Gabriele Gianini

SOAP Processing Performance and Enhancement

The web services (WS) technology provides a comprehensive solution for representing, discovering, and invoking services in a wide variety of environments, including Service Oriented Architectures (SOA ) and grid computing systems. At the core of WS technology lie a number of XML-based standards, such as the Simple Object Access Protocol (SOAP), that have successfully ensured WS extensibility, transparency, and interoperability. Nonetheless, there is an increasing demand to enhance WS performance, which is severely impaired by XMLs verbosity. SOAP communications produce considerable network traffic, making them unfit for distributed, loosely coupled, and heterogeneous computing environments such as the open Internet. Also, they introduce higher latency and processing delays than other technologies, like Java RMI and CORBA. WS research has recently focused on SOAP performance enhancement. Many approaches build on the observation that SOAP message exchange usually involves highly similar messages (those created by the same implementation usually have the same structure, and those sent from a server to multiple clients tend to show similarities in structure and content). Similarity evaluation and differential encoding have thus emerged as SOAP performance enhancement techniques. The main idea is to identify the common parts of SOAP messages, to be processed only once, avoiding a large amount of overhead. Other approaches investigate nontraditional processor architectures, including micro- and macrolevel parallel processing solutions, so as to further increase the processing rates of SOAP/XML software toolkits. This survey paper provides a concise, yet comprehensive review of the research efforts aimed at SOAP performance enhancement. A unified view of the problem is provided, covering almost every phase of SOAP processing, ranging over message parsing, serialization, deserialization, compression, multicasting, security evaluation, and data/instruction-level processing.

Landscape-aware location-privacy protection in location-based services

Mobile network providers have developed a variety of location-based services (LBSs), such as friend-finder, point of interest services, emergency rescue and many other safety and security services. The protection of location-privacy has consequently become a key aspect to the success of LBSs, since users consider their own physical location and movements highly privacy-sensitive, and demand for solutions able to protect such an information in a variety of environments. The idea behind location-privacy protection is that the individual should be able to set the level at which the location information is released to avoid undesired exploitation by a potential attacker: one of the approaches to this problem is given by the application of spatial obfuscation techniques, actuated by a trusted agent, and consisting in artificial perturbations of the location information collected by sensing technologies, before its disclosure to third parties. In many situations, however, landscape/map information can help a third party to perform Bayesian inference over spatially obfuscated data and to refine the users location estimate up to a violation of the original users location-privacy requirements. The goal of this paper is to provide a map-dependent obfuscation procedure that enables the release of the maximum possible users location information, that does not lead to a violation of the original users location-privacy requirements, even when refined through map-based inference.

A Retinex model based on Absorbing Markov Chains

The Retinex algorithm, developed by Land and McCann, provides an abstract model of the mechanism of color sensation in the Human Vision System. At the basis of model lies the fact that the color appearance of a point does not depend only on its color value, but rather on the comparison among itself and other pixels. According to the model, separately for each chromatic channel, an image pixel receives suitably filtered information about the brightness of other image regions, based on which its own brightness is eventually re-scaled. The original formulation (Land and McCann, 1971) uses a path-based sampling approach: the information is transported by memoryless random walks, starting from randomly chosen points; along the path the information is filtered - based on the brightness of the travelled regions - by a specific path function, computed through chains of ratios of pixel intensities. Such a function is path-dependent and retains the value of the brightest point found along the path. The overall correction to a pixel depends on the specific realizations of two sampling processes: the starting-point sampling process and the path-sampling process. As a consequence of the sampling, this algorithm is known to be intrinsically noisy. This draw-back can be overcome by passing from the path-sampling algorithm to the probabilistic representation of the corresponding diffusion process. In this paper we start from the random path simulative model of Retinex, we respell the standard path-based sampling process representation of the Retinex model, as formalized in Provenzi et?al. (2005), and we show that - despite the overall path-dependence - the model can be given a representation in terms of Absorbing Markov Chains, by means of the embedding into a suitable state-space. We derive the corresponding analytic model, accounting for the combined effects of path-function, path sampling process and starting-point sampling process. Finally we provide a numerical algorithm for working out its solution. Using such a model, the output brightness of a pixel can be computed based on the solution of a simple sparse linear system. We show that the output of the random walk sampling algorithm and the Markov Chain based algorithm agree to an extent that can be controlled by few model parameters. We have found also that the Markov Chain based algorithm is more efficient than the basic random path sampling in obtaining noise free images. Those analytic probabilistic models and simulative models can be used as complementary tools for studying the Retinex mechanism and for identifying and comparing variants.

QBRIX: a quantile-based approach to retinex.

In this paper, we introduce a novel probabilistic version of retinex. It is based on a probabilistic formalization of the random spray retinex sampling and contributes to the investigation of the spatial properties of the model. Various versions available of the retinex algorithm are characterized by different procedures for exploring the image content (so as to obtain, for each pixel, a reference white value), then used to rescale the pixel lightness. Here we propose an alternative procedure, which computes the reference white value from the percentile values of the pixel population. We formalize two versions of the algorithm: one with global and one with local behavior, characterized by different computational costs.

Activity Theory for OSS Ecosystems

The digital business ecosystem is an innovative approach to support the adoption and development of information and communication technologies (ICT). A natural life ecosystem is a biological community of interacting organisms and their physical environments. Conversely, a business ecosystem is a network of buyers, suppliers and makers of related products or services, plus the socio-economic environment that includes the institutional and regulatory framework. The development process of an OSS environment can be modelled as an information ecosystem. This paper describes how activity theory can be used to inform the development of OSS projects.

Energy-driven path search for Termite Retinex

The human color sensation depends on the local and global spatial arrangements of the colors in the scene. Emulating this dependence requires the exploration of the image in search of a white reference. The algorithm Termite Retinex explores the image by a set of paths resembling traces of a swarm of termites. Starting from this approach, we develop a novel spatial exploration scheme where the termite paths are local minimums of an energy function, which depend on the image visual content. The energy is designed to favor the visitation of regions containing information relevant to the color sensation while minimizing the coverage of less essential regions. This exploration method contributes to the investigation of the spatial properties of the color sensation and, to the best of our knowledge, is the first model relying on mathematical global conditions for the Retinex paths. The experiments show that the estimation of the color sensation obtained by means of the proposed spatial sampling is a valid alternative to the one based on Termite Retinex.

Geo-based automatic image annotation

A huge number of user-tagged images are daily uploaded to the web. Recently, a growing number of those images are also geotagged. These provide new opportunities for solutions to automatically tag images so that efficient image management and retrieval can be achieved. In this paper an automatic image annotation approach is proposed. It is based on a statistical model that combines two different kinds of information: high level information represented by user tags of images captured in the same location as a new unlabeled image (input image); and low level information represented by the visual similarity between the input image and the collection of geographically similar images. To maximize the number of images that are visually similar to the input image, an iterative visual matching approach is proposed and evaluated. The results show that a significant recall improvement can be achieved with an increasing number of iterations. The quality of the recommended tags has also been evaluated and an overall good performance has been observed.

Discovering the software process by means of stochastic workflow analysis

A fundamental feature of the software process consists in its own stochastic nature. A convenient approach for extracting the stochastic dynamics of a process from log data is that of modelling the process as a Markov model: in this way the discovery of the short/medium range dynamics of the process is cast in terms of the learning of Markov models of different orders, i.e. in terms of learning the corresponding transition matrices. In this paper we show that the use of a full Bayesian approach in the learning process helps providing robustness against statistical noise and over-fitting, as the size of a transition matrix grows exponentially with the order of the model. We give a specific model-model similarity definition and the corresponding calculation procedure to be used in model-to-sequence or sequence-to-sequence conformance assessment, this similarity definition could also be applied to other inferential tasks, such as unsupervised process learning.

A population-based approach to point-sampling spatial color algorithms

Inspired by the behavior of the human visual system, spatial color algorithms perform image enhancement by correcting the pixel channel lightness based on the spatial distribution of the intensities in the surrounding area. The two visual contrast enhancement algorithms RSR and STRESS belong to this family of models: they rescale the input based on local reference values, which are determined by exploring the image by means of random point samples, called sprays. Due to the use of sampling, they may yield a noisy output. In this paper, we introduce a probabilistic formulation of the two models: our algorithms (RSR-P and STRESS-P) rely implicitly on the whole population of possible sprays. For processing larger images, we also provide two approximated algorithms that exploit a suitable target-dependent space quantization. Those spray population-based formulations outperform RSR and STRESS in terms of the processing time required for the production of noiseless outputs. We argue that this population-based approach, which can be extended to other members of the family, complements the sampling-based approach, in that it offers not only a better control in the design of approximated algorithms, but also additional insight into individual models and their relationships. We illustrate the latter point by providing a model of halo artifact formation.

A Game-Theoretical Approach to Data-Privacy Protection from Context-Based Inference Attacks: A Location-Privacy Protection Case Study

One of the approaches to the problem of data-privacy protection is given by the application of obfuscation techniques; in many situations, however, context information can help an attacker to perform inference over obfuscated data and to refine the estimate of the sensitive data up to a violation of the original privacy requirements. We consider the problem in a location privacy protection set-up where the sensitive attribute to be protected is the position of a Location Based Service user, and where the location anonymization technique is cloaking, whereas the context, supporting inference attacks, consists in some landscape-related information, namely positional constraints. In this work we adopt the assumption that the anonymizer and the attacker are two rational agents and frame the problem in a game theoretical approach by modeling the contest as a two-player, zero-sum, signaling game, then we point to the corresponding equilibrium solution and show that, when the anonymizer plays the equilibrium strategies, the advantage provided to the attacker by a non-neutral landscape gets canceled. We suggest that the game theoretical solution could be used as a reference solution for inter-technique comparisons.