The Experts below are selected from a list of 11826 Experts worldwide ranked by ideXlab platform

Sandro Fiore - One of the best experts on this subject based on the ideXlab platform.

  • an integrated big and fast data Analytics Platform for smart urban transportation management
    IEEE Access, 2019
    Co-Authors: Sandro Fiore, Donatello Elia, Carlos Eduardo Santos Pires, Demetrio Gomes Mestre, Cinzia Cappiello, Monica Vitali, Nazareno Andrade, Tarciso Braz, Daniele Lezzi, Regina Moraes
    Abstract:

    Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from Platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA ( Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport Analytics application that has been developed on top of the EUBra-BIGSEA Platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data Analytics Platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.

  • Ophidia: A full software stack for scientific data Analytics
    2014 International Conference on High Performance Computing & Simulation (HPCS), 2014
    Co-Authors: Sandro Fiore, Donatello Elia, Alessandro D'anca, Cosimo Palazzo, Ian Foster, Dean Williams, Giovanni Aloisio
    Abstract:

    The Ophidia project aims to provide a big data Analytics Platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia software infrastructure is discussed in detail, presenting the entire software stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.

Regina Moraes - One of the best experts on this subject based on the ideXlab platform.

  • an integrated big and fast data Analytics Platform for smart urban transportation management
    IEEE Access, 2019
    Co-Authors: Sandro Fiore, Donatello Elia, Carlos Eduardo Santos Pires, Demetrio Gomes Mestre, Cinzia Cappiello, Monica Vitali, Nazareno Andrade, Tarciso Braz, Daniele Lezzi, Regina Moraes
    Abstract:

    Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from Platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA ( Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport Analytics application that has been developed on top of the EUBra-BIGSEA Platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data Analytics Platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.

Donatello Elia - One of the best experts on this subject based on the ideXlab platform.

  • an integrated big and fast data Analytics Platform for smart urban transportation management
    IEEE Access, 2019
    Co-Authors: Sandro Fiore, Donatello Elia, Carlos Eduardo Santos Pires, Demetrio Gomes Mestre, Cinzia Cappiello, Monica Vitali, Nazareno Andrade, Tarciso Braz, Daniele Lezzi, Regina Moraes
    Abstract:

    Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from Platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA ( Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport Analytics application that has been developed on top of the EUBra-BIGSEA Platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data Analytics Platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.

  • Ophidia: A full software stack for scientific data Analytics
    2014 International Conference on High Performance Computing & Simulation (HPCS), 2014
    Co-Authors: Sandro Fiore, Donatello Elia, Alessandro D'anca, Cosimo Palazzo, Ian Foster, Dean Williams, Giovanni Aloisio
    Abstract:

    The Ophidia project aims to provide a big data Analytics Platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia software infrastructure is discussed in detail, presenting the entire software stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.

Sarath Chandra Janga - One of the best experts on this subject based on the ideXlab platform.

  • sequoia an interactive visual Analytics Platform for interpretation and feature extraction from nanopore sequencing datasets
    BMC Genomics, 2021
    Co-Authors: Ratanond Koonchanok, Swapna Vidhur Daulatabad, Quoseena Mir, Khairi Reda, Sarath Chandra Janga
    Abstract:

    Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Here, we present Sequoia, a visual Analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia .

  • Sequoia: an interactive visual Analytics Platform for interpretation and feature extraction from nanopore sequencing datasets
    'Springer Science and Business Media LLC', 2021
    Co-Authors: Ratanond Koonchanok, Swapna Vidhur Daulatabad, Quoseena Mir, Khairi Reda, Sarath Chandra Janga
    Abstract:

    Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual Analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia

  • sequoia an interactive visual Analytics Platform for interpretation and feature extraction from nanopore sequencing datasets
    bioRxiv, 2019
    Co-Authors: Ratanond Koonchanok, Swapna Vidhur Daulatabad, Quoseena Mir, Khairi Reda, Sarath Chandra Janga
    Abstract:

    Abstract Sequoia is a visualization tool that allows biologists to explore characteristics of signals generated by the Oxford Nanopore Technologies (ONT) in detail. From Fast5 files generated by ONT, the tool displays relative similarities between signals using the dynamic time warping and the t-SNE algorithms. Raw signals can be visualized through mouse actions while particular signals of interest can also be exported as a CSV file for further analysis. Sequoia consists of two major components: the command-line back-end that performs necessary computations using Python and the front-end that displays the visualization through a web interface. Two datasets are used to conduct a case study in order to illustrate the usability of the tool.

Alfred Essa - One of the best experts on this subject based on the ideXlab platform.

  • learning Analytics Platform towards an open scalable streaming solution for education
    Educational Data Mining, 2015
    Co-Authors: Nicholas Lewkow, Neil L Zimmerman, Mark Riedesel, Alfred Essa
    Abstract:

    Next generation digital learning environments require delivering just-in-time feedback to learners and those who support them. Unlike traditional business intelligence environments, streaming data requires resilient infrastructure that can move data at scale from heterogeneous data sources, process the data quickly for use across several data pipelines, and serve the data to a variety of applications. As a solution to this problem, we have designed and deployed into production the Learning Analytics Platform (LAP), which can ingest data from different education systems using standardized IMS Caliper events. The education events are triggered by student and instructor activity within Caliper instrumented learning systems. Once sent to the LAP, events are transformed and stored in a data store where they can be used for student, educator, and administrator visualizations as well as education driven Analytics research. Two McGraw-Hill Education Platforms, Connect, used for higher education, and Engrade, for K-12, are currently instrumented to send the LAP event data which in turn feeds visualizations for educational insight. Future plans for the LAP include collection of education event data from a wide variety of proprietary and open source education Platforms, computational engines for predictive Analytics, and an open API for third-party Analytics using LAP data.