1,720,998 research outputs found
Augmented Business Intelligence
No full textAugmented reality allows users to superimpose digital information (typically, of operational type) upon real world entities. The synergy of analytical frameworks and augmented reality opens the door to a new wave of situated OLAP, in which users within a physical environment are provided with immersive analyses of local contextual data. In this paper we propose an approach that, based on the sensed augmented context (provided by wearable and smart devices), proposes a set of relevant analytical queries to the user. This is done by relying on a mapping between the entities that can be recognized by the devices and the elements of the enterprise data, and also taking into account the queries preferred by users during previous interactions that occurred in similar contexts. A set of experimental tests evaluates the proposed approach in terms of efficiency and effectiveness
Social BI to understand the debate on vaccines on the Web and social media: unraveling the anti-, free, and pro-vax communities in Italy
No full textThe debate on vaccines in Italy has greatly intensified in recent years. The promulgation of a law that makes a set of ten vaccines obligatory has pushed this formerly niche topic to a mainstream level. The law itself is an answer to the progressive erosion of the vaccine coverage. The debate has become a political topic with three main positions: supporters of the importance of vaccines, opponents who claim that vaccines are harmful to health, and the new position of those contesting only the mandatoriness of vaccinations. In this paper, we build on a Social Business Intelligence architecture to propose an in-depth analysis of the emerging social debate. Our analysis spans over more than three years, covering all the Web and social media. We adopt several techniques, including community detection and text analytics, to understand the evolution of the debate, the discussed topics, and the structure and peculiarities of the main social communities. The study reveals that the communities are well characterized, especially from a political perspective, and provides useful insights to official health organizations to improve their communication strategies
Map-matching on big data: A distributed and efficient algorithm with a hidden Markov model
No full textA Similarity Function for Multi-Level and Multi-Dimensional Itemsets
Full text linkThe key objective of frequent itemsets (FIs) mining is uncovering relevant patterns from a transactional dataset. In particular we are interested in multi-dimensional and multi-level transactions, i.e., ones that include different points of view about the same event and are described at different levels of detail. In the context of a work aimed at devising original techniques for summarizing and visualizing this kind of itemsets, in this paper we extend the definition of itemset containment to the multi-dimensional and multi-level scenario, and we propose a new similarity function for itemsets, enabling a more effective grouping. The most innovative aspect of our similarity function is that it takes into account both the extensional and intensional natures of itemsets
Streaming Approach to Schema Profiling
Full text linkSchema profiling consists in producing key insights about the schema of data in a high-variety context. In this paper, we present a streaming approach to schema profiling, where heterogeneous data is continuously ingested from multiple sources, as is typical in many IoT applications (e.g., with multiple devices or applications dynamically logging messages). The produced profile is a clustering of the schemas extracted from the data and it is computed and evolved in real-time under the overlapping sliding window paradigm. The approach is based on two-phase k-means clustering, which entails pre-aggregating the data into a coreset and incrementally updating the previous clustering results without recomputing it in every iteration. Differently from previous proposals, the approach works in a domain where dimensionality is variable and unknown apriori, it automatically selects the optimal number of clusters, and detects cluster evolution by minimizing the need to recompute the profile. The experimental evaluation demonstrated the effectiveness and efficiency of the approach against the naïve baseline and the state-of-the-art algorithms on stream clustering
Towards a foundational API for resilient distributed systems design
No full textEngineering resilient distributed systems remains extremely challenging, particularly in mapping from collective specifications to individual device behavior. Aggregate programming aims to address this problem using a computational field abstraction to provide inherent guarantees of resilience, scalability, and safe composition. These capabilities are provided, however, by an expressive but terse set of operators too low-level for pragmatic use in complex systems development. We thus present an API to raise the level of abstraction, thereby providing an accessible and user-friendly interface for construction of complex resilient distributed systems. In particular, we capture and organize a large, heterogeneous collection of algorithms and use patterns into a unified framework, including methods for common tasks such as leader election, distance and state estimation, and gossip-based information dissemination. We demonstrate how the expressiveness of this library reduces the abstraction gap required to engineer scenarios of large-scale pervasive computing, while introducing the novel multiInstance pattern enabling an unanticipated composition of computational fields
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Crop Management with the IoT: an Interdisciplinary Survey
Full text linkIn this study we analyze how crop management is going to benefit from the Internet of Things providing an overview of its architecture and components from an agronomic and a technological perspective. The present analysis highlights that IoT is a mature enabling technology, with articulated hardware and software components. Cheap networked devices may sense crop fields at a finer grain, to give timeliness warnings on stress conditions and the presence of disease to a wider range of farmers. Cloud computing allows to reliably store and access heterogeneous data, developing and deploy farm services. From this study emerges that IoT is also going to increase attention to sensor quality and placement protocol, while Machine Learning should be oriented to produce understandable knowledge, which is also useful to enhance Cropping System Simulation systems
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