704 research outputs found
On the Limitations of Provenance for Queries With Difference
The annotation of the results of database transformations was shown to be
very effective for various applications. Until recently, most works in this
context focused on positive query languages. The provenance semirings is a
particular approach that was proven effective for these languages, and it was
shown that when propagating provenance with semirings, the expected equivalence
axioms of the corresponding query languages are satisfied. There have been
several attempts to extend the framework to account for relational algebra
queries with difference. We show here that these suggestions fail to satisfy
some expected equivalence axioms (that in particular hold for queries on
"standard" set and bag databases). Interestingly, we show that this is not a
pitfall of these particular attempts, but rather every such attempt is bound to
fail in satisfying these axioms, for some semirings. Finally, we show
particular semirings for which an extension for supporting difference is
(im)possible.Comment: TAPP 201
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Provenance Semirings
We show that relational algebra calculations for incomplete databases, probabilistic databases, bag semantics and why provenance are particular cases of the same general algorithms involving semirings. This further suggests a comprehensive provenance representation that uses semirings of polynomials. We extend these considerations to datalog and semirings of formal power series. We give algorithms for datalog provenance calculation as well as datalog evaluation for incomplete and probabilistic databases. Finally, we show that for some semirings containment of conjunctive queries is the same as for standard set semantics
Provenance for Aggregate Queries
We study in this paper provenance information for queries with aggregation.
Provenance information was studied in the context of various query languages
that do not allow for aggregation, and recent work has suggested to capture
provenance by annotating the different database tuples with elements of a
commutative semiring and propagating the annotations through query evaluation.
We show that aggregate queries pose novel challenges rendering this approach
inapplicable. Consequently, we propose a new approach, where we annotate with
provenance information not just tuples but also the individual values within
tuples, using provenance to describe the values computation. We realize this
approach in a concrete construction, first for "simple" queries where the
aggregation operator is the last one applied, and then for arbitrary (positive)
relational algebra queries with aggregation; the latter queries are shown to be
more challenging in this context. Finally, we use aggregation to encode queries
with difference, and study the semantics obtained for such queries on
provenance annotated databases
First-Order Provenance Games
We propose a new model of provenance, based on a game-theoretic approach to
query evaluation. First, we study games G in their own right, and ask how to
explain that a position x in G is won, lost, or drawn. The resulting notion of
game provenance is closely related to winning strategies, and excludes from
provenance all "bad moves", i.e., those which unnecessarily allow the opponent
to improve the outcome of a play. In this way, the value of a position is
determined by its game provenance. We then define provenance games by viewing
the evaluation of a first-order query as a game between two players who argue
whether a tuple is in the query answer. For RA+ queries, we show that game
provenance is equivalent to the most general semiring of provenance polynomials
N[X]. Variants of our game yield other known semirings. However, unlike
semiring provenance, game provenance also provides a "built-in" way to handle
negation and thus to answer why-not questions: In (provenance) games, the
reason why x is not won, is the same as why x is lost or drawn (the latter is
possible for games with draws). Since first-order provenance games are
draw-free, they yield a new provenance model that combines how- and why-not
provenance
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