Causality and explanations in databases

Meliou, Alexandra; Roy, Sudeepa; Suciu, Dan

doi:10.14778/2733004.2733070

Cited by 75 publications

(62 citation statements)

References 4 publications

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“…Another feature that is badly needed in database exploration is the capability to provide explanations [37] and causal dependencies, whose aim is basically to understand the reasons for query results. Imagine that the user wants to understand "why there is a huge difference between Valtellina and the rest of Lombardia": By joining the original dataset with one containing data about the Italian regions an explanation system might discover that Valtellina is very poor of iodine.…”

Section: A Database Perspectivementioning

confidence: 99%

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

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The Big Data challenge has made the issue of "making sense" of data urgent and unavoidable. This paper introduces exploratory computing (EC), a novel paradigm whose aim is to support a comprehensive "exploratory" experience for the user. "Exploratory" because it supports search and discovery of information through various tasks (investigation, knowledge seeking, serendipitous discovery, comparison of information…) in a dynamic interaction, where meaningful feedbacks from the system play a crucial role, closely resembling a human-to-human dialogue. "Computing" because a complex interaction as the one outlined above requires powerful computational strength for the user to be able to fully profit from, and even enjoy, the interaction. EC is not associated with a predefined set of techniques: Rather, it is an approach that can be concretized in different ways. In the paper, two different implementations of the EC approach are presented, both of which interpret the EC highlevel requirements. It is the authors' hope that others will follow.

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Section: A Database Perspectivementioning

confidence: 99%

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

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“…Determining which part of a database is relevant for answering a query is a problem that arises in different contexts. For instance, causality in databases aims to determine which tuples in the database instance caused the output to a query [16,17]. Then, the contingency set asks for the smallest set K such that Q(I) = Q(I − K).…”

Section: Related Workmentioning

confidence: 99%

Optimal Broadcasting Strategies for Conjunctive Queries over Distributed Data

Ketsman

Neven

2016

Theory Comput Syst

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In a distributed context where data is dispersed over many computing nodes, monotone queries can be evaluated in an eventually consistent and coordination-free manner through a simple but naive broadcasting strategy which makes all data available on every computing node. In this paper, we investigate more economical broadcasting strategies for full conjunctive queries without self-joins that only transmit a part of the local data necessary to evaluate the query at hand. We consider oblivious broadcasting strategies which determine which local facts to broadcast independent of the data at other computing nodes. We introduce the notion of broadcast dependency set (BDS) as a sound and complete formalism to represent locally optimal oblivious broadcasting functions. We provide algorithms to construct a BDS for a given conjunctive query and study the complexity of various decision problems related to these algorithms. ACM Subject Classification H.2.3 Query Languages, H.2.4 Distributed databases Keywords and phrases IntroductionWe assume the setting introduced in the context of declarative networking [6,14], where queries are specified on a logical level over a global schema and are evaluated by multiple computing nodes over which the input database is distributed. These nodes can perform local computations and communicate asynchronously with each other via messages. The model then operates under the assumption that messages can never be lost but can be arbitrarily delayed. It is known that every monotone query can be evaluated in an eventually consistent and coordination-free manner through a naive broadcasting strategy that makes all data available to all nodes [14].

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“…Scorpion is most closely related to notions of influence introduced in the context of data provenance [10,12,7] that define how the probability of a result tuple is influenced by an input tuple. For example, Meliou et al [11] define influence in the context of boolean expressions, where an input tuple's influence over a result tuple is relative to the minimum number of additional tuples (a contingency set) that must also be added to or removed from the database to toggle the result tuple's existence.…”

Section: Related Workmentioning

confidence: 99%

Scorpion

Wu¹,

Madden²

2013

Proc. VLDB Endow.

174

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Database users commonly explore large data sets by running aggregate queries that project the data down to a smaller number of points and dimensions, and visualizing the results. Often, such visualizations will reveal outliers that correspond to errors or surprising features of the input data set. Unfortunately, databases and visualization systems do not provide a way to work backwards from an outlier point to the common properties of the (possibly many) unaggregated input tuples that correspond to that outlier. We propose Scorpion, a system that takes a set of user-specified outlier points in an aggregate query result as input and finds predicates that explain the outliers in terms of properties of the input tuples that are used to compute the selected outlier results. Specifically, this explanation identifies predicates that, when applied to the input data, cause the outliers to disappear from the output. To find such predicates, we develop a notion of influence of a predicate on a given output, and design several algorithms that efficiently search for maximum influence predicates over the input data. We show that these algorithms can quickly find outliers in two real data sets (from a sensor deployment and a campaign finance data set), and run orders of magnitude faster than a naive search algorithm while providing comparable quality on a synthetic data set.

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Causality and explanations in databases

Cited by 75 publications

References 4 publications

Exploratory computing: a comprehensive approach to data sensemaking

Exploratory computing: a comprehensive approach to data sensemaking

Optimal Broadcasting Strategies for Conjunctive Queries over Distributed Data

Scorpion

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