Dataset search: a survey (1901.00735v1)

Abstract: Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

• The paper establishes dataset search as a distinct field by categorizing basic keyword-based and constructive data integration methods.
• It highlights limitations in metadata quality and standardization, emphasizing the need for robust evaluation frameworks.
• It urges the development of interactive interfaces and AI-driven indexing to enhance dataset discoverability and usability.

Overview of "Dataset search: a survey"

The paper "Dataset search: a survey" provides an extensive analysis of the emerging field of dataset search. It seeks to establish dataset search as an independent research area, identifying distinct challenges and unique features when compared to traditional search paradigms in information retrieval (IR) and databases.

Survey of Dataset Search

The authors categorize dataset search into two main types: basic dataset search, where users attempt to find existing datasets, and constructive dataset search, where users assemble datasets for specific needs from diverse sources. For basic dataset search, the paper highlights prevalent methods including keyword-based searches over metadata, conventional in many scientific and commercial repositories. The paper notes the limitations inherent in metadata-based searches, particularly the restricted ability to assess the suitability of datasets for specific tasks due to insufficient metadata coverage on attributes that may indicate dataset quality, granularity, or provenance.

Constructive dataset search, on the other hand, focuses on dynamic processes enabling users to construct datasets by merging or synthesizing data from heterogeneous sources. This requires robust mechanisms for data integration and interoperability, concepts which borrow heavily from the domain of databases. Here, the paper emphasizes the relevance of data marketplaces and open data ecosystems that facilitate such integrative search functionalities.

Technical Foundation

The paper draws parallels between dataset search and existing verticals such as entity-centric search, tabular search, and database queries. Metadata schema compliance, data quality, and provenance are recurrent themes across these domains that can be enhanced in dataset search applications. The authors acknowledge ongoing efforts in metadata standardization (DCAT, schema.org) and the development of summary and annotation techniques that enrich dataset understanding and discoverability.

Open Problems and Future Challenges

While the paper provides a comprehensive survey of the state-of-the-art in dataset search, it also outlines several open problems. These include the development of sophisticated query languages that extend beyond keyword searches, improved metadata and annotation methods for richer dataset evaluation, and advanced ranking algorithms adapted to the multidimensional nature of datasets. Additionally, the importance of facilitating differentiated access to datasets with varying security, privacy, and licensing considerations is highlighted as a critical need for future research, particularly in sensitive domains like healthcare.

The authors argue that current dataset search systems need to evolve to support more interactive interfaces, allowing users to explore and integrate datasets seamlessly, fostering a more intuitive discovery process. Furthermore, they call for benchmarking and standardized evaluation metrics tailored to dataset search to enable consistent assessment and tracking of technological progress.

Implications and Conclusion

The implications of this paper are significant for the development of data-intensive applications across scientific, commercial, and public sectors. By categorizing and structuring the field of dataset search, the paper sets a foundation for more targeted research endeavors and technological advancements. It encourages the exploration of interdisciplinary methodologies that contribute to more efficient and reliable dataset retrieval systems.

Looking forward, the paper invites researchers to broaden their focus to include mechanisms that leverage AI and machine learning for dataset indexing, understanding, and recommendation, a pursuit that promises substantial innovation in the domain of dataset search.

In conclusion, "Dataset search: a survey" serves as an essential reference point for researchers and technologists aiming to understand and address the multifaceted challenges of dataset search. The paper’s insights into current practices and its articulation of future directions are poised to drive substantial advancements in how datasets are discovered, integrated, and utilized.