Mario G. C. A. Cimino

Patterns and technologies for enabling supply chain traceability through collaborative e-business

Industrial traceability systems are designed to operate over complex supply chains, with a large and dynamic group of participants. These systems need to agree on processing and marketing of goods, information management, responsibility, and identification. In addition, they should guarantee context independence, scalability, and interoperability. In this paper, we first discuss the main issues emerging at different abstraction levels in developing traceability systems. Second, we introduce a data model for traceability and a set of suitable patterns to encode generic traceability semantics. Then, we discuss suitable technological standards to define, register, and enable business collaborations. Finally, we show a practical implementation of a traceability system through a real world experience on food supply chains.

Autonomic tracing of production processes with mobile and agent-based computing

Tracing items in a supply chain, across different enterprises and through the full processes scope, is today an inherently complex design task. Enterprises are typically comprised of hundreds of applications that are custom built at different times, acquired from third parties and parts of legacy systems, and also operating in multiple tiers of different manufacturing and information system platforms. Further, traceability is characterized by a goal-oriented approach, in which business-process analyses are driven by goal achievements rather than by systematic engineering processes. The use of a classical enterprise integration approach mostly needs tailoring to different applications. Due to the number and diversity of the systems and of their interactions, and to their dynamicity, it is difficult, costly, and therefore often not convenient to develop in large scale distributed systems. To overcome these issues, a supply chain traceability system with a high level of automation is discussed in this paper. In particular, the system adopts an agent-based approach, in which cooperative software agents find solutions to back-end tracing problems by self-organization. Such cooperative agents are based on a business process aware traceability model, and on a service-oriented composition paradigm. Furthermore, an interface agent assists each user to carry out the front-end tracking activities. Interface agents rely on the context-awareness paradigm to gain self-configurability and self-adaptation of the user interface, and on ubiquitous computing technology, i.e., mobile devices and radio-frequency identification, to perform agile and automatic lot identification. The paper comprises real-world experiences on the fashion supply chain.

An adaptive rule-based approach for managing situation-awareness

Situation awareness is a powerful paradigm that can efficiently exploit the increasing capabilities of handheld devices, such as smart phones and PDAs. Indeed, accurate understanding of the current situation can allow the device to proactively provide information and propose services to users in mobility. Of course, to recognize the situation is a challenging task, due to such factors as the variety of possible situations, uncertain and imprecise data, and different users preferences and behavior. In this framework, we propose a robust and general rule-based approach to manage situation awareness. We adopt Semantic Web reasoning, fuzzy logic modeling, and genetic algorithms to handle, respectively, situational/contextual inference, uncertain input processing, and adaptation to the users behavior. We exploit an agent-oriented architecture so as to provide both functional and structural interoperability in an open environment. The system is evaluated by means of a real-world case study concerning resource recommendation. Experimental results show the effectiveness of the proposed approach.

Monitoring elderly behavior via indoor position-based stigmergy

In this paper we present a novel approach for monitoring elderly people living alone and independently in their own homes. The proposed system is able to detect behavioral deviations of the routine indoor activities on the basis of a generic indoor localization system and a swarm intelligence method. For this reason, an in-depth study on the error modeling of state-of-the-art indoor localization systems is presented in order to test the proposed system under different conditions in terms of localization error. More specifically, spatiotemporal tracks provided by the indoor localization system are augmented, via marker-based stigmergy, in order to enable their self-organization. This allows a marking structure appearing and staying spontaneously at runtime, when some local dynamism occurs. At a second level of processing, similarity evaluation is performed between stigmergic marks over different time periods in order to assess deviations. The purpose of this approach is to overcome an explicit modeling of users activities and behaviors that is very inefficient to be managed, as it works only if the user does not stray too far from the conditions under which these explicit representations were formulated. The effectiveness of the proposed system has been experimented on real-world scenarios. The paper includes the problem statement and its characterization in the literature, as well as the proposed solving approach and experimental settings.

Combining fuzzy logic and semantic web to enable situation-awareness in service recommendation

Mobile Internet is rapidly growing and an enormous quantity of resources are currently available. Thus, the common mechanisms used up to now to locate resources, such as browsing and searching, do not look anymore to be effective in helping users in mobility. Indeed, the users personal information space can be very large, with respect to the limited interaction capabilities of mobile devices. This paper proposes a situation-aware framework for providing personalized resources in a proactive manner. Current situations of the user are inferred by exploiting domain knowledge expressed in terms of ontologies and semantic rules, which are represented in the well-known Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL), respectively. Uncertainty in some contextual rule conditions is handled by defining appropriate linguistic variables through the Fuzzy Control Language (FCL), a standard representation of fuzzy systems for data exchange among different implementations, and adopting a purposely-adapted coding of ontologies and rules. Uncertain conditions bring to infer more than one situation with different certainty degrees: these degrees are used to assign a rank to concurrent situations. Finally, situations are connected to a set of related resources to be recommended to the user.

A General Framework for Food Traceability

In this paper, we propose a generic data model for food traceability. We discuss the characteristics of our model with respect both to tracing and tracking requirements, and to quality control. Since traceability is based on reliable and faithful exchange of documents among the units of the supply chain, we also consider how electronic business-to-business standards, such as ebXML, can help support data homogeneity as well as system interoperability. Finally, we highlight how the implementation effort can be accordingly reduced.

Situation-Aware Mobile Service Recommendation with Fuzzy Logic and Semantic Web

Todays mobile Internet service portals offer thousands of services and mobile devices can host plenty of applications, documents and web URLs. Hence, for average mobile users there is an increasing cognitive burden in finding the most appropriate service among the many available. On the other hand, methodologies such as bookmarks and resource tagging require a great arranging effort to handle increasing resources. To help mobile users in managing and using this personal information space, new levels of granularity should be introduced in the organization of services, together with some degree of self-awareness. This paper proposes a situation-aware service recommender that helps locating services proactively. In the recommender, a semantic layer determines one or more user current situations by using domain knowledge expressed in terms of ontology and semantic rules. A fuzzy inference layer manages the vagueness of some contextual condition of these rules and outputs an uncertainty degree for each situation. Based on this degree, the recommender proposes a set of specific resources.

Cerere: an information system supporting traceability in the food supply chain

In this paper, we present a system for traceability in the food supply chain. The system is able to systematically store information about products and processes operating on products throughout the entire supply chain from farm suppliers to retailers. The system manages quality information too. Thanks to the use of the electronic business using extensible markup language (ebXML) standard, the traceability system also provides data homogeneity, scalability and interoperability.

Genetic interval neural networks for granular data regression

Granular data and granular models offer an interesting tool for representing data in problems involving uncertainty, inaccuracy, variability and subjectivity have to be taken into account. In this paper, we deal with a particular type of information granules, namely interval-valued data. We propose a multilayer perceptron (MLP) to model interval-valued input-output mappings. The proposed MLP comes with interval-valued weights and biases, and is trained using a genetic algorithm designed to fit data with different levels of granularity. In the evolutionary optimization, two implementations of the objective function, based on a numeric-valued and an interval-valued network error, respectively, are discussed and compared. The modeling capabilities of the proposed MLP are illustrated by means of its application to both synthetic and real world datasets.

A framework for detecting unfolding emergencies using humans as sensors

Abstract The advent of online social networks (OSNs) paired with the ubiquitous proliferation of smartphones have enabled social sensing systems. In the last few years, the aptitude of humans to spontaneously collect and timely share context information has been exploited for emergency detection and crisis management. Apart from event-specific features, these systems share technical approaches and architectural solutions to address the issues with capturing, filtering and extracting meaningful information from data posted to OSNs by networks of human sensors. This paper proposes a conceptual and architectural framework for the design of emergency detection systems based on the “human as a sensor” (HaaS) paradigm. An ontology for the HaaS paradigm in the context of emergency detection is defined. Then, a modular architecture, independent of a specific emergency type, is designed. The proposed architecture is demonstrated by an implemented application for detecting earthquakes via Twitter. Validation and experimental results based on messages posted during earthquakes occurred in Italy are reported.