Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Design of high fidelity building energy monitoring system

Building energy meter network, based on per-appliance monitoring system, willbe an important part of the Advanced Metering Infrastructure. Two key issues exist for designing such networks. One is the network structure to be used. The other is the implementation of the network structure on a large amount of small low power devices, and the maintenance of high quality communication when the devices have electric connection with high voltage AC line. The recent advancement of low-power wireless communication makes itself the right candidate for house and building energy network. Among all kinds of wireless solutions, the low speed but highly reliable 802.15.4 radio has been chosen in this design. While many network-layer solutions have been provided on top of 802.15.4, an IPv6 based method is used in this design. 6LOWPAN is the particular protocol which adapts IP on low power personal network radio. In order to extend the network into building area without, a specific network layer routing mechanism-RPL, is included in this design. The fundamental unit of the building energy monitoring system is a smart wall plug. It is consisted of an electricity energy meter, a RF communication module and a low power CPU. The real challenge for designing such a device is its network firmware. In this design, IPv6 is implemented through Contiki operation system. Customize hardware driver and meter application program have been developed on top of the Contiki OS. Some experiments have been done, in order to prove the network ability of this system.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Differential recruitment of brain networks during visuospatial and color processing: Evidence from ERP microstates

Recent functional magnetic resonance imaging (fMRI) studies consistently revealed contributions of fronto-parietal and related networks to the execution of a visuospatial judgment task, the so-called "Clock Task". However, due to the low temporal resolution of fMRI, the exact cortical dynamics and timing of processing during task performance could not be resolved until now. In order to clarify the detailed cortical activity and temporal dynamics, 14 healthy subjects performed an established version of the "Clock Task", which comprises a visuospatial task (angle discrimination) and a control task (color discrimination) with the same stimulus material, in an electroencephalography (EEG) experiment. Based on the time-resolved analysis of network activations (microstate analysis), differences in timing between the angle compared to the color discrimination task were found after sensory processing in a time window starting around 200ms. Significant differences between the two tasks were observed in an analysis window from 192ms to 776ms. We divided this window in two parts: an early phase - from 192ms to ∼440ms, and a late phase - from ∼440ms to 776ms. For both tasks, the order of network activations and the types of networks were the same, but, in each phase, activations for the two conditions were dominated by differing network states with divergent temporal dynamics. Our results provide an important basis for the assessment of deviations in processing dynamics during visuospatial tasks in clinical populations.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Redes personales digitales y contexto escolar : estudio de un grupo de adolescentes de Mendoza (Argentina)

Este artículo se centra en el análisis de redes personales digitales de un grupo de adolescentes del último año de una escuela secundaria de gestión privada en Mendoza, Argentina. Se escogió Facebook porque es la red más difundida entre ellos. El objetivo de este estudio fue analizar las redes de Facebook de adolescentes de una escuela con una fuerte implementación de las TIC y comprender las percepciones que tienen sobre sus vinculaciones digitales. La recolección de los datos fueron tres momentos (administración de TouchGraph sobre Facebook, entrevista individual y Focus Group). El análisis de los datos fue cuantitativo, de análisis de grafos y cualitativo. Con respecto a los resultados se observaron que los varones tuvieron redes con mayor dispersión en cuanto al número de miembros y las mujeres mayor homogeneidad (menor número de subgrupos). En los grafos se observó que la familia tuvo una baja importancia (está aislada, es pequeña y con baja relación o no está). El análisis cualitativo, reveló un acercamiento crítico a Facebook y que la protección de la privacidad obstaculiza la relación a través de esta red con la escuela y los padres. Finalmente las redes personales digitales brindan apoyo afectivo, informacional, tangible y axiológico.

Study of OLSR protocol with Network Simulator 2 over Linux

Until a few years ago, most of the network communications were based in the wire as the physical media, but due to the advances and the maturity of the wireless communications, this is changing. Nowadays wireless communications offers fast, secure, efficient and reliable connections. Mobile communications are in expansion, clearly driven by the use of smart phones and other mobile devices, the use of laptops, etc… Besides that point, the inversion in the installation and maintenance of the physical medium is much lower than in wired communications, not only because the air has no cost, but because the installation and maintenance of the wire require a high economic cost. Besides the economic cost we find that wire is a more vulnerable medium to external threats such as noise, sabotages, etc… There are two different types of wireless networks: those which the structure is part of the network itself and those which have a lack of structure or any centralization, in a way that the devices that form part of the network can connect themselves in a dynamic and random way, handling also the routing of every control and information messages, this kind of networks is known as Ad-hoc. In the present work we will proceed to study one of the multiple wireless protocols that allows mobile communications, it is Optimized Link State Routing, from now on, OLSR, it is an pro-active routing, standard mechanism that works in a distributed in order to stablish the connections among the different nodes that belong to a wireless network. Thanks to this protocol it is possible to get all the routing tables in all the devices correctly updated every moment through the periodical transmission of control messages and on this way allow a complete connectivity among the devices that are part of the network and also, allow access to other external networks such as virtual private networks o Internet. This protocol could be perfectly used in environments such as airports, malls, etc… The update of the routing tables in all the devices is got thanks to the periodical transmission of control messages and finally it will offer connectivity among all the devices and the corresponding external networks. For the study of OLSR protocol we will have the help of the network simulator “Network Simulator 2”, a freeware network simulator programmed in C++ based in discrete events. This simulator is used mainly in educational and research environments and allows a very extensive range of protocols, both, wired networks protocols and wireless network protocols, what is going to be really useful to proceed to the simulation of different configurations of networks and protocols. In the present work we will also study different simulations with Network Simulator 2, in different scenarios with different configurations, wired networks, and Ad-hoc networks, where we will study OLSR Protocol. RESUMEN. Hasta hace pocos años, la mayoría de las comunicaciones de red estaban basadas en el cable como medio físico pero debido al avance y madurez alcanzados en el campo de las comunicaciones inalámbricas esto está cambiando. Hoy día las comunicaciones inalámbricas nos ofrecen conexiones veloces, seguras, eficientes y fiables. Las comunicaciones móviles se encuentran en su momento de máxima expansión, claramente impulsadas por el uso de teléfonos y demás dispositivos móviles, el uso de portátiles, etc… Además la inversión a realizar en la instalación y el mantenimiento del medio físico en las comunicaciones móviles es muchísimo menor que en comunicaciones por cable, ya no sólo porque el aire no tenga coste alguno, sino porque la instalación y mantenimiento del cable precisan de un elevado coste económico por norma. Además del coste económico nos encontramos con que es un medio más vulnerable a amenazas externas tales como el ruido, escuchas no autorizadas, sabotajes, etc… Existen dos tipos de redes inalámbricas: las constituidas por una infraestructura que forma parte más o menos de la misma y las que carecen de estructura o centralización alguna, de modo que los dispositivos que forman parte de ella pueden conectarse de manera dinámica y arbitraria entre ellos, encargándose además del encaminamiento de todos los mensajes de control e información, a este tipo de redes se las conoce como redes Ad-hoc. En el presente Proyecto de Fin de Carrera se procederá al estudio de uno de los múltiples protocolos inalámbricos que permiten comunicaciones móviles, se trata del protocolo inalámbrico Optimized Link State Routing, de ahora en adelante OLSR, un mecanismo estándar de enrutamiento pro-activo, que trabaja de manera distribuida para establecer las conexiones entre los nodos que formen parte de las redes inalámbricas Ad-hoc, las cuales carecen de un nodo central y de una infraestructura pre-existente. Gracias a este protocolo es posible conseguir que todos los equipos mantengan en todo momento las tablas de ruta actualizadas correctamente mediante la transmisión periódica de mensajes de control y así permitir una completa conectividad entre todos los equipos que formen parte de la red y, a su vez, también permitir el acceso a otras redes externas tales como redes privadas virtuales o Internet. Este protocolo sería usado en entornos tales como aeropuertos La actualización de las tablas de enrutamiento de todos los equipos se conseguirá mediante la transmisión periódica de mensajes de control y así finalmente se podrá permitir conectividad entre todos los equipos y con las correspondientes redes externas. Para el estudio del protocolo OLSR contaremos con el simulador de redes Network Simulator 2, un simulador de redes freeware programado en C++ basado en eventos discretos. Este simulador es usado principalmente en ambientes educativos y de investigación y permite la simulación tanto de protocolos unicast como multicast. El campo donde más se utiliza es precisamente en el de la investigación de redes móviles Ad-hoc. El simulador Network Simulator 2 no sólo implementa el protocolo OLSR, sino que éste implementa una amplia gama de protocolos, tanto de redes cableadas como de redes inalámbricas, lo cual va a sernos de gran utilidad para proceder a la simulación de distintas configuraciones de redes y protocolos. En el presente Proyecto de Fin de Carrera se estudiarán también diversas simulaciones con el simulador NS2 en diferentes escenarios con diversas configuraciones; redes cableadas, redes inalámbricas Ad-hoc, donde se estudiará el protocolo antes mencionado: OLSR. Este Proyecto de Fin de Carrera consta de cuatro apartados distintos: Primeramente se realizará el estudio completo del protocolo OLSR, se verán los beneficios y contrapartidas que ofrece este protocolo inalámbrico. También se verán los distintos tipos de mensajes existentes en este protocolo y unos pequeños ejemplos del funcionamiento del protocolo OLSR. Seguidamente se hará una pequeña introducción al simulador de redes Network Simulator 2, veremos la historia de este simulador, y también se hará referencia a la herramienta extra NAM, la cual nos permitirá visualizar el intercambio de paquetes que se produce entre los diferentes dispositivos de nuestras simulaciones de forma intuitiva y amigable. Se hará mención a la plataforma MASIMUM, encargada de facilitar en un entorno académico software y documentación a sus alumnos con el fin de facilitarles la investigación y la simulación de redes y sensores Ad-hoc. Finalmente se verán dos ejemplos, uno en el que se realizará una simulación entre dos PCs en un entorno Ethernet y otro ejemplo en el que se realizará una simulación inalámbrica entre cinco dispositivos móviles mediante el protocolo a estudiar, OLSR.

Item analysis /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Knowledge Presentation and Reasoning with Loglinear Models

Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.

Directionality based preventive link maintenance scheme for mobile ad hoc networks

The purpose of this study was to design a preventive scheme using directional antennas to improve the performance of mobile ad hoc networks. In this dissertation, a novel Directionality based Preventive Link Maintenance (DPLM) Scheme is proposed to characterize the performance gain [JaY06a, JaY06b, JCY06] by extending the life of link. In order to maintain the link and take preventive action, signal strength of data packets is measured. Moreover, location information or angle of arrival information is collected during communication and saved in the table. When measured signal strength is below orientation threshold , an orientation warning is generated towards the previous hop node. Once orientation warning is received by previous hop (adjacent) node, it verifies the correctness of orientation warning with few hello pings and initiates high quality directional link (a link above the threshold) and immediately switches to it, avoiding a link break altogether. The location information is utilized to create a directional link by orienting neighboring nodes antennas towards each other. We call this operation an orientation handoff, which is similar to soft-handoff in cellular networks. ^ Signal strength is the indicating factor, which represents the health of the link and helps to predict the link failure. In other words, link breakage happens due to node movement and subsequently reducing signal strength of receiving packets. DPLM scheme helps ad hoc networks to avoid or postpone costly operation of route rediscovery in on-demand routing protocols by taking above-mentioned preventive action. ^ This dissertation advocates close but simple collaboration between the routing, medium access control and physical layers. In order to extend the link, the Dynamic Source Routing (DSR) and IEEE 802.11 MAC protocols were modified to use the ability of directional antennas to transmit over longer distance. A directional antenna module is implemented in OPNET simulator with two separate modes of operations: omnidirectional and directional. The antenna module has been incorporated in wireless node model and simulations are performed to characterize the performance improvement of mobile ad hoc networks. Extensive simulations have shown that without affecting the behavior of the routing protocol noticeably, aggregate throughput, packet delivery ratio, end-to-end delay (latency), routing overhead, number of data packets dropped, and number of path breaks are improved considerably. We have done the analysis of the results in different scenarios to evaluate that the use of directional antennas with proposed DPLM scheme has been found promising to improve the performance of mobile ad hoc networks. ^

Cuba’s Role in Venezuela’s Control of the Internet and Online Social Networks

The arrival of Cuba’s Information Technology (IT) and Communications Minister Ramiro Valdés to Venezuela in the Spring of 2010 to serve as a ‘consultant’ to the Venezuelan government awakened a new reality in that country. Rampant with deep economic troubles, escalating crime, a murder rate that has doubled since Chávez took over in 1999, and an opposition movement led by university students and other activists who use the Internet as their primary weapon, Venezuela has resorted to Cuba for help. In a country where in large part traditional media outlets have been censored or are government-controlled, the Internet and its online social networks have become the place to obtain, as well as disseminate, unfiltered information. As such, Internet growth and use of its social networks has skyrocketed in Venezuela, making it one of Latin America’s highest Web users. Because of its increased use to spark political debate among Venezuelans and publish information that differs with the official government line, Chávez has embarked on an initiative to bring the Internet to the poor and others who would otherwise not have access, by establishing government-sponsored Internet Info Centers throughout the country, to disseminate information to his followers. With the help of Cuban advisors, who for years have been a part of Venezuela’s defense, education, and health care initiatives, Chávez has apparently taken to adapting Cuba’s methodology for the control of information. He has begun to take special steps toward also controlling the type of information flowing through the country’s online social networks, considering the implementation of a government-controlled single Internet access point in Venezuela. Simultaneously, in adapting to Venezuela’s Internet reality, Chávez has engaged online by creating his own Twitter account in an attempt to influence public opinion, primarily of those who browse the Web. With a rapidly growing following that may soon reach one million subscribers, Chávez claims to have set up his own online trench to wage cyber space battle.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

The TOMO-ETNA experiment: an imaging active campaign at Mt. Etna volcano. Context, main objectives, working-plans and involved research projects

The TOMO-ETNA experiment was devised to image of the crust underlying the volcanic edifice and, possibly, its plumbing system by using passive and active refraction/reflection seismic methods. This experiment included activities both on-land and offshore with the main objective of obtaining a new high-resolution seismic tomography to improve the knowledge of the crustal structures existing beneath the Etna volcano and northeast Sicily up to Aeolian Islands. The TOMO ETNA experiment was divided in two phases. The first phase started on June 15, 2014 and finalized on July 24, 2014, with the withdrawal of two removable seismic networks (a Short Period Network and a Broadband network composed by 80 and 20 stations respectively) deployed at Etna volcano and surrounding areas. During this first phase the oceanographic research vessel “Sarmiento de Gamboa” and the hydro-oceanographic vessel “Galatea” performed the offshore activities, which includes the deployment of ocean bottom seismometers (OBS), air-gun shooting for Wide Angle Seismic refraction (WAS), Multi-Channel Seismic (MCS) reflection surveys, magnetic surveys and ROV (Remotely Operated Vehicle) dives. This phase finished with the recovery of the short period seismic network. In the second phase the Broadband seismic network remained operative until October 28, 2014, and the R/V “Aegaeo” performed additional MCS surveys during November 19-27, 2014. Overall, the information deriving from TOMO-ETNA experiment could provide the answer to many uncertainties that have arisen while exploiting the large amount of data provided by the cutting-edge monitoring systems of Etna volcano and seismogenic area of eastern Sicily.

Multipacket reception in LTE femtocell networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores