Leveraging Data Provenance Middleware for Large-Scale Applications
This 8-page document explores SPADE, an open-source data provenance middleware, and its adaptation for handling large datasets. It discusses challenges, techniques, and successful implementations in various domains, showcasing its effectiveness in handling large-scale provenance data.
What is Included In this Technical Paper:
- Core concepts and capabilities of provenance middleware.
- Ways in which SPADE enables individuals and applications to capture, store, and query records representing computational processes and data artifacts.
- Two case studies that highlight the actual applicability of SPADE in dealing with enormous provenance datasets.
- Collection/queue and integration challenges in managing provenance at scale, which are critical for building successful solutions.
- SPADE’s solution to these difficulties includes transformers, content-based integration, and storage screening, all of which improve provenance management and system performance.
This technical paper investigates the advantages of employing SPADE to handle large provenance datasets, offering insights and techniques for scholars, data scientists, and industry experts. The aim is to extend the data provenance middleware’s full capability for projects and applications.
