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
- 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) - Capture the Flag: Uncovering Data Insights with Large Language Models [90.47038584812925]
This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data.
We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset.
arXiv Detail & Related papers (2023-12-21T14:20:06Z) - Lightweight Knowledge Representations for Automating Data Analysis [33.094930396228676]
We take the first steps towards automating a key aspect of the data science pipeline: data analysis.
We present an taxonomy of data analytic operations that scopes analytics across domains and data, as well as a method for codifying domain-specific knowledge that links this taxonomy to actual data.
In this way, we produce information spaces over data that enable complex analyses and search over this data scopes and pave the way for fully automated data analysis.
arXiv Detail & Related papers (2023-10-15T06:44:45Z) - Serving Deep Learning Model in Relational Databases [72.72372281808694]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-artDL-Centric architecture offloadsDL computations to dedicated DL frameworks.
The potential UDF-Centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the database system.
The potentialRelation-Centric architecture aims to represent a large-scale tensor computation through operators.
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - 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.