA object to integrate legacy code.published_or_final_versio

uC: Ubiquitous Collaboration Platform for Multimodal Team Interaction Support

A human-centered computing platform that improves teamwork and transforms the “human- computer interaction experience” for distributed teams is presented. This Ubiquitous Collaboration, or uC (“you see”), platform\u27s objective is to transform distributed teamwork (i.e., work occurring when teams of workers and learners are geographically dispersed and often interacting at different times). It achieves this goal through a multimodal team interaction interface realized through a reconfigurable open architecture. The approach taken is to integrate: (1) an intuitive speech- and video-centric multi-modal interface to augment more conventional methods (e.g., mouse, stylus and touch), (2) an open and reconfigurable architecture supporting information gathering, and (3) a machine intelligent approach to analysis and management of heterogeneous live and stored sensor data to support collaboration. The system will transform how teams of people interact with computers by drawing on both the virtual and physical environment

A Distributed Hierarchical Structure for Object Networks Supporting Activity Recognition

Pervasive environments will witness heterogeneous smart embedded devices (e.g. sensors, actuators) integrated into user’s living environment (e.g. smart homes and hospitals) and provide a multitude of information that can transparently support user’s lifestyle. One promising application resulting from the management and exploitation of this information is the human activity recognition. In this paper we briefly describe our activity recognition architecture and focus on an important management component of this architecture using the concept of object networks. We explore how object networks can integrate various sensor networks and heterogeneous devices into a coherent network through embedded context and role profile and at the same time support distributed context reasoning. The paper also describes the mechanisms used to eliminate and refine context information that is deemed irrelevant due to user behaviour changes over time, by employing the idea of role fitness

Personalization on E-Content Retrieval Based on Semantic Web Services

In the current educational context there has been a significant increase in learning object repositories (LOR), which are found in large databases available on the hidden web. All these information is described in any metadata labeling standard (LOM, Dublin Core, etc). It is necessary to work and develop solutions that provide efficiency in searching for heterogeneous content and finding distributed context. Distributed information retrieval, or federated search, attempts to respond to the problem of information retrieval in the hidden Web. Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This study presents a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents using a Service Oriented Architecture. The model allows the development of an open and flexible architecture that supports the services necessary to dynamically search for distributed digital content. A major challenge in searching and retrieving digital content is also to efficiently find the most suitable content for the users. This model proposes a new approach to filtering the educational content retrieved based on Case-Based Reasoning (CBR). It is based on the model AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments), a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. The model and the technologies presented in this research exemplify the potential for developing personalized recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture, as outlined in this article, are its flexibility, customization, integrative solution and efficiency

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three di�erent technologies

SOA-Based Model for Value-Added ITS Services Delivery

Integration is currently a key factor in intelligent transportation systems (ITS), especially because of the ever increasing service demands originating from the ITS industry and ITS users. The current ITS landscape is made up of multiple technologies that are tightly coupled, and its interoperability is extremely low, which limits ITS services generation. Given this fact, novel information technologies (IT) based on the service-oriented architecture (SOA) paradigm have begun to introduce new ways to address this problem. The SOA paradigm allows the construction of loosely coupled distributed systems that can help to integrate the heterogeneous systems that are part of ITS. In this paper, we focus on developing an SOA-based model for integrating information technologies (IT) into ITS to achieve ITS service delivery. To develop our model, the ITS technologies and services involved were identified, catalogued, and decoupled. In doing so, we applied our SOA-based model to integrate all of the ITS technologies and services, ranging from the lowest-level technical components, such as roadside unit as a service (RS S), to the most abstract ITS services that will be offered to ITS users (value-added services). To validate our model, a functionality case study that included all of the components of our model was designed

Cyber Physical System Based Proactive Collaborative Maintenance

The aim of the MANTIS project is to provide a proactive maintenance service platform architecture based on Cyber Physical Systems. The platform will allow estimating future performance, predicting and preventing imminent failures and scheduling proactive maintenance. Maintenance is an important element that creates added value in the business processes and it also creates new business models with a stronger service orientation. Physical systems and the environment they work in are continuously monitored by a range of intelligent sensors, resulting in massive amounts of data, which characterise the usage history, working condition, location, movement and other physical properties of the systems. These systems are part of a larger network of heterogeneous and collaborative systems (e.g. vehicle fleets) connected via robust communication mechanisms able to operate in challenging environments. MANTIS consists of distributed processing chains that efficiently transform raw data into knowledge, while minimising the need for bandwidth. Sophisticated distributed sensing and decision-making functions are performed at different levels collaboratively, ranging from local nodes to locally optimise performance, bandwidth and maintenance; to cloud-based platforms that integrate information from diverse systems and execute distributed processing and analytics algorithms for global decision-making

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three different technologies.Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-RMinisterio de Ciencia y Tecnología TIN2013-40848-RMinisterio de Ciencia y Tecnología TIN2016-75394-