=Paper=
{{Paper
|id=Vol-2646/m-invited
|storemode=property
|title=Getting Rid of Data (Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2646/m-invited.pdf
|volume=Vol-2646
|authors=Tova Milo
|dblpUrl=https://dblp.org/rec/conf/sebd/Milo20
}}
==Getting Rid of Data (Abstract)==
<pdf width="1500px">https://ceur-ws.org/Vol-2646/m-invited.pdf</pdf>
<pre>
                      Getting Rid of Data

                                   Tova Milo

                              Tel Aviv University
                          School of Computer Science
                                Tel Aviv, Israel
                          Email: milo@cs.tau.ac.il



    Abstract. We are experiencing an amazing data-centered revolution.
    Incredible amounts of data are collected, integrated and analyzed, lead-
    ing to key breakthroughs in science and society. This well of knowledge,
    however, is at a great risk if we do not dispense with some of the data
    flood. First, the amount of generated data grows exponentially and al-
    ready at 2025 is expected to be more than five times the available storage.
    Second, even disregarding storage constraints, uncontrolled data reten-
    tion risks privacy and security, as recognized, e.g., by the recent EU Data
    Protection reform. Data disposal policies must be developed to bene-
    fit and protect organizations and individuals. Retaining the knowledge
    hidden in the data while respecting storage, processing and regulatory
    constraints is a great challenge. The difficulty stems from the distinct,
    intricate requirements entailed by each type of constraint, the scale and
    velocity of data and the constantly evolving needs. While multiple data
    sketching, summarization and deletion techniques were developed to ad-
    dress specific aspects of the problem, we are still very far from a com-
    prehensive solution. Every organization has to battle the same tough
    challenges, with ad hoc solutions that are application specific and rarely
    sharable. In this talk I will discuss the logical, algorithmic, and method-
    ological foundations required for the systematic disposal of large-scale
    data, for constraints enforcement and for the development of applica-
    tions over the retained information. I will overview relevant related work,
    highlighting new research challenges and potential reuse of existing tech-
    niques, as well as the research performed in this direction in the Tel Aviv
    Databases group.




Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). This volume is published
and copyrighted by its editors. SEBD 2020, June 21-24, 2020, Villasimius, Italy.

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