Auflistung nach Autor:in "Zumpano, Ester"

Gerade angezeigt 1 - 2 von 2
  • Vorschaubild nicht verfügbar
    Publikation
    Automatic indexing for MongoDB
    (Springer, 2023) Espona, Lucía; Vichalkovski, Anton; Steingartner, William; Pustulka, Elzbieta; Abelló, Alberto; Vassiliadis, Panos; Romero, Oscar; Wrembel, Robert; Bugiotti, Francesca; Gamper, Johann; Vargas Solar, Genoveva; Zumpano, Ester [in: New trends in database and information systems. ADBIS 2023 short Papers, doctoral consortium and workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, roceedings]
    We present a new method for automated index suggestion for MongoDB, based solely on the queries (called aggregation pipelines), without requiring data or usage information. The solution handles complex aggregations and is suitable for both cloud and standalone databases. We validated the algorithm on TPC-H and showed that all suggested indexes were used. We report on the performance and provide hints for further development of an automated method of index selection. Our algorithm is, to the best of our knowledge, the first query-based solution for automated indexing in MongoDB.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Document versioning for MongoDB
    (Springer, 2022) Espona, Lucía; Pustulka, Elzbieta; Chiusano, Silvia; Cerquitelli, Tania; Wrembel, Robert; Nørvåg, Kjetil; Catania, Barbara; Vargas-Solar, Genoveva; Zumpano, Ester [in: New trends in database and information systems. ADBIS 2022 Short Papers, Doctoral Consortium and Workshops: DOING, K-GALS, MADEISD, MegaData, SWODCH, Turin, Italy, September 5–8, 2022, Proceedings]
    Data versioning is required in various business and science contexts, including governance, risk and compliance (GRC) and is essential for security audits, legal compliance and business strategy development. We present a data versioning library for MongoDB to support an innovative enterprise resource planning (ERP) system for small and medium enterprises (SMEs) which aims to be flexible and adapt to changing business needs. We exploit the fact that the volume of archival data is orders of magnitude larger than of the currently valid documents and that historic data is rarely accessed. Experiments with eight sets of 1 million mutations/queries on 100K of valid documents (average size 2.3 kB), carried out over a period of 60 h on a local PC show stable average versioning write/read operation performance per document in the range of 12.3/1.2 ms which proves that the solution is viable in an SME scenario.
    04B - Beitrag Konferenzschrift