Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.Comment: Accepted by International Conference on Computational Science (ICCS) 2017 which will be held in Zurich, Switzerland from June 11-June 1

Evaluation and optimization of Big Data Processing on High Performance Computing Systems

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] Hoxe en día, moitas organizacións empregan tecnoloxías Big Data para extraer información de grandes volumes de datos. A medida que o tamaño destes volumes crece, satisfacer as demandas de rendemento das aplicacións de procesamento de datos masivos faise máis difícil. Esta Tese céntrase en avaliar e optimizar estas aplicacións, presentando dúas novas ferramentas chamadas BDEv e Flame-MR. Por unha banda, BDEv analiza o comportamento de frameworks de procesamento Big Data como Hadoop, Spark e Flink, moi populares na actualidade. BDEv xestiona a súa configuración e despregamento, xerando os conxuntos de datos de entrada e executando cargas de traballo previamente elixidas polo usuario. Durante cada execución, BDEv extrae diversas métricas de avaliación que inclúen rendemento, uso de recursos, eficiencia enerxética e comportamento a nivel de microarquitectura. Doutra banda, Flame-MR permite optimizar o rendemento de aplicacións Hadoop MapReduce. En xeral, o seu deseño baséase nunha arquitectura dirixida por eventos capaz de mellorar a eficiencia dos recursos do sistema mediante o solapamento da computación coas comunicacións. Ademais de reducir o número de copias en memoria que presenta Hadoop, emprega algoritmos eficientes para ordenar e mesturar os datos. Flame-MR substitúe o motor de procesamento de datos MapReduce de xeito totalmente transparente, polo que non é necesario modificar o código de aplicacións xa existentes. A mellora de rendemento de Flame-MR foi avaliada de maneira exhaustiva en sistemas clúster e cloud, executando tanto benchmarks estándar coma aplicacións pertencentes a casos de uso reais. Os resultados amosan unha redución de entre un 40% e un 90% do tempo de execución das aplicacións. Esta Tese proporciona aos usuarios e desenvolvedores de Big Data dúas potentes ferramentas para analizar e comprender o comportamento de frameworks de procesamento de datos e reducir o tempo de execución das aplicacións sen necesidade de contar con coñecemento experto para elo.[Resumen] Hoy en día, muchas organizaciones utilizan tecnologías Big Data para extraer información de grandes volúmenes de datos. A medida que el tamaño de estos volúmenes crece, satisfacer las demandas de rendimiento de las aplicaciones de procesamiento de datos masivos se vuelve más difícil. Esta Tesis se centra en evaluar y optimizar estas aplicaciones, presentando dos nuevas herramientas llamadas BDEv y Flame-MR. Por un lado, BDEv analiza el comportamiento de frameworks de procesamiento Big Data como Hadoop, Spark y Flink, muy populares en la actualidad. BDEv gestiona su configuración y despliegue, generando los conjuntos de datos de entrada y ejecutando cargas de trabajo previamente elegidas por el usuario. Durante cada ejecución, BDEv extrae diversas métricas de evaluación que incluyen rendimiento, uso de recursos, eficiencia energética y comportamiento a nivel de microarquitectura. Por otro lado, Flame-MR permite optimizar el rendimiento de aplicaciones Hadoop MapReduce. En general, su diseño se basa en una arquitectura dirigida por eventos capaz de mejorar la eficiencia de los recursos del sistema mediante el solapamiento de la computación con las comunicaciones. Además de reducir el número de copias en memoria que presenta Hadoop, utiliza algoritmos eficientes para ordenar y mezclar los datos. Flame-MR reemplaza el motor de procesamiento de datos MapReduce de manera totalmente transparente, por lo que no se necesita modificar el código de aplicaciones ya existentes. La mejora de rendimiento de Flame-MR ha sido evaluada de manera exhaustiva en sistemas clúster y cloud, ejecutando tanto benchmarks estándar como aplicaciones pertenecientes a casos de uso reales. Los resultados muestran una reducción de entre un 40% y un 90% del tiempo de ejecución de las aplicaciones. Esta Tesis proporciona a los usuarios y desarrolladores de Big Data dos potentes herramientas para analizar y comprender el comportamiento de frameworks de procesamiento de datos y reducir el tiempo de ejecución de las aplicaciones sin necesidad de contar con conocimiento experto para ello.[Abstract] Nowadays, Big Data technologies are used by many organizations to extract valuable information from large-scale datasets. As the size of these datasets increases, meeting the huge performance requirements of data processing applications becomes more challenging. This Thesis focuses on evaluating and optimizing these applications by proposing two new tools, namely BDEv and Flame-MR. On the one hand, BDEv allows to thoroughly assess the behavior of widespread Big Data processing frameworks such as Hadoop, Spark and Flink. It manages the configuration and deployment of the frameworks, generating the input datasets and launching the workloads specified by the user. During each workload, it automatically extracts several evaluation metrics that include performance, resource utilization, energy efficiency and microarchitectural behavior. On the other hand, Flame-MR optimizes the performance of existing Hadoop MapReduce applications. Its overall design is based on an event-driven architecture that improves the efficiency of the system resources by pipelining data movements and computation. Moreover, it avoids redundant memory copies present in Hadoop, while also using efficient sort and merge algorithms for data processing. Flame-MR replaces the underlying MapReduce data processing engine in a transparent way and thus the source code of existing applications does not require to be modified. The performance benefits provided by Flame- MR have been thoroughly evaluated on cluster and cloud systems by using both standard benchmarks and real-world applications, showing reductions in execution time that range from 40% to 90%. This Thesis provides Big Data users with powerful tools to analyze and understand the behavior of data processing frameworks and reduce the execution time of the applications without requiring expert knowledge

Improvements to GeoQA, a Question Answering system for Geospatial Questions

Η παρούσα εργασία αποτελεί μια προσπάθεια για συγκέντρωση, μελέτη και σύγκριση συστημάτων απάντησης ερωτήσεων όπως τα QUINT, TEMPO και NEQA και του σκελετού συστημάτων απάντησης ερωτήσεων Frankenstein. Η μελέτη επικεντρώνεται στην απάντηση ερωτήσεων σε γεωχωρικά δεδομένα και πιο στο σύστημα GeoQA. Το σύστημα αυτό έχει προταθεί πρόσφατα και ειναι το πρώτο σύστημα απάντησης ερωτήσεων πάνω σε συνδεδεμένα γεωχωρικά δεδομένα βασιζόμενο σε πρότυπα. Βελτιώνουμε το παραπάνω σύστημα χρησιμοποιώντας τα δεδομένα για το σχήμα των βάσεων γνώσης του, προσθέτοντας πρότυπα για πιο σύνθετες ερωτήσεις και αναπτύσσοντας το υποσύστημα για την επεξεργασία φυσικής γλώσσας.We study the question-answering GeoQA which was proposed recently. GeoQA is the first template-based question answering system for linked geospatial data. We improve this system by exploiting the data schema information of the kb’s it’s using, adding more templates for more complex queries and by improving the natural language processing module in order to recognize the patterns. The current work is also an attempt to concentrate, study and compare some other question-answering systems like QUINT, Qanary methodology and Frankenstein framework for question answering systems

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Understanding the Relationship Between People and Their Environments Using Smartphone Data: A Study of Personality, Places Visited, and Emotional Experiences

Much has been theorized about the relationship between people and their environments. Certain people may be more inclined to visit certain types of places (e.g., campus, pub) and display different patterns of mobility as they move among them (e.g., number of places visited, distances traveled). Moreover, even the same place may affect people differently, depending on their psychological characteristics (e.g., personality). In this dissertation, I draw upon recent technological advances in smartphone-sensing methods to investigate the relationship between people’s psychological characteristics and their physical movements through space. I begin by reviewing the existing psychological literature. I next describe features that can be extracted from GPS data and categorize them to provide a framework for collecting, analyzing, and discussing mobility. Then, I conduct an empirical investigation demonstrating this methodology at work. One-hundred and eighteen participants provided ecological momentary assessments, reporting their places visited and emotional states (e.g., feeling stressed, relaxed, sad) four times per day for two to four weeks. In addition to these ecological momentary assessments, place and mobility data were also automatically collected for forty students using their smartphone’s GPS sensors. I supplemented these data by collecting place attributions from an independent sample of 267 participants who evaluated the situational characteristics (e.g., sociality, positivity) of the most commonly visited locations. Lastly, I look at how people perceive places and whether their judgments about a location (e.g., predictions about the personality of those most likely to visit a location) demonstrate consensus or accuracy. A lens model analysis highlights the cues underlying these perceptions. The results show how places visited (based on self-reported places) and mobility patterns (based on sensed GPS data) are related to people’s in-the-moment emotional experiences and their enduring psychological characteristics, such as their personality and wellbeing. I also examine how one’s personality interacts with the situational characteristics of a place to affect emotional states. For instance, one key finding reveals that, in general, participants experienced more positive emotions in social places (e.g., common rooms, pubs) but that this was especially true for more extraverted individuals. Lastly, I find that though people demonstrate consensus in their judgments when virtually visiting a place, they do not show significant accuracy. My discussion focuses on the benefits of using place and GPS-based mobility measures to understand the relationship between people and their environments, as well as the unique methodological and logistical challenges inherent to this. I conclude by discussing potential implications for privacy and research ethics and point to promising directions for future research