Combining dependent annotations for relational algebra

Kostylev, Egor V.; Buneman, Peter

doi:10.1145/2274576.2274597

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…Unique technical challenges in our settings include accounting for the workflow in addition to data (including treatment of possibly infinitely many executions) in conjunction with the combination of different kinds of choices (external and data dependent). The work of [31] has studied combination of annotations, but especially in the context of dependency between them and only for the relational algebra.…”

Section: Related Workmentioning

confidence: 99%

Provenance-based analysis of data-centric processes

2015

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We consider in this paper static analysis of the possible executions of data-dependent applications, namely applications whose control flow is guided by a finite-state machine, as well as by the state of an underlying database. We note that previous work in this context has not addressed two important features of such analysis, namely analysis under hypothetical scenarios, such as changes to the application's state machine and/or to the underlying database, and the consideration of meta-data, such as cost or access privileges. Observing that semiring-based provenance has been proven highly effective in supporting these two features for database queries, we develop in this paper a semiring-based provenance framework for the analysis of data-dependent processes, accounting for hypothetical reasoning and meta-data. The development addresses two interacting new challenges: (1) combining provenance annotations for both information that resides in the database and information about external inputs (e.g., user choices) and (2) finitely capturing infinitely many process executions. We have implemented our framework as part of the PROPOLIS system.

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Section: Related Workmentioning

confidence: 99%

Provenance-based analysis of data-centric processes

2015

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“…Unique technical challenges in our settings include accounting for the workflow in addition to data (including treatment of possibly infinitely many executions) in conjunction with the combination of different kinds of choices (external and data-dependent). The work of [28] has studied combination of annotations, but especially in the context of dependency between them and only for the relational algebra; in particular the issues that led us to consider tensor products are not addressed.…”

Section: Related Workmentioning

confidence: 99%

A provenance framework for data-dependent process analysis

2014

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A data-dependent process (DDP) models an application whose control flow is guided by a finite state machine, as well as by the state of an underlying database. DDPs are commonly found e.g., in e-commerce. In this paper we develop a framework supporting the use of provenance in static (temporal) analysis of possible DDP executions. Using provenance support, analysts can interactively test and explore the effect of hypothetical modifications to a DDP's state machine and/or to the underlying database. They can also extend the analysis to incorporate the propagation of annotations from meta-domains of interest, e.g., cost or access privileges.Toward this goal we note that the framework of semiringbased provenance was proven highly effective in fulfilling similar needs in the context of database queries. In this paper we consider novel constructions that generalize the semiring approach to the context of DDP analysis. These constructions address two interacting new challenges: (1) to combine provenance annotations for both information that resides in the database and information about external inputs (e.g., user choices), and (2) to finitely capture infinite process executions. We analyze our solution from theoretical and experimental perspectives, proving its effectiveness.

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“…The crucial idea is that annotations are distinguished from data not statically, as is the case in the relational semiring models [16,20], but dynamically, depending on the query under consideration and, moreover, the particular embedding from the query into the instance. In other words, the image of the body of the query under the embedding defines the data that is queried, and everything "above" the image is considered as annotations to be propagated automatically.…”

Section: Propagating Of Annotations Through Term Conjunctive Queriesmentioning

confidence: 99%

“…A related, and somewhat more complicated formalism has been developed for RDF/S [23]. The observation that two or more annotations could interact was made in [20], but it still makes a two-level distinction between data and annotation.…”

Section: Related Workmentioning

confidence: 99%

“…And suppose further that comment annotations were themselves annotated with valid time (which is not unreasonable), how would we represent and propagate such a hierarchy? An attempt to deal with such situations was made in [20], where combined annotations were considered. However, this model suffers from the same problems as other existing models of annotations-the distinction between data and annotations is given beforehand and all the pieces of data should have an annotation from the same fixed combination of domains.…”

Section: Introductionmentioning

confidence: 99%

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Annotations are relative

Buneman

Kostylev

Vansummeren

2013

Proceedings of the 16th International Conference on Database Theory

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Most systems that have been developed for annotation of data assume a two-level structure in which annotation is superimposed on, and separate from, the data. However there are many cases in which an annotation may itself be annotated. For example threads in e-mail and newsgroups allow the imposition of one comment on another; belief annotations can be compounded; and valid time, regarded as an annotation can be freely mixed with belief annotations (at time t1, B1 believed that at time t2, B2 believed that . . . ).In this paper we describe a hierarchical model of annotation in which there is no absolute distinction between annotation and data. First, we introduce a term model for annotations and, in order to express the fact that an annotation may apply to two or more data values with some shared structure, we provide a simple schema for annotation hierarchies. We then look at how queries can be applied to such hierarchies; in particular we ask the usual question of how annotations should propagate through queries. We take the view that the query together with schema describes a level in the hierarchy: everything below this level is treated as data to which the query should be applied; everything above it is annotation which should, according to certain rules, be propagated with the query. We also examine the representation of annotation hierarchies in conventional relational structures and describe a technique for annotating datalog programs.

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Combining dependent annotations for relational algebra

Cited by 13 publications

References 18 publications

Provenance-based analysis of data-centric processes

Provenance-based analysis of data-centric processes

A provenance framework for data-dependent process analysis

Annotations are relative

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