Enabling Change Exploration

Bleifuß, Tobias; Johnson, Theodore; Kalashnikov, Dmitri V.; Naumann, Felix; Shkapenyuk, Vladislav; Srivastava, Divesh

doi:10.1145/3077331.3077340

Cited by 1 publication

(1 citation statement)

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“…We define the problem of data change exploration as follows: For a given, dynamic dataset, efficiently identify, quantify, and summarize changes in data at (1) value-, (2) aggregate-, and (3) schema-level, and support users to effectively and interactively explore this change, as laid out in our short previous work [11]. Beyond the actual changed values, this problem definition includes such broad issues as (i) the fact that change occurred, (ii) the speed and frequency at which this change occurred, (iii) the source of the change, (iv) other values that experience similar change (in value) or correlated change (in time), or (v) reviewing the "history" of an entity.…”

Section: Change In Data and Metadatamentioning

confidence: 99%

Exploring change

et al. 2018

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Data and metadata in datasets experience many different kinds of change. Values are inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.

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Section: Change In Data and Metadatamentioning

confidence: 99%