Modeling Data Analytics Architecture for Smart Cities Data-Driven Applications using DAT

• URL: http://arxiv.org/abs/2307.08870v2
• Date: Sun, 23 Jul 2023 20:59:35 GMT
• Title: Modeling Data Analytics Architecture for Smart Cities Data-Driven Applications using DAT
• Authors: Moamin Abughazala, Henry Muccini
• Abstract summary: This article shares our experiences in developing a Data Analytics Architecture (DAA) using model-driven engineering for Data-Driven Smart Cities applications utilizing DAT. DAA uses model-driven engineering for Data-Driven Smart Cities applications utilizing DAT.
• Score: 1.8945921149936187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting valuable insights from vast amounts of information is a critical process that involves acquiring, storing, managing, analyzing, and visualizing data. Providing an abstract overview of data analytics applications is crucial to ensure that collected data is transformed into meaningful information. One effective way of achieving this objective is through Data Architecture. This article shares our experiences in developing a Data Analytics Architecture (DAA) using model-driven engineering for Data-Driven Smart Cities applications utilizing DAT.

Related papers

• Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning [3.623224034411137]
  offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though the field is by definition data-driven, efforts have thus far neglected data in their drive to achieve state-of-the-art results. We show how the majority of works generate their own datasets without consistent methodology and provide sparse information about the characteristics of these datasets.
  arXiv  Detail & Related papers  (2024-09-18T14:13:24Z)
• DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
  We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
  arXiv  Detail & Related papers  (2024-09-12T02:08:00Z)
• Dataset Regeneration for Sequential Recommendation [69.93516846106701]
  We propose a data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets.
  arXiv  Detail & Related papers  (2024-05-28T03:45:34Z)
• Architecting Data-Intensive Applications : From Data Architecture Design to Its Quality Assurance [0.0]
  Data Architecture is crucial in describing, collecting, storing, processing, and analyzing data to meet business needs. We have evaluated the DAT on more than five cases within various industry domains, demonstrating its exceptional adaptability and effectiveness.
  arXiv  Detail & Related papers  (2024-01-22T14:58:54Z)
• Data Architecture for Digital Object Space Management Service (DOSM) using DAT [1.8945921149936187]
  This work focuses on describing the movement of data, data formats, data location, data processing (batch or real-time), data storage technologies, and main operations on the data. Data architecture is a complex task that involves describing the flow of data from its source to its destination.
  arXiv  Detail & Related papers  (2023-06-22T14:22:56Z)
• DAT: Data Architecture Modeling Tool for Data-Driven Applications [1.6037279419318131]
  Data Architecture (DA) focuses on describing, collecting, storing, processing, and analyzing the data to meet business needs. We present the DAT, a model-driven engineering tool enabling data architects, data engineers, and other stakeholders to describe how data flows through the system.
  arXiv  Detail & Related papers  (2023-06-21T11:24:59Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System [48.62158108517576]
  We introduce InsightPilot, an automated data exploration system designed to simplify the data exploration process. InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining. In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts.
  arXiv  Detail & Related papers  (2023-04-02T07:27:49Z)
• A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck [68.61583160269664]
  Event argument extraction (EAE) aims to extract arguments with given roles from texts. We propose a multi-format transfer learning model with variational information bottleneck. We conduct extensive experiments on three benchmark datasets, and obtain new state-of-the-art performance on EAE.
  arXiv  Detail & Related papers  (2022-08-27T13:52:01Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.