32nd IEEE International Conference on Data Engineering

May 16-20, 2016 · Helsinki, Finland


It is with great pleasure that the organizers of the 32nd IEEE International Conference on Data Engineering invite you to take part in ICDE 2016 to be held in Helsinki, Finland, from May 16 to 20, 2016. As the capital of Finland, Helsinki is a vibrant city on the Baltic Sea renowned for its design and friendly atmosphere. The venue is at Aalto University's School of Business, which is located right in the city center.
  • Helsinki_City
  • Finlandia_Hall
  • Venue
Photos now available in Photo Gallery under 'General Information'.

Dark Data: Are We Solving the Right Problems?

  • Moderator:
    • Tim Kraska (Brown University, USA)
  • Date: Tuesday May 17, 2016.
  • Panel Description: With the increasing urge of the enterprises to ingest as much data as they can in what's commonly referred to as “Data Lakes”, the new environment presents serious challenges to traditional ETL models and to building analytic layers on top of well-understood global schema. With the recent development of multiple technologies to support this “load-first” paradigm, even traditional enterprises have fairly large HDFS-based data lakes now. They have even had them long enough that their first generation IT projects delivered on some, but not all, of the promise of integrating their enterprise's data assets. In short, we moved from no data to Dark data. Dark data is what enterprises might have in their possession, without the ability to access it or with limited awareness of what this data represents. In particular, business-critical information might still remain out of reach.

    This panel is about Dark Data and whether we have been focusing on the right data management challenges in dealing with it. The list of panelists is:

    • Michael Cafarella (University of Michigan, USA)
    • Ihab F. Ilyas (University of Waterloo, Canada)
    • Marcel Kornacke (Cloudera, USA)
    • Christopher Ré (Stanford University, USA)

Big Data Quality — Whose problem is it?

  • Moderators:
    • Paolo Papotti (Arizona State University, USA)
    • Shazia Sadiq (The University of Queensland, Australia)
  • Date & Time: Thursday May 19, 2016, 11:00am – 12:30pm.
  • Panel Description: The increased reliance on data driven enterprise has seen an unprecedented investment in big data initiatives. As companies intensify their efforts to get value from big data, the growth in the amount of data being managed continues at an exponential rate, leaving organizations with a massive footprint of unexplored, unfamiliar datasets. On February 8th, 2015, a group of global thought leaders from the database research community outlined the grand challenges in getting value from big data [Stoyanovich and Suchanek, 2015]. The key message was the need to develop the capacity to "understand how the quality of data affects the quality of the insight we derive from it". At the same time, data quality discovery and repair techniques are highly contextual and their success depends on their fitness against both the data quality dimension (e.g. completeness, consistency, timeliness, accuracy, reliability) as well as the type of data (e.g. structured/relational, text, spatial, time series, social/graph, RDF/web). The techniques include those driven by logic such as data dependency constraints and integrity rules; those driven by numerical and statistical approaches; and several others such as probabilistic, learning and empirical methods.

    In this session, the panelists will debate on the fitness of techniques in diverse settings. The attendees of the panel can expect to get a broad understanding of key approaches on data quality and to get more familiar with emerging techniques and lessons learnt in different settings. Panelists include:

    • Felix Naumann (Hasso-Plattner-Institut, Germany)
    • Tamraparni Dasu (AT&T Labs Research, USA)
    • Juliana Freire (New York University Tandon School of Engineering, USA)
    • Ihab F. Ilyas (University of Waterloo, Canada)
    • Eric Simon (SAP, France)