1,721,136 research outputs found
SeDaSOMA: A Framework for Supporting Serendipitous, Data-As-A-Service-Oriented, Open Big Data Management and Analytics
No full textThis paper describes the anatomy of SeDaSOMA, a reference framework for supporting serendipitous, data-as-a-service-oriented, open big data management and analytics. The proposed framework aims at supporting advanced big data management and analytics by relying on innovative research findings and next-generation big data tools. The paper also depicts some Cloud-aware big data vertical applications of SeDaSOMA in specific scenarios that are currently of great interest
A novel GPU-aware Histogram-based algorithm for supporting moving object segmentation in big-data-based IoT application scenarios
No full textMultimedia data are a popular case of Big Data that expose the classical 3V characteristics (i.e., volume, velocity and variety). Such kind of data are likely to be processed within the core layer of Internet of Things (IoT) platforms, where a multiple, typically high, number of “things” (e.g., sensors, devices, actuators, and so forth) collaborate to massively process big data for supporting intelligent algorithms running over them. In such platforms, the computational bottleneck is very often represented by the component running the main algorithm, while communication and cooperation costs still remain relevant. Inspired by this emerging trend of big-data-based IoT applications, in this paper we focus on the specific application context represented by the problem of supporting moving object segmentation over images originated in the context of big multimedia data, and we propose an innovative background maintenance approach to this end. In particular, we provide a novel GPU-aware Histogram-based Moving Object Segmentation algorithm that adopts a pixel-oriented approach and it is based on Graphic Processing Units (GPU), called PIXHMOS_GPU. PIXHMOS_GPU allows us to achieve higher performance, hence making the computational gap of big-data-based IoT applications decisively smaller. Experimental results clearly confirm our arguments
An Innovative Monocular Mobile Object Self-localization Approach Based on Ceiling Vision
Full text linkThis study deals with the estimation of the position of a mobile object using ceiling landmarks images acquired by a low resolution camera placed on a mobile object. The mobile object is moving in an indoor environment where light is given by electric lamps with circular holders. The images of the circular holders are projected on the image plane of the camera and are processed by means of computer vision algorithms. The pixels of the images of the light holders on the ceiling are mapped to the pixels of the images of the light holders on the image plane of the camera by means of a two dimensional dynamic programming algorithm (2D-DPA). The projection distortions are thus compensated and this reduces the estimation errors. The algorithm described in this paper estimates the distance from the camera lens to the center of the landmarks using only ceiling vision. Localization can be easily obtain from such distance estimations. The projections are geometrically described and the distance estimation is based on the pixels mapping information obtained by 2D-DPA
A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently
Full text linkIn this paper we describe a family of scan-matching based registration algorithms for tracking moving objects which fall in the emerging area that predicates the integration between robotics and big data applications. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed family of algorithms is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, algorithms are also very fast thanks to the genetic pre-alignment task and the novel metrics we propose
A novel framework for supporting mobile object self-localization via emerging artificial intelligence tools
No full textThis paper focuses the attention on the problem of supporting mobile object self-localization. The proposed algorithm estimates the distance from the camera lens to the center of the landmarks using only ceiling vision. Localization can be easily obtain from such distance estimations. Projections are geometrically described and the distance estimation is based on the pixels mapping information obtained by a two-dimensional dynamic programming algorithm (2D-DPA)
Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments
Full text linkBig moving objects arise as a novel class of big data objects in emerging environments. Here, the main problems are the following: (i) tracking, which represents the baseline operation for a plethora of higher-level functionalities, such as detection, classification, and so forth; (ii) analysis, which meaningfully marries with big data analytics scenarios. In line with these goals, in this paper we propose a novel family of scan matching algorithms based on registration, which are enhanced by using a genetic pre-alignment phase based on a novel metrics, fist, and, second, performing a finer alignment using a deterministic approach. Our experimental assessment and analysis confirms the benefits deriving from the proposed novel family of such algorithms
Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big Data Framework: Principles, Practice, and Experiences
No full textOLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e.g., Clouds). Here, privacypreserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes
User Emotion Detection via Taxonomy Management: An Innovative System
Full text linkCatching the attention of a new acquaintance and empathize with her can improve the social skills of a robot. For this reason, we illus-trate here the first step towards a system which can be used by a social robot in order to "break the ice"between a robot and a new acquain-tance. After a training phase, the robot acquires a sub-symbolic coding of the main concepts being expressed in tweets about the IAB Tier-1 categories. Then this knowledge is used to catch the new acquaintance interests, which let arouse in her a joyful sentiment. The analysis process is done alongside a general small talk, and once the process is finished, the robot can propose to talk about something that catches the attention of the user, hopefully letting arise in him a mix of feelings which involve surprise and joy, triggering, therefore, an engagement between the user and the social robot
Dempster-Shafer-Based Fusion of Multi-Modal Biometrics for Supporting Identity Verification Effectively and Efficiently
Full text linkThe purpose of this paper is to describe a novel fusion algorithm for multi-modal biometric identification. In this paper we describe the fusion of fingerprints and voice. This combination of biometrics is rarely used in verification systems although this biometric pair is simple to use and not too invasive. A framework for the combination of several data fusion algorithms is described. In this paper we use only two types of data fusion techniques, namely weighted sum and fuzzy system. Two independent identity decisions can be thus obtained, and from them two beliefs that the identity is verified can be derived. The two beliefs are combined using Dempster-Shafer's approach to obtain the final decision. The results are reported by ROC curves
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