Luca Vicidomini

Exploiting D-Mason on Parallel Platforms: A Novel Communication Strategy

Agent-based simulation models are a powerful experimental tool for research and management in many scientific and technological fields. D-Mason is a parallel version of Mason, a library for writing and running Agent-based simulations. In this paper, we present a novel development of D-Mason, a decentralized communication strategy which realizes a Publish/Subscribe paradigm through a layer based on the MPI standard. We show that our communication mechanism is much more scalable and efficient than the previous centralized one.

On Evaluating Graph Partitioning Algorithms for Distributed Agent Based Models on Networks

Graph Partitioning is a key challenge problem with application in many scientific and technological fields. The problem is very well studied with a rich literature and is known to be NP-hard. Several heuristic solutions, which follow diverse approaches, have been proposed, they are based on different initial assumptions that make them difficult to compare. An analytical comparison was performed based on an Implementation Challenge [3], however being a multi-objective problem (two opposing goals are for instance load balancing and edge-cut size), the results are difficult to compare and it is hard to foresee what can be the impact of one solution, instead of another, in a real scenario. In this paper we analyze the problem in a real context: the development of a distributed agent-based simulation model on a network field (which for instance can model social interactions).

Distributed Agent-Based Simulation and GIS: An Experiment with the Dynamics of Social Norms

In the last decade, the investigation of the social complexity has witnessed the rise of Computational Social Science, a research paradigm that heavily relies upon data and computation to foster our understanding of social phenomena. In this field, a key role is played by the explanatory and predictive power of agent-based social simulations that are showing to take advantage of GIS, higher number of agents and real data. We focus GIS based distibuted ABMs. We observed that the density distribution of agents, over the field, strongly impact on the overall performances. In order to better understand this issue, we analyzes three different scenarios ranging from real positioning, where the citizens are positioned according to a real dataset to a random positioning where the agent are positioned uniformly at random on the field. Results confirm our hypothesis and show that an irregular distribution of the agents over the field increases the communication overhead. We provide also an analytic analysis which, in a 2-dimensional uniform field partitioning, is affected by several parameters (which depend on the model), but is also influenced by the density distribution of agents over the field. According to the presented results, we have that uniform space partitioning strategy does not scale on GIS based ABM characterized by an irregular distribution of agents.

Engaging Citizens with a Social Platform for Open Data

Open Data are valuable initiatives in favour of transparency. Public administrations are increasing the availability of datasets for citizens, associations, innovators and other stakeholders, by releasing their data with open licenses. Open initiatives are achieving less success than expected, mainly due to the lack of engagement. There is a growing demand for approaches to actively engage citizens in exploiting Open Data. This paper introduces SPOD, a Social Platform for Open Data, which aims to engage citizens, local associations and organizations in forming communities of interests, stimulating the interpretation of Open Data and exploiting their use in Data-driven discussions, something not well-supported on traditional social networks. Social collaboration is the key aspect to increase the public value, where citizens participate in the discussions, co-create knowledge and data. The paper describes the engagement of four communities of citizens, which contributed to the public value by discussing topics in the context of Cultural Heritage, generating information from existing and co-created open datasets, by using SPOD.

Datalet-Ecosystem Provider (DEEP): Scalable Architecture for Reusable, Portable and User-Friendly Visualizations of Open Data

This paper presents the DatalEt-Ecosystem Provider (DEEP), an extensible, and scalable Edge-centric architecture to visualize Open Data, retrieved in real time from institutional open data portals. The aim is to engage citizens and stakeholders through reusable, portable and interactive visualizations, named datalets. The DEEP architecture exploits the increasing computing power and capacity of end-users devices, moving the computation to process and visualize data, from the central server, directly to the client-side ensuring data trustiness, privacy, scalability and dynamic data loading. DEEP and its datalets have been fully exploited, in the ROUTE-TO-PA, HORIZON 2020 funded project, by five public administrations across Europe as pilot projects. The project engages and involves citizens in creating, sharing and commenting existing visualizations of Open Data. DEEP is open source, its source code is fully available on GitHub, thus every single component can be reused by other projects.

D-Mason on the Cloud: An Experience with Amazon Web Services

D-Mason framework is a parallel version of the Mason library for writing and running Agent-based simulations – a class of models that, by simulating the behavior of multiple agents, aims to emulate and/or predict complex phenomena. D-Mason has been conceived to harness the amount of unused computing power available in common installations like educational laboratory. Then the focus moved to dedicated installation, such as massively parallel machines or supercomputing centers. In this paper, D-Mason takes another step forward and now it can be used on a cloud environment.

D-MASON: A Short Tutorial

D-Mason framework is a parallel version of the Mason library for writing and running Agent-based simulations. We briefly present Mason architecture, functionalities and some programming examples. Full documentation, additional tutorials and other material can be found at www.dmason.org where the framework can be downloaded.

Agile production of high quality open data

This paper introduces an Agile Methodology to support the production of Open Data (OD) which can be internally adopted and adapted by Public Agencies (PAs) in order to make data publicly available. The methodology is iterative, incremental, evolutionary, test-driven, and collaborative. The novel idea is that PAs produce open data and, at same time, envision and anticipate possible uses and re-uses by citizens. The opportunity is that PAs will publish use cases along with Open Data as a way to engage citizens. The fit-for-use test is a specific step of the methodology in which PAs conceive possible uses of the dataset by creating a set of relevant visualisations (e.g., charts). This step mitigates some well-known barriers in the field of the OD, such as, that the data could be not accurate, not interesting, or too costly to be re-used [27]. The paper describes a platform named SPOD, based on the above agile methodology, meant to be used by PAs over their Intranet to support the collaborative process to make data publicly available. Moreover, the methodology and the SPOD have specific steps to assist PAs in checking the dataset quality through syntactic, sanity and domain-specific steps implemented through a set of heuristics. Both the methodology and the SPOD have been successfully adopted by the Council of the Campania Region in Italy to produce their open data, this experience has detailed described and reported in the paper.

A comprehensive architecture to support open data access, co-creation, and dissemination

Contemporary data infrastructures are yet to afford easy access to available data, better understanding of these data, engagement around data to drive collective sense-making and knowledge co-creation. This work synthesizes the knowledge gained through a 40-month research and innovation project which led to designing and implementing an architecture which addresses those challenges.

Increasing Public Value through Co-Creation of Open Knowledge

The aim of our research is to study how to increase Public Value through the collective participation, involving Public Administrations, stakeholders and citizens together. The Public Value for citizens is in the available and gained Knowledge. The paper models this concept by introducing a variant of the classic Data-Information-Knowledge pyramid, considering everything published as open and public. The paper introduces a social and iterative process designed for user appropriation, that includes the Knowledge and Data Co-Creation with the aim to generate public Open Knowledge. Users with process and technology appropriation can creatively follow the process in different ways. The paper concludes by introducing a brief preliminary scenario that exploits the process, platform and technology in the context of Cultural Heritage.