This document describes Emanuele Panigati's doctoral dissertation on the SuNDroPS system for managing semantic and dynamic data in pervasive systems. It provides an overview of SuNDroPS and its components for processing streaming and historical data, including Context-ADDICT for querying heterogeneous data sources and PerLa and Tesla for information flow processing. It also describes how SuNDroPS was tested in the motivating Green Move vehicle sharing scenario.
Slides for my Associate Professor (oavlönad docent) lecture.
The lecture is about Data Streaming (its evolution and basic concepts) and also contains an overview of my research.
Within this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) fault tolerance and (2) scalability in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.
Scientific Application Development and Early results on SummitGanesan Narayanasamy
The document summarizes Oak Ridge National Laboratory's (ORNL) new supercomputer Summit and its capabilities for scientific applications and early results. Summit is the most powerful and smartest supercomputer in the world, with 200 petaflops of performance and capabilities well-suited for machine learning and artificial intelligence applications. ORNL is preparing scientific applications for Summit through its Center for Accelerated Application Readiness program to enable early science results and ensure applications are optimized for Summit's architecture.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers.
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
This talk will examine issues of workflow execution, in particular using the Pegasus Workflow Management System, on distributed resources and how these resources can be provisioned ahead of the workflow execution. Pegasus was designed, implemented and supported to provide abstractions that enable scientists to focus on structuring their computations without worrying about the details of the target cyberinfrastructure. To support these workflow abstractions Pegasus provides automation capabilities that seamlessly map workflows onto target resources, sparing scientists the overhead of managing the data flow, job scheduling, fault recovery and adaptation of their applications. In some cases, it is beneficial to provision the resources ahead of the workflow execution, enabling the re-use of resources across workflow tasks. The talk will examine the benefits of resource provisioning for workflow execution.
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
Slides for my Associate Professor (oavlönad docent) lecture.
The lecture is about Data Streaming (its evolution and basic concepts) and also contains an overview of my research.
Within this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) fault tolerance and (2) scalability in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.
Scientific Application Development and Early results on SummitGanesan Narayanasamy
The document summarizes Oak Ridge National Laboratory's (ORNL) new supercomputer Summit and its capabilities for scientific applications and early results. Summit is the most powerful and smartest supercomputer in the world, with 200 petaflops of performance and capabilities well-suited for machine learning and artificial intelligence applications. ORNL is preparing scientific applications for Summit through its Center for Accelerated Application Readiness program to enable early science results and ensure applications are optimized for Summit's architecture.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers.
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
This talk will examine issues of workflow execution, in particular using the Pegasus Workflow Management System, on distributed resources and how these resources can be provisioned ahead of the workflow execution. Pegasus was designed, implemented and supported to provide abstractions that enable scientists to focus on structuring their computations without worrying about the details of the target cyberinfrastructure. To support these workflow abstractions Pegasus provides automation capabilities that seamlessly map workflows onto target resources, sparing scientists the overhead of managing the data flow, job scheduling, fault recovery and adaptation of their applications. In some cases, it is beneficial to provision the resources ahead of the workflow execution, enabling the re-use of resources across workflow tasks. The talk will examine the benefits of resource provisioning for workflow execution.
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
Grid optical network service architecture for data intensive applicationsTal Lavian Ph.D.
Integrated SW System Provide the “Glue”
Dynamic optical network as a fundamental Grid service in data-intensive Grid application, to be scheduled, to be managed and coordinated to support collaborative operations
From Super-computer to Super-network
In the past, computer processors were the fastest part
peripheral bottlenecks
In the future optical networks will be the fastest part
Computer, processor, storage, visualization, and instrumentation - slower "peripherals”
eScience Cyber-infrastructure focuses on computation, storage, data, analysis, Work Flow.
The network is vital for better eScience
Grid'5000: Running a Large Instrument for Parallel and Distributed Computing ...Frederic Desprez
The increasing complexity of available infrastructures (hierarchical, parallel, distributed, etc.) with specific features (caches, hyper-threading, dual core, etc.) makes it extremely difficult to build analytical models that allow for a satisfying prediction. Hence, it raises the question on how to validate algorithms and software systems if a realistic analytic study is not possible. As for many other sciences, the one answer is experimental validation. However, such experimentations rely on the availability of an instrument able to validate every level of the software stack and offering different hardware and software facilities about compute, storage, and network resources.
Almost ten years after its premises, the Grid'5000 testbed has become one of the most complete testbed for designing or evaluating large-scale distributed systems. Initially dedicated to the study of large HPC facilities, Grid’5000 has evolved in order to address wider concerns related to Desktop Computing, the Internet of Services and more recently the Cloud Computing paradigm. We now target new processors features such as hyperthreading, turbo boost, and power management or large applications managing big data. In this keynote we will both address the issue of experiments in HPC and computer science and the design and usage of the Grid'5000 platform for various kind of applications.
Scalable Distributed Real-Time Clustering for Big Data StreamsAntonio Severien
This thesis presents a scalable distributed clustering algorithm for streaming big data. The author implemented a real-time distributed clustering algorithm and a classification algorithm using the Scalable Advanced Massive Online Analysis (SAMOA) framework. SAMOA is a platform-independent framework for distributed machine learning on data streams. It provides interfaces for algorithms to be run on distributed stream processing engines like Apache S4 and Twitter Storm. The author's algorithms were tested on these platforms using the SAMOA framework.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
This document discusses tools for distributed data analysis including Apache Spark. It is divided into three parts:
1) An introduction to cluster computing architectures like batch processing and stream processing.
2) The Python data analysis library stack including NumPy, Matplotlib, Scikit-image, Scikit-learn, Rasterio, Fiona, Pandas, and Jupyter.
3) The Apache Spark cluster computing framework and examples of its use including contexts, HDFS, telemetry, MLlib, streaming, and deployment on AWS.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
Materials Data Facility: Streamlined and automated data sharing, discovery, ...Ian Foster
Reviews recent results from the Materials Data Facility. Thanks in particular to Ben Blaiszik, Jonathon Goff, and Logan Ward, and the Globus data search team. Some features shown here are still in beta. We are grateful for NIST for their support.
This document discusses image search and analysis techniques for remote sensing data. It describes an index management system that takes in data and indexes it using column-based databases. Images are analyzed to extract features that allow for image search based on compression in compressed streams. Queries can be performed on the indexed data to return similar images based on semantic labels and normalized distances from queries. Examples are provided using different remote sensing datasets, including GeoEye, DigitalGlobe, and TerraSAR-X images.
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.
Accelerating Data-driven Discovery in Energy ScienceIan Foster
A talk given at the US Department of Energy, covering our work on research data management and analysis. Three themes:
(1) Eliminate data friction (use of SaaS for research data management)
(2) Liberate scientific data (research on data extraction, organization, publication)
(3) Create discovery engines at DOE facilities (services that organize data + computation)
1) Scientists at the Advanced Photon Source use the Argonne Leadership Computing Facility for data reconstruction and analysis from experimental facilities in real-time or near real-time. This provides feedback during experiments.
2) Using the Swift parallel scripting language and ALCF supercomputers like Mira, scientists can process terabytes of data from experiments in minutes rather than hours or days. This enables errors to be detected and addressed during experiments.
3) Key applications discussed include near-field high-energy X-ray diffraction microscopy, X-ray nano/microtomography, and determining crystal structures from diffuse scattering images through simulation and optimization. The workflows developed provide significant time savings and improved experimental outcomes.
Challenges and Issues of Next Cloud Computing PlatformsFrederic Desprez
Cloud computing has now crossed the frontiers of research to reach industry. It is used every day , whether to exchange emails or make
reservations on web sites. However, many research works remain to be done to improve the performance and functionality of these platforms of tomorrow. In this talk, I will do an overview of some these theoretical and appliead researches done at INRIA and particularly around Clouds distribution, energy monitoring and management, massive data processing and exchange, and resource management.
Next Generation Grid: Integrating Parallel and Distributed Computing Runtimes...Geoffrey Fox
“Next Generation Grid – HPC Cloud” proposes a toolkit capturing current capabilities of Apache Hadoop, Spark, Flink and Heron as well as MPI and Asynchronous Many Task systems from HPC. This supports a Cloud-HPC-Edge (Fog, Device) Function as a Service Architecture. Note this "new grid" is focussed on data and IoT; not computing. Use interoperable common abstractions but multiple polymorphic implementations.
Opportunities for X-Ray science in future computing architecturesIan Foster
The world of computing continues to evolve rapidly. In just the past 10 years, we have seen the emergence of petascale supercomputing, cloud computing that provides on-demand computing and storage with considerable economies of scale, software-as-a-service methods that permit outsourcing of complex processes, and grid computing that enables federation of resources across institutional boundaries. These trends shown no signs of slowing down: the next 10 years will surely see exascale, new cloud offerings, and terabit networks. In this talk I review various of these developments and discuss their potential implications for a X-ray science and X-ray facilities.
Share and analyze geonomic data at scale by Andy Petrella and Xavier TordoirSpark Summit
This document discusses analyzing genomic data at scale using distributed machine learning tools like Spark, ADAM, and the Spark Notebook. It outlines challenges with genomic data like its large size and need for distributed teams in research projects. The document proposes sharing data, processes, and results more efficiently through tools like Shar3 that can streamline the data analysis lifecycle and allow distributed collaboration on genomic research projects and datasets.
Sistem informasi adalah kumpulan komponen yang saling berinteraksi untuk mencapai tujuan bersama dengan menerima input dan menghasilkan output. Sistem memiliki karakteristik seperti komponen, batas, lingkungan luar, penghubung, masukan, output, dan pengolahan. Sistem life cycle meliputi perencanaan, analisis, desain, implementasi, dan penggunaan.
The document discusses controlled ovarian stimulation (COS) for in vitro fertilization (IVF). It notes that the number of oocytes retrieved during COS is a key prognostic factor for live birth, and that tests like antral follicle count (AFC) and anti-Müllerian hormone (AMH) can help predict ovarian response. The goal of COS is to optimize oocyte yield while avoiding over-response and risks like ovarian hyperstimulation syndrome (OHSS). Personalizing regimens based on individual factors like age and ovarian reserve tests may improve outcomes.
This document provides 10 ways to modernize a PTA by leveraging technology and streamlining processes. Some of the key recommendations include eliminating paper use by going digital for communications and payments, offering direct donation fundraisers online, collecting PTA dues online to boost participation, using Facebook to engage the school community, and signing up for payment processing tools like Cheddar Up to simplify administration tasks and potentially earn money back on funds collected. The overall message is that embracing technology can help PTAs operate more efficiently and increase involvement.
Sistemas de informacion hospitalaria HISVictor Blanco
El documento describe los componentes clave de un sistema de información hospitalario propuesto, incluyendo los datos requeridos, los usuarios que tendrán acceso y sus roles, y los módulos principales del sistema. El sistema registrará datos sobre equipos médicos, compras, inventarios, y mantenimiento preventivo y correctivo para optimizar los recursos del hospital.
Grid optical network service architecture for data intensive applicationsTal Lavian Ph.D.
Integrated SW System Provide the “Glue”
Dynamic optical network as a fundamental Grid service in data-intensive Grid application, to be scheduled, to be managed and coordinated to support collaborative operations
From Super-computer to Super-network
In the past, computer processors were the fastest part
peripheral bottlenecks
In the future optical networks will be the fastest part
Computer, processor, storage, visualization, and instrumentation - slower "peripherals”
eScience Cyber-infrastructure focuses on computation, storage, data, analysis, Work Flow.
The network is vital for better eScience
Grid'5000: Running a Large Instrument for Parallel and Distributed Computing ...Frederic Desprez
The increasing complexity of available infrastructures (hierarchical, parallel, distributed, etc.) with specific features (caches, hyper-threading, dual core, etc.) makes it extremely difficult to build analytical models that allow for a satisfying prediction. Hence, it raises the question on how to validate algorithms and software systems if a realistic analytic study is not possible. As for many other sciences, the one answer is experimental validation. However, such experimentations rely on the availability of an instrument able to validate every level of the software stack and offering different hardware and software facilities about compute, storage, and network resources.
Almost ten years after its premises, the Grid'5000 testbed has become one of the most complete testbed for designing or evaluating large-scale distributed systems. Initially dedicated to the study of large HPC facilities, Grid’5000 has evolved in order to address wider concerns related to Desktop Computing, the Internet of Services and more recently the Cloud Computing paradigm. We now target new processors features such as hyperthreading, turbo boost, and power management or large applications managing big data. In this keynote we will both address the issue of experiments in HPC and computer science and the design and usage of the Grid'5000 platform for various kind of applications.
Scalable Distributed Real-Time Clustering for Big Data StreamsAntonio Severien
This thesis presents a scalable distributed clustering algorithm for streaming big data. The author implemented a real-time distributed clustering algorithm and a classification algorithm using the Scalable Advanced Massive Online Analysis (SAMOA) framework. SAMOA is a platform-independent framework for distributed machine learning on data streams. It provides interfaces for algorithms to be run on distributed stream processing engines like Apache S4 and Twitter Storm. The author's algorithms were tested on these platforms using the SAMOA framework.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
This document discusses tools for distributed data analysis including Apache Spark. It is divided into three parts:
1) An introduction to cluster computing architectures like batch processing and stream processing.
2) The Python data analysis library stack including NumPy, Matplotlib, Scikit-image, Scikit-learn, Rasterio, Fiona, Pandas, and Jupyter.
3) The Apache Spark cluster computing framework and examples of its use including contexts, HDFS, telemetry, MLlib, streaming, and deployment on AWS.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
Materials Data Facility: Streamlined and automated data sharing, discovery, ...Ian Foster
Reviews recent results from the Materials Data Facility. Thanks in particular to Ben Blaiszik, Jonathon Goff, and Logan Ward, and the Globus data search team. Some features shown here are still in beta. We are grateful for NIST for their support.
This document discusses image search and analysis techniques for remote sensing data. It describes an index management system that takes in data and indexes it using column-based databases. Images are analyzed to extract features that allow for image search based on compression in compressed streams. Queries can be performed on the indexed data to return similar images based on semantic labels and normalized distances from queries. Examples are provided using different remote sensing datasets, including GeoEye, DigitalGlobe, and TerraSAR-X images.
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.
Accelerating Data-driven Discovery in Energy ScienceIan Foster
A talk given at the US Department of Energy, covering our work on research data management and analysis. Three themes:
(1) Eliminate data friction (use of SaaS for research data management)
(2) Liberate scientific data (research on data extraction, organization, publication)
(3) Create discovery engines at DOE facilities (services that organize data + computation)
1) Scientists at the Advanced Photon Source use the Argonne Leadership Computing Facility for data reconstruction and analysis from experimental facilities in real-time or near real-time. This provides feedback during experiments.
2) Using the Swift parallel scripting language and ALCF supercomputers like Mira, scientists can process terabytes of data from experiments in minutes rather than hours or days. This enables errors to be detected and addressed during experiments.
3) Key applications discussed include near-field high-energy X-ray diffraction microscopy, X-ray nano/microtomography, and determining crystal structures from diffuse scattering images through simulation and optimization. The workflows developed provide significant time savings and improved experimental outcomes.
Challenges and Issues of Next Cloud Computing PlatformsFrederic Desprez
Cloud computing has now crossed the frontiers of research to reach industry. It is used every day , whether to exchange emails or make
reservations on web sites. However, many research works remain to be done to improve the performance and functionality of these platforms of tomorrow. In this talk, I will do an overview of some these theoretical and appliead researches done at INRIA and particularly around Clouds distribution, energy monitoring and management, massive data processing and exchange, and resource management.
Next Generation Grid: Integrating Parallel and Distributed Computing Runtimes...Geoffrey Fox
“Next Generation Grid – HPC Cloud” proposes a toolkit capturing current capabilities of Apache Hadoop, Spark, Flink and Heron as well as MPI and Asynchronous Many Task systems from HPC. This supports a Cloud-HPC-Edge (Fog, Device) Function as a Service Architecture. Note this "new grid" is focussed on data and IoT; not computing. Use interoperable common abstractions but multiple polymorphic implementations.
Opportunities for X-Ray science in future computing architecturesIan Foster
The world of computing continues to evolve rapidly. In just the past 10 years, we have seen the emergence of petascale supercomputing, cloud computing that provides on-demand computing and storage with considerable economies of scale, software-as-a-service methods that permit outsourcing of complex processes, and grid computing that enables federation of resources across institutional boundaries. These trends shown no signs of slowing down: the next 10 years will surely see exascale, new cloud offerings, and terabit networks. In this talk I review various of these developments and discuss their potential implications for a X-ray science and X-ray facilities.
Share and analyze geonomic data at scale by Andy Petrella and Xavier TordoirSpark Summit
This document discusses analyzing genomic data at scale using distributed machine learning tools like Spark, ADAM, and the Spark Notebook. It outlines challenges with genomic data like its large size and need for distributed teams in research projects. The document proposes sharing data, processes, and results more efficiently through tools like Shar3 that can streamline the data analysis lifecycle and allow distributed collaboration on genomic research projects and datasets.
Sistem informasi adalah kumpulan komponen yang saling berinteraksi untuk mencapai tujuan bersama dengan menerima input dan menghasilkan output. Sistem memiliki karakteristik seperti komponen, batas, lingkungan luar, penghubung, masukan, output, dan pengolahan. Sistem life cycle meliputi perencanaan, analisis, desain, implementasi, dan penggunaan.
The document discusses controlled ovarian stimulation (COS) for in vitro fertilization (IVF). It notes that the number of oocytes retrieved during COS is a key prognostic factor for live birth, and that tests like antral follicle count (AFC) and anti-Müllerian hormone (AMH) can help predict ovarian response. The goal of COS is to optimize oocyte yield while avoiding over-response and risks like ovarian hyperstimulation syndrome (OHSS). Personalizing regimens based on individual factors like age and ovarian reserve tests may improve outcomes.
This document provides 10 ways to modernize a PTA by leveraging technology and streamlining processes. Some of the key recommendations include eliminating paper use by going digital for communications and payments, offering direct donation fundraisers online, collecting PTA dues online to boost participation, using Facebook to engage the school community, and signing up for payment processing tools like Cheddar Up to simplify administration tasks and potentially earn money back on funds collected. The overall message is that embracing technology can help PTAs operate more efficiently and increase involvement.
Sistemas de informacion hospitalaria HISVictor Blanco
El documento describe los componentes clave de un sistema de información hospitalario propuesto, incluyendo los datos requeridos, los usuarios que tendrán acceso y sus roles, y los módulos principales del sistema. El sistema registrará datos sobre equipos médicos, compras, inventarios, y mantenimiento preventivo y correctivo para optimizar los recursos del hospital.
Preston Sturges led an unlikely life that mirrored the plots of his best films. He was born into a wealthy family but had a varied career, inventing products and unsuccessfully pursuing inventions after being forced out of his family's company. He began writing stories and plays in the 1920s-1930s. Moving to Hollywood in 1932, he found success as a writer and director at Paramount in the early 1940s. However, later commercial failures and a reputation as an perfectionist led him to make his last film in France before dying in 1959.
Este documento compara las teorías organizativas tradicionales y actuales. Las organizaciones tradicionales tienen un enfoque interno, visión de corto plazo y estructura rígida, mientras que las organizaciones actuales tienen un enfoque interno y externo, visión a largo plazo y estructura flexible. Ambos tipos de organizaciones usan recursos para lograr objetivos relacionados con beneficios, pero las organizaciones actuales fomentan la participación, el liderazgo participativo, la capacitación y la adaptación al cambio
Miranda is a minor but dynamic and round character from the book. She is Via's best friend since first grade and dislikes people who make fun of August. Her parents are divorced and she prefers hanging out with Via's family. Throughout the story, Miranda changes her hair color and style of clothes, demonstrating that she is a dynamic and round character.
Miranda is a minor, dynamic and round character from the book Wonder. She is Via's best friend since first grade and likes hanging out with Via's family since her own parents are divorced and her mother does not talk to her much. Miranda changed her hair color from brown to pink and the type of clothes she wears, showing she is a dynamic character that does not stay the same.
Share Success With Others To Get More Visitors!Keith Jones
The document discusses the importance of networking and sharing success with others in order to grow one's business online. It recommends complimenting other websites, getting to know popular site owners, and finding ways to promote other businesses through your own site, such as writing reviews, adding banners, or featuring their products. By helping others in this way and creating relationships, it can help drive traffic to your own site from new visitors and referrals over time.
Dokumen ini membahas reka bentuk dan model pangkalan data. Ada beberapa model pangkalan data utama seperti model hirarki yang menyusun data dalam bentuk hirarki, model jaringan yang menggunakan hubungan banyak-ke-banyak, model hubungan yang menyimpan data dalam tabel berasingan dengan menghubungkannya melalui kunci utama, dan model hubungan entiti yang menggambarkan hubungan antara entiti seperti objek dunia nyata.
Via is the main character of the project. She lives with her family and has a boyfriend and best friend. She is involved with musical theater and has a dog. While she is a minor character in helping the main character's story, she is dynamic as she learns over the course of the story not to be as overprotective of her brother Auggie. She is also considered a round character because the story reveals many personal details of her life.
This study analyzed 10,280 IVF cycles to determine if different ratios of administered luteinizing hormone (LH) to follicle-stimulating hormone (FSH) during ovarian stimulation impact the risk of clinically significant late follicular progesterone (P) elevations. The study found:
1) Stimulations using no administered LH had the highest risk of P elevation, while a ratio of 0.30-0.60 LH to FSH had the lowest risk.
2) Ratios <0.30 or >0.60 LH to FSH were associated with an increased risk of P elevation compared to a 0.30-0.60 ratio.
3) This relationship between LH/F
A central air conditioning cannot be repaired by anyone but an experienced; it has no user serviceable parts. A window or room air conditioning does have parts that can be repaired without the need for an experienced person. However, because room air conditioners vary from model to model, brand-to-brand, Air conditioning repairs will require that the owner’s manual to be consulted.
I summarize requirements for an "Open Analytics Environment" (aka "the Cauldron"), and some work being performed at the University of Chicago and Argonne National Laboratory towards its realization.
This document provides an update on perfSONAR network measurement tools, the IRIS and DyGIR projects, the Archipelago measurement platform, network services on TransPAC3 and ACE, and the Data Logistics Toolkit. Key points include:
- perfSONAR and OSCARS software will be used to provide monitoring and dynamic circuit services on TransPAC3 and ACE.
- The IRIS and DyGIR projects will develop monitoring and dynamic circuit software packages for international research networks.
- The Archipelago platform conducts large-scale IPv4 topology measurements from over 50 probes worldwide.
- TransPAC3 and ACE will provide high-performance connectivity between regions and dedicated infrastructure for data movement using the
This document describes Jean-Paul Calbimonte's doctoral research on enabling semantic integration of streaming data sources. The research aims to provide semantic query interfaces for streaming data, expose streaming data for the semantic web, and integrate streaming sources through ontology mappings. The approach involves ontology-based data access to streams, a semantic streaming query language, and semantic integration of distributed streams. Work done so far includes defining a language (SPARQLSTR) for querying RDF streams and enabling an engine to support streaming data sources through ontology mappings. Future work involves query optimization and quantitative evaluation.
Ingredients for Semantic Sensor NetworksOscar Corcho
The document discusses ingredients for creating a Semantic Sensor Web including an ontology model, URI definition practices, semantic technologies like SPARQL, and mappings to integrate sensor data. It provides an overview of the SSN ontology for describing sensors and observations. Examples are given of querying sensor data streams using SPARQL extensions and translating queries to sensor network APIs using mappings. Lessons on publishing and consuming linked stream data are also discussed.
Ontology based top-k query answering over massive, heterogeneous, and dynamic...Daniele Dell'Aglio
This document discusses ontology-based top-k continuous query answering over streaming data from multiple heterogeneous sources. It aims to investigate how ontologies and top-k queries can improve continuous query processing by exploiting ordering. The research will analyze state of the art solutions, define an evaluation framework, and assess the effects on correctness and performance of techniques that integrate stream reasoning and top-k queries. Preliminary results include an extension of an RDF stream processor testbench and a case study on real-time social media analytics.
Opening Keynote Lecture
15th Annual ON*VECTOR International Photonics Workshop
Calit2’s Qualcomm Institute
University of California, San Diego
February 29, 2016
Reflections on Almost Two Decades of Research into Stream ProcessingKyumars Sheykh Esmaili
This is the slide deck that I used during my tutorial presentation at the ACM DEBS Conference (http://www.debs2017.org/) that was held in Barcelona between June 19 and June 23, 2017.
The tutorial paper itself can be accessed here: http://dl.acm.org/citation.cfm?id=3095110
An Ad-hoc Smart Gateway Platform for the Web of Things (IEEE iThings 2013 Bes...Darren Carlson
The Web of Things (WoT) aims to extend the Web into the physical world by promoting the adoption of Web protocols by situated services and smart objects (ambient artifacts). However, real-world ambient artifacts often adopt proprietary and/or non-Web protocols, making them invisible to Web search engines and inaccessible to conventional Web agents. Smart Gateways have been proposed as a way to “Web-enable” proprietary ambient artifacts through intermediary proxy nodes; however, the requisite infrastructure is difficult to deploy at Web scale. To address such challenges, we are developing Ambient Dynamix (Dynamix): a plug-and-play context framework for mobile devices, which enables Web agents to interoperate with non-Web ambient artifacts – directly from the browser. In this paper, we describe how Dynamix can be used to transform the user’s device into an ad-hoc Smart Gateway in-situ, enabling Web applications (in the device’s browser) to seamlessly interact with non-Web ambient artifacts in the physical environment. We describe an operational prototype implementation, which enables Web apps to discover and control nearby UPnP and AirPlay media devices uniformly. We also present a performance evaluation that indicates the prototype imposes low processing and memory overhead, and is suitable for deployment on many commodity mobile devices.
This document discusses streaming data analytics and PNNL's Analytics in Motion (AIM) initiative. It provides context on data streams and continuous queries over sliding windows. It then describes AIM's goals of advancing interactive streaming analytics through human-machine feedback. Key areas of focus include streaming data characterization, hypothesis generation and testing, and infrastructure. Several use cases are outlined, including cyber defense. The document concludes by discussing AIM's testing environment and metrics for measuring performance.
This document discusses mining data streams. It begins by defining stream data and how it differs from traditional database management systems in terms of characteristics like continuous arrival of huge volumes of data that require fast real-time response. It then covers challenges in processing stream data like limited memory and approximate query answering. Common techniques for mining stream data are also introduced, such as random sampling, histograms, sliding windows, and sketches. Finally, the document discusses challenges in mining dynamics from data streams and provides examples of multi-dimensional stream analysis.
This document discusses stream reasoning, which involves making sense of gigantic, noisy data streams in real-time to support decision making. It provides background on data streams and stream processing, introduces the concept of stream reasoning, and summarizes achievements in defining continuous query languages and efficient reasoning on streams. Open challenges remain in fully combining streams with background knowledge and distributed, parallel processing.
The document summarizes discussions from Day 2 of the 2011 TERN Symposium. It describes presentations on TERN facility portals and 2010 Round 2 funding projects. It also summarizes discussions on TERN's role in environmental data collection, storage and distribution. The vision for TERN portals is to establish long-term ecosystem science as a priority, encourage long-term data management practices, and develop a network of long-term researchers. Strategies include promoting open access to data and developing robust cyberinfrastructure. The proposed portal architecture includes facility-specific and TERN-wide portals using common standards. Status updates indicate prototypes from four facilities with the TERN portal prototype available in late 2011.
Triplewave: a step towards RDF Stream Processing on the WebDaniele Dell'Aglio
The slides of my talk at INSIGHT Centre for Data Analytics (in NUI Galway) where I presented TripleWave (http://streamreasoning.github.io/TripleWave/), an open-source framework to create and publish streams of RDF data.
The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.
Transient and persistent RDF views over relational databases in the context o...Nikolaos Konstantinou
As far as digital repositories are concerned, numerous benefits emerge from the disposal of their contents as Linked Open Data (LOD). This leads more and more repositories towards this direction. However, several factors need to be taken into account in doing so, among which is whether the transition needs to be materialized in real-time or in asynchronous time intervals. In this paper we provide the problem framework in the context of digital repositories, we discuss the benefits and drawbacks of both approaches and draw our conclusions after evaluating a set of performance measurements. Overall, we argue that in contexts with infrequent data updates, as is the case with digital repositories, persistent RDF views are more efficient than real-time SPARQL-to-SQL rewriting systems in terms of query response times, especially when expensive SQL queries are involved.
How to use NCI's national repository of big spatial data collectionsARDC
This document provides an overview of how to access spatial data collections through the National Computational Infrastructure (NCI). It describes NCI's data catalog that contains various climate, satellite, and other geoscience datasets. The document outlines how users can browse the catalog, search for specific collections like CMIP5, and view metadata. It also explains that datasets are stored on NCI's global filesystems and made available through data services like THREDDS, which provides OPeNDAP, WMS, WCS, and other access methods. Users can find datasets, view them visually through Godiva, or download files through these services.
This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
1. SuNDroPS: Semantic and dyNamic Data in a
Pervasive System
Context-ADDICT Revisited
Doctoral Dissertation of:
Emanuele Panigati
Advisor: Prof. Letizia Tanca
Co-Advisors: Prof. Fabio A. Schreiber, Prof. G. Cugola
Politecnico di Milano { Dipartimento di Elettronica, Informazione e Bioingegneria
December 10th, 2014
2. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
Summary of the content
Introduction and Motivation Go
A motivating scenario: the Green Move project Go
The SuNDroPS system Go
The SuNDroPS legacy blocks Go
Context-ADDICT Go
PerLa & Tesla Go
Hystorical data analysis Go
The SuNDroPS new components Go
New features of PerLa Go
New features of Tesla/TRex: SemTRex Go
MR-Miner & MREClaT Go
Testing SuNDroPS in the Green Move scenario Go
Conclusions and
3. nal remarks Go
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
4. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
Introduction and Motivation
Users are surrounded by a high quantity of heterogeneous data,
often in the form of data streams
Humans cannot fully exploit the whole richness of these data
without digital support for their analysis
Real-time, on-the-
y and historical data processing are equally
necessary to obtain useful knowledge
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
5. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
A Motivating Scenario: the Green Move Project
Green Move is a zero-emission vehicle-sharing system which
supports the users with additional digital services
The Green Move project has been used as a real-world on-
6. eld test,
using several SuNDroPS components
The user experience is
7. nely personalized based on the user context
and on contextual preferences, so the data management process
must consider a contextual tailoring of the data
Data coming as streams from dierent kinds of sensors (e.g.,
on-board vehicle status sensor, environmental sensor, . . . ) must be
processed on-the-
y in order to give to users an immediate feedback
and empower their experience.
Dierent Information Flow Processing systems have been considered
to perform this task (PerLa TRex)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
8. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
SuNDroPS Architecture Overview
SuNDroPS allows to manage the
ow of (possibly semantically
enriched) information contained
in data streams and to study
how to extract useful knowledge
from it and from the more
traditional, static datasets.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
9. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
SuNDroPS Architecture Overview
SuNDroPS allows to manage the
ow of (possibly semantically
enriched) information contained
in data streams and to study
how to extract useful knowledge
from it and from the more
traditional, static datasets.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
10. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: The Context-ADDICT system
Allows to query dierent
and heterogeneous data
sources providing a
single entry-point for
queries
Automatically tailors the
query according to the
current context of the
user, and rewrites it,
integrating the results
coming from each
dierent data source
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
11. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: PerLa Tesla
Two information
ow
processing systems
PerLa is based on a DSMS
paradigm
Tesla/TRex is based on a
Complex Event Processing
paradigm
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
12. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
More on Information Flow Processing
Two dierent approaches:
DSMSs, developed by the database community, consider a data
stream as a sequence of tuples, processing them using SQL-like
query languages
CEPs, developed by the distributed software engineering community,
consider the stream as a sequence of events and process them using
rule and/or logic based languages for temporal pattern detection
PerLa is an example of the
13. rst kind of systems while Tesla/TRex
belongs to the second category.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
14. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
More on Information Flow Processing
Two dierent approaches:
DSMSs, developed by the database community, consider a data
stream as a sequence of tuples, processing them using SQL-like
query languages
CEPs, developed by the distributed software engineering community,
consider the stream as a sequence of events and process them using
rule and/or logic based languages for temporal pattern detection
PerLa is an example of the
15. rst kind of systems while Tesla/TRex
belongs to the second category.
We next compare the features of the two approaches.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
16. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Data
VehicleData
greenBox id Timestamp Speed
A300A 10/12/2014 14:00 0.0
B400B 10/12/2014 14:15 15.0
C100C 10/12/2014 14:01 50.0
TakenOrReleased
greenBox id Timestamp takenReleased
A300A 10/12/2014 7:00 TAKEN
A300A 10/12/2014 8:00 RELEASED
B400B 10/12/2014 10:00 TAKEN
B400B 10/12/2014 11:00 RELEASED
C100C 10/12/2014 12:00 TAKEN
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
17. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Queries
PerLa Low Level Query
CREATE SNAPSHOT MostRecentUse (greenBox id String, takenReleased Integer, date[3] Integer)
WITH DURATION 10
AS LOW:
EVERY 30 m
SELECT greenBox id, takenReleased, date[3]
HAVINGdate = MAX(date, 10)
UP TO 30m
SAMPLING ON EVENT takenInCharge Released
PerLa High Level Query
CREATE OUTPUT STREAM Theft (greenBox id String, recentUsage date)
AS HIGH:
EVERY 10 m
SELECT greenBox id, MAX(MostRecentUse.date) as mass
FROM MostRecentUse, TakenOrReleased, VehicleData
WHERE VehicleData.greenBox id = TakenOrReleased.greenBox id AND
TakenOrReleased.date = mass AND TakenOrReleased.takenReleased = 0 AND
VehicleData.speed 0
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
18. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Query Results
MostRecentUse
greenBox id takenReleased date
A300A 10/12/2014
8:00
RELEASED
B400B 10/12/2014
11:00
RELEASED
C100C 10/12/2014
12:00
TAKEN
Theft
greenBox id RecentUsage
B400B 10/12/2014 11:00
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
19. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
20. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
Rule
DEFINE Theft (ID : String)
FROM VehicleData (greenBox id = $id AND speed 0) AND
LAST Release (greenBox id = $id) WITHIN 10day FROM VehicleData AND
NOT Taken (greenBox id = $id) BETWEEN Release AND VehicleData
WHERE ID = VehicleData.greenBox id
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
21. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
Rule
DEFINE Theft (ID : String)
FROM VehicleData (greenBox id = $id AND speed 0) AND
LAST Release (greenBox id = $id) WITHIN 10day FROM VehicleData AND
NOT Taken (greenBox id = $id) BETWEEN Release AND VehicleData
WHERE ID = VehicleData.greenBox id
Results
event id : 301; greenBox id : B400B; ts : 10=12=2014 14 : 15
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
22. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Data
VehicleData
greenBox id GPS Speed
A300A 45.1,15.1 30.0
B400B 45.1,20.2 15.0
C100C 42.2,15.1 50.0
Weather
GPS Climate Limit
45.1,15.1 Normal 130
45.1,20.2 Rain 90
42.2,15.1 Ice 30
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
23. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Queries
PerLa Low Level Query
CREATE STREAM WeatherChange (position gps data, climate String)
AS LOW:
EVERY 10 m
SELECT position, climate
SAMPLING ON EVENT WeatherChanged
WHERE climate = Rain OR climate = Ice OR
climate = Snow OR climate = Fog
REFRESH EVERY 5 m
PerLa High Level Query
CREATE OUTPUT SNAPSHOT DangerousDriving (greenBox id String)
WITH DURATION 2 h
AS HIGH:
SELECT greenBox id
FROM VehicleData, WeatherChange, Weather
WHERE VehicleData.gps data = WeatherChange.position AND
WeatherChange.climate = Weather.climate AND
VehicleData.speed Weather.limits
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
24. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Query Results
DangerousDriving
position climate
45.1,20.2 Rain
42.2,15.1 Ice
WeatherChange
greenBox id
C100C
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
25. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
Tesla Data
Events
event id : 17; greenBox id : A300A; pos : 45:1; 15:1; speed : 30:0
event id : 17; greenBox id : B400B; pos : 45:1; 20:2; speed : 15:0
event id : 17; greenBox id : C100C; pos : 42:2; 15:1; speed : 50:0
event id : 40; pos : 45:1; 15:1; climate : Normal; temp : 20
event id : 40; pos : 45:1; 20:2; climate : Rain; temp : 17
event id : 40; pos : 42:2; 15:1; climate : Ice; temp : 2
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
26. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
Tesla Rules Results
Rule (Rain)
DEFINE DangerousDrivingRain(ID : String) FROM VehicleData(speed90) AND LAST
Weather(VehicleData.pos-xposVehicleData.pos+x) WITHIN 1h FROM VehicleData AND Weather.climate=rain
WHERE DangerousDrivingRain.ID = VehicleData.greenBox id
Rule (Ice)
DEFINE DangerousDrivingIce(ID : String) FROM VehicleData(speed50) AND LAST
Weather(VehicleData.pos-xposVehicleData.pos+x and temp0) WITHIN 1h FROM VehicleData WHERE
DangerousDrivingRain.ID = VehicleData.greenBox id
Results
event id : 340; greenBox id : C100C
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
27. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: Hystorical Data Analysis
Data Mining allows to extract knowledge from the gathered data,
discovering previously unknown facts from them.
Frequent Itemset Mining
28. nds in the database all the sets of items
whose frequency is above a given support threshold
Several algorithms are available to perform this task:
A Priori
Partition
FP-Growth
EClaT
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
29. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: Hystorical Data Analysis
Data Mining allows to extract knowledge from the gathered data,
discovering previously unknown facts from them.
Frequent Itemset Mining
30. nds in the database all the sets of items
whose frequency is above a given support threshold
Several algorithms are available to perform this task:
A Priori
Partition
FP-Growth
EClaT
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
31. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New Components in the Big Data Era
SuNDroPS Adds new features to
Context-ADDICT:
Monitors the environment directly,
using sensors, also reasoning on the
gathered data
Automatically infers (part of) the
user context from the
environmental data that have been
sensed
Integrates historical data processing
with analysis operations,
introducing a new parallel Data
Mining algorithms
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
32. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New features of PerLa
The PerLa middleware has been completely reengineered to include
asynchronous behaviors of sources (sensors, web services, . . . )
Distributed PerLa allows to exploit the sources (and network)
computation power
PerLa for Context explicitly integrates the context-aware approach of
Context-ADDICT with Context-Oriented Programming COP,
allowing sensors to behave dierently according to their current
context
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
33. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New features of Tesla/TRex: SemTRex
Original TRex cannot interact with static data
SemTRex adds a RDF static data repository to TRex and new
operators in the Tesla language (IN)
Integrating RDF repositories allows reasoning on the data
IN allows to:
Enrich the events, including into them facts retrieved from the KB
Filter the events using facts included in the KB
Prefetching and Caching of data become necessary to keep
reasonable response times: basic cache, parametric basic cache,
frequent data cache, combined caching strategy.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
34. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MapReduce-based Frequent Itemset Mining
MR-Miner supports the mining
processes in SuNDroPS using
MREClaT, an EClaT-based
algorithm exploiting the MapReduce
programming paradigm, that allows
SuNDroPS to analyze the data load
typical of Big Data scenarios
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
35. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MapReduce-based Frequent Itemset Mining
MR-Miner supports the mining
processes in SuNDroPS using
MREClaT, an EClaT-based
algorithm exploiting the MapReduce
programming paradigm, that allows
SuNDroPS to analyze the data load
typical of Big Data scenarios
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
36. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
First step: Mine 1-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
37. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
Second step: Mine 2-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
38. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
Third step: Mine k-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
39. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
Experiments
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
40. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
Pre
41. x Extension Experiments
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
42. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Testing SuNDroPS in the Green Move Scenario
PerLa context-aware sensors
SemTRex Pervasive and context-aware information push
Context-aware vehicle assignment to user reservation, based on
contextual user preferences
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
43. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Conclusions
The SuNDroPS system helps users to deal with the high information load
that surronds them
Context inference based on the environmental sensor data
ows
Historical data mining using parallel MapReduce algorithms to speed
up processing
Semantic-enhanced complex event processing using cache to reduce
the performance degradation due to the disk bottle-neck
Reengineering of the PerLa middleware allowing its distribution on
the network components and the integration with other
context-oriented paradigms (e.g. Context Oriented Programming)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
44. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Future Works
Several enhancements are required
Complete implementation of Distributed PerLa (currently a
prototype)
Complete integration of Context Oriented Programming (COP) in
PerLa
Complete the implementation of caches in SemTRex (currently only
the basic and parametric caches are fully implemented)
Further testing on the whole system
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
45. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Thanks
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
46. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication I
1 A. G. Bianchessi, G. Cugola, S. Formentin, A Morzenti, C. Ongini,
E. Panigati, M. Rossi, S. Savaresi, F. Schreiber, L. Tanca, and
E. Vannutelli Depoli
Green move: A platform for highly con
47. gurable, heterogeneous
electric vehicle sharing
Intelligent Transportation Systems Magazine, IEEE, 6(3):96{108,
Fall 2014
2 E. Panigati
Personalized management of semantic, dynamic data in pervasive
systems: Context-addict revisited
In Proc. of the 2014 International Conference on High Performance
Computing Simulation (HPCS 2014), 323-326, 2014
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
48. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication II
3 F. A. Schreiber, E. Panigati
Context-aware software approaches: a comparison and an
integration proposal
In Proc. of the 22nd Italian Symposium on Advanced database
Systems (SEBD), pages 175{184, 2014
4 A. G. Bianchessi, C. Ongini, G. Alli, E. Panigati, S. Savaresi
Vehicle-sharing: Technological infrastructure, vehicles, and
user-side devices - technological review
In Proc. of the 16th International IEEE Conference on Intelligent
Transportation Systems - (ITSC), 2013, pages 1599{1604, Oct 2013
5 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Pervasive data management in the green move system: a progress
report*
In Proc. of the 21st Italian Symposium on Advanced Database
Systems (SEBD), pages 279{288, 2013
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
49. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication III
6 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Aspects of pervasive information management: an account of the
Green Move system
In Proc. of the 10th IEEE/IFIP International Conference on
Embedded and Ubiquitous Computing, Paphos, Cyprus, Dec 2012
7 G. Alli, L. Baresi, A. G. Bianchessi, G. Cugola, A. Margara,
A. Morzenti, C. Ongini, E. Panigati , M. Rossi, S. Rotondi,
S. Savaresi, F. A. Schreiber, A. Sivieri, L. Tanca, E. Vannutelli
Depoli.
Green Move: towards next generation sustainable
smartphone-based vehicle sharing
In Proc. of SustainIT2012, Oct 2012
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
50. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication IV
8 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Context-aware information management in the Green Move system
{ extended abstract
In Proc. of the 5th Interop-VLab Workshop, co-Located with ItAIS
2012, Rome, Sept 2012
9 E. Panigati, F. A. Schreiber, C. Zaniolo
Data Streams and Data Stream Management Systems
Submitted for publication in Data Management in Pervasive
Systems - The Shapes and Dynamics of Information in a Pervasive
World Book, Springer
10 F. A. Schreiber, E. Panigati
Context aware data management and context oriented
programming: is convergence possible?
Technical Report 2014.7 (DEIB)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
51. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication V
11 E. Panigati
Methods for Supporting Critical Systems' Failure Diagnosis in the
Railway Scenario
Technical Report 2013.12 (DEIB)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System
52. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
SemTRex Query Example
Send a GM dynamic app to the Green eBox
DEFINE SendApp (String: greenBox id, String:
appUrl, String:class)
FROM VehicleStatus(greenbox id=$a) and ($url,
$class) IN
(SELECT ?url ?class FROM appKB.rdf WHERE f ?a
prop:hasName foo. ?a prop:downloadFromURL ?url.
?a prop:mainClass ?classg)
WHERE SendApp.greenBox id=VehicleStatus.greenBox id
and SendApp.appUrl = $url and SendApp.class= $class
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System