Alessio Bechini

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.

Design of a toolset for dynamic analysis of concurrent Java programs

The Java language supports the use of monitors, sockets, and remote method invocation for concurrent programming. Also, Java classes can be defined to simulate other types of concurrent constructs. However, concurrent Java programs, like other concurrent programs, are difficult to specify, design, code, test and debug. In this paper, we describe the design of a toolset, called JaDA (Java Dynamic Analyzer), that provides testing and debugging tools for concurrent Java programs. To collect run-time information or control program execution, JaDA requires transformation of a concurrent Java program into a slightly different Java program. We show that by modifying Java classes that support concurrent programming, Java application programs only need minor modifications. We also present a novel approach to managing threads that are needed for testing and debugging of concurrent Java programs.

A MapReduce solution for associative classification of big data

Associative classifiers have proven to be very effective in classification problems. Unfortunately, the algorithms used for learning these classifiers are not able to adequately manage big data because of time complexity and memory constraints. To overcome such drawbacks, we propose a distributed association rule-based classification scheme shaped according to the MapReduce programming model. The scheme mines classification association rules (CARs) using a properly enhanced, distributed version of the well-known FP-Growth algorithm. Once CARs have been mined, the proposed scheme performs a distributed rule pruning. The set of survived CARs is used to classify unlabeled patterns. The memory usage and time complexity for each phase of the learning process are discussed, and the scheme is evaluated on seven real-world big datasets on the Hadoop framework, characterizing its scalability and achievable speedup on small computer clusters. The proposed solution for associative classifiers turns to be suitable to practically address big datasets even with modest hardware support. Comparisons with two state-of-the-art distributed learning algorithms are also discussed in terms of accuracy, model complexity, and computation time.

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.

Use of a CORBA/RMI gateway: characterization of communication overhead

Many distributed applications make use of distributed object technology. In this kind of systems, modules providing services are implemented as objects spread over a network. Distributed objects are usually accessed through communication frameworks based on specific middleware solutions, such as CORBA, DCOM, and RMI. Applications of this kind might be built up (or extended) integrating different modules, possibly already coded and available on the market. Each required and available module might use a specific communication framework, hampering its prompt integration into a system exploiting a different framework. A convenient way to tackle this problem is the insertion of a gateway module, passing service requests between two different middleware solutions. This approach allows a quick integration of service modules, but it could lead to performance problems, due to the introduced communication overhead. In this paper, we report our experience in developing a simple CORBA/RMI gateway module, and we discuss how it affects the application performance. Measures of the communication overhead show that the employment of the gateway is a viable solution in many real-world applications, and gives hints for efficiently placing modules on the available hosts.

A tool for testing of parallel and distributed programs in message-passing environments

Due to the non-deterministic behavior of some parallel and distributed programs, addressing the problem of testing in such context is a non-trivial task. A proficient testing phase must be done using tools which record information about a single execution, and which are able to force a concurrent program to exercise a given execution. This paper describes a tool for testing of programs based on the Horus system. Our approach is novel in dealing with events connected to process group handling and group communication. Moreover, we show that using the capabilities of the Horus run-time system can be really helpful in solving testing problems. Our approach has the advantages of requiring no modifications neither in the program nor in the operating system, and to avoid a centralized solution through the use of partial orders of synchronization events.

Cross-lattice behavior of general ACO folding for proteins in the HP model

The computational investigation of protein folding is one of the most relevant challenges in bioinformatics. In this field, simplified lattice models for proteins like the classical HP model have been proposed, and different lattice types can be employed. A promising approach to find ground state conformations relies on Ant Colony Optimization (ACO), a popular biology-inspired heuristics: several variants have been implemented so far, on square lattices in 2D and 3D. In this paper we propose a general scheme of ACO for HP on both square and triangular lattices in 2D and 3D, including also a novel initialization procedure for the pheromone matrix according to some pre-computed suboptimal conformations. The algorithm behavior, considering the influence of the optional parts and the required parameter tuning, is investigated for the first time with experiments that systematically span different lattice types. The test outcomes are useful in understanding how to operate on the algorithm parameters. The presented results are used to sketch out general guidelines for the practical employment of ACO in conformational studies, depending on the chosen sequences and lattice types.

Enabling ontology-based document classification and management in ebXML registries

Document Management Systems (DMSs) are a key component in modern enterprises. For successful document search and retrieval, an adequate metadata set should be defined in order to describe documents with sufficient detail. However, often a single metadata set is not sufficient throughout the whole DMS, as different document types require different attributes to be properly characterized. In this paper, we introduce ontologies as a modeling technology for structured metadata definition within DMSs. Focusing on the ebXML registry standard, we show an approach to enhance DMSs for semantic content management and then we propose a method to exploit this new capability for automated document characterization.

Low-Effort Support to Efficient Urban Parking in a Smart City Perspective

The Internet of Things (IoT) is today considered as one of the most important enabling technologies for developing a wide variety of smart services aimed at assisting the final user in the urban environment. In this chapter, we present how IoT can be employed to develop a system for the effective and efficient management of urban parking, thus providing a small, yet relevant contribution to the implementation of a real Smart City. Our system relies on the identification of each single parking slot but, unlike other approaches proposed in the last years, it does not require dedicated sensors and/or infrastructure, thus it can be regarded as a low-cost and low-effort solution. Indeed, it collects parking data from a mobile application on the drivers’ mobile devices and possibly identifies each slot by QR codes deployed on the single parking spots. The amount of data collected by the system on parking occupancy allows inferring valuable information that can be used by local governments. For instance, it will be possible to define appropriate pricing schemes so as to promote parking areas not particularly occupied. The employment of an SOA design guarantees the integration of the developed system with other existing services within a Smart City.

On the characterization and software implementation of general protein lattice models.

Abstract models of proteins have been widely used as a practical means to computationally investigate general properties of the system. In lattice models any sterically feasible conformation is represented as a self-avoiding walk on a lattice, and residue types are limited in number. So far, only two- or three-dimensional lattices have been used. The inspection of the neighborhood of alpha carbons in the core of real proteins reveals that also lattices with higher coordination numbers, possibly in higher dimensional spaces, can be adopted. In this paper, a new general parametric lattice model for simplified protein conformations is proposed and investigated. It is shown how the supporting software can be consistently designed to let algorithms that operate on protein structures be implemented in a lattice-agnostic way. The necessary theoretical foundations are developed and organically presented, pinpointing the role of the concept of main directions in lattice-agnostic model handling. Subsequently, the model features across dimensions and lattice types are explored in tests performed on benchmark protein sequences, using a Python implementation. Simulations give insights on the use of square and triangular lattices in a range of dimensions. The trend of potential minimum for sequences of different lengths, varying the lattice dimension, is uncovered. Moreover, an extensive quantitative characterization of the usage of the so-called “move types” is reported for the first time. The proposed general framework for the development of lattice models is simple yet complete, and an object-oriented architecture can be proficiently employed for the supporting software, by designing ad-hoc classes. The proposed framework represents a new general viewpoint that potentially subsumes a number of solutions previously studied. The adoption of the described model pushes to look at protein structure issues from a more general and essential perspective, making computational investigations over simplified models more straightforward as well.