Multimedia web searches using static SAT

In this paper we describe the parallelization of a data structure used to perform multimedia web searches. Multimedia Web Engines have not been deeply studied and is a challenging issue. The data structure selected to index the queries is the Spatial Approximation Tree, where the complexity measure is given by the number of distance computed to retrieve those objects close enough to the query. We present a parallel method for load balancing the work performed by the processors. The method can adapt itself to the changes of the workload produced by the user queries. Empirical results with di fferent kind of databases show e fficient performance in a real cluster of PC. The algorithm is designed with the bulk-synchronous model of parallel computingVII Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Latent and explicit mnemonic communities on social media:studying digital memory formation through hashtag co-occurrence analysis

This article explores the nature and dynamics of mnemonic communities within the context of social media platforms and proposes to identify mnemonic communities using hashtag co-occurrence analysis. The article distinguishes between 'explicit' and 'latent' mnemonic communities, arguing that while some digital mnemonic communities may exhibit characteristics of offline communities, others exist latently as discursive spaces or semiospheres without direct awareness. On platforms like Instagram, hashtags function as semiotic markers, but also as user-chosen indexes to the content. As hashtags link the social and semantic aspects of community formation, hashtag co-occurrence analysis offers a robust framework for understanding and mapping these communities. This method allows to detect and analyse patterns of hashtag use that suggest the presence of networked community structures that may not be apparent or conscious to the social media users themselves. Additionally, a metric is introduced for determining the degree of 'latentness' of communities that quantifies the cohesion within communities compared to their external connections. The article demonstrates this approach by applying hashtag co-occurrence analysis to a dataset of Instagram posts tagged with #Juneteenth, a popular hashtag used to commemorate the ending of slavery in the United States. It identifies 87 mnemonic communities that reflect the diversity and complexity of how platforms facilitate memory-sharing practices and the role of semiotic markers in forming (latent) mnemonic networks.</p