Innovative Data Exploration Laboratory (InDeX Lab) is an academic research group directed by Dr. Abolfazl Asudeh, at the Computer Science department of the University of Illinois at Chicago. At InDeX Lab, we study different aspects of Big Data and Data Science, including data management, data analytics, and data mining, for which we aim to find efficient, accurate, and scalable algorithmic solutions.

  • 2021:
    - We published Four full papers (two in SIGMOD'21, one in VLBD'21, and one in ICDE'21), one invited paper in Data Engineering Bulletin, and one workshop paper (in SIGMOD-DEEM'21), in collaboration with the DBGroup@UM, ChuDataLab@GaTech, University of Rochester, Politecnico di Torino, and Google Research (Structured Data Group).
    - We received Google's Research Scholar award for our work on Cherry-picked Trendlines.
    - Communications of the ACM featured our work on "Signal Reconstruction at Scale" as its Research Highlight in February 2021, 64.2: 106-115.
    - ACM SIGMOD Blog featured our article "Enabling Responsible Data Science in Practice", Jan. 2021.
  • 2020:
    - We published 5 papers in VLBD 2020 (three full reseach papers, one tutorial, and one demo), 1 demo paper in SIGMOD 2020, and 1 invited paper in VLBDJ (Special Issue on Best of VLDB'18), in collaboration with the DBGroup@UM and DBXLAB@UTA.
  • 2019:
    - We published 3 papers in SIGMOD 2019 (two full reseach papers and one demo), 2 papers in VLDB 2019 (one full research paper and one demo), 1 full research paper in ICDE 2019, 1 in PAKDD 2019, 1 demo paper in CIKM 2019, and 1 invited paper in Data Engineering Bulletin.
    - We received the ACM SIGMOD Research Highlight Award 2019.
  • A big Thank you to Google for supporting our work on Cherry-picked Trendlines with the Research Scholar award!
  • Congratulations to Nima Shahbazi on his SIGMOD 2021 paper, “Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes”.[paper][slides][video]
  • Congratulations to Matteo Corain on his ICDE 2021 paper, “DBSCOUT: A density-based method for scalable outlier detection in very large datasets”. This paper is the outcome of his joint MS thesis with Politecnico di Torino (Italy), co-adviced by Dr. Paolo Garza.