Semantic Data Management in Data Lakes

• URL: http://arxiv.org/abs/2310.15373v1
• Date: Mon, 23 Oct 2023 21:16:50 GMT
• Title: Semantic Data Management in Data Lakes
• Authors: Sayed Hoseini, Johannes Theissen-Lipp, Christoph Quix
• Abstract summary: In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies.

Related papers

• Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models [79.65071553905021]
  We propose Data Advisor, a method for generating data that takes into account the characteristics of the desired dataset. Data Advisor monitors the status of the generated data, identifies weaknesses in the current dataset, and advises the next iteration of data generation.
  arXiv  Detail & Related papers  (2024-10-07T17:59:58Z)
• 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)
• OpenDataLab: Empowering General Artificial Intelligence with Open Datasets [53.22840149601411]
  This paper introduces OpenDataLab, a platform designed to bridge the gap between diverse data sources and the need for unified data processing. OpenDataLab integrates a wide range of open-source AI datasets and enhances data acquisition efficiency through intelligent querying and high-speed downloading services. We anticipate that OpenDataLab will significantly boost artificial general intelligence (AGI) research and facilitate advancements in related AI fields.
  arXiv  Detail & Related papers  (2024-06-04T10:42:01Z)
• Empowering Data Mesh with Federated Learning [5.087058648342379]
  New paradigm, Data Mesh, treats domains as a first-class concern by distributing the data ownership from the central team to each data domain. Many multi-million dollar organizations like Paypal, Netflix, and Zalando have already transformed their data analysis pipelines based on this new architecture. We introduce a pioneering approach that incorporates Federated Learning into Data Mesh.
  arXiv  Detail & Related papers  (2024-03-26T17:10:15Z)
• Retrieve, Merge, Predict: Augmenting Tables with Data Lakes [7.449868392714658]
  We analyze alternative methods used in the three main steps: retrieving joinable tables, merging information, and predicting with the resultant table. As data lakes, the paper uses YADL (Yet Another Data Lake) and Open Data US, a well-referenced real data lake.
  arXiv  Detail & Related papers  (2024-02-09T09:48:38Z)
• A Comprehensive Survey of Dataset Distillation [73.15482472726555]
  It has become challenging to handle the unlimited growth of data with limited computing power. Deep learning technology has developed unprecedentedly in the last decade. This paper provides a holistic understanding of dataset distillation from multiple aspects.
  arXiv  Detail & Related papers  (2023-01-13T15:11:38Z)
• Federated Causal Discovery [74.37739054932733]
  This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD) It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
  arXiv  Detail & Related papers  (2021-12-07T08:04:12Z)
• A Big Data Lake for Multilevel Streaming Analytics [0.4640835690336652]
  This paper focuses on storing high volume, velocity and variety data in the raw formats in a data storage architecture called a data lake. We discuss and compare different open source and commercial platforms that can be used to develop a data lake. We present a real-world data lake development use case for data stream ingestion, staging, and multilevel streaming analytics.
  arXiv  Detail & Related papers  (2020-09-25T19:57:21Z)
• Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data [78.74367441804183]
  We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain. NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client. We show the effectiveness of NDS in various transfer learning scenarios, demonstrating state-of-the-art performance on several target datasets.
  arXiv  Detail & Related papers  (2020-01-09T01:21:30Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

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.