Provenance in Databases: Principles and Applications

Abstract

Data provenance is extra information computed during query evaluation over databases, which provides additional context about query results. Several formal frameworks for data provenance have been proposed, in particular based on provenance semirings. The provenance of a query can be computed in these frameworks for a variety of query languages. Provenance has applications in various settings, such as probabilistic databases, view maintenance, or explanation of query results. Though the theory of provenance semirings has mostly been developed in the setting of relational databases, it can also apply to other data representations, such as XML, graph, and triple-store databases.

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

“…They demonstrate the provenance analysis of SQL queries with how-provenance which determines which query expressions have been relevant for the evaluation of selected pieces of output data and why-provenance which determines relevant pieces of input data records [13]. Senellart et al discussed important applications of different forms of data provenance based on the provenance semirings such as the Boolean provenance for probabilistic databases [14]. In this paper, we construct the above-discussed data provenance techniques including bag semantics, how-provenance, why-provenance, Boolean expression, and provenance semiring into the proposed architecture for processing and analyzing the annotated data.…”

Big social data provenance framework for Zero-Information Loss Key-Value Pair (KVP) Database

“…The importance of social data provenance in social media is also presented in [21,36,46] with several key challenges such as measuring quality and truthiness of social data, provenance storage, provenance querying, etc. [7,9,14,15,23,49,53]. Several research works are carried out to identify the suitability of NoSQL database to manage big social data with efficient storage, fast querying, and horizontal scalability [20,33].…”

“…What provides the description about the social media posts, Who describes the correlations among social media users, and When characterizes the evolution of users’ behaviour over time. Like data provenance [ 24 , 49 ], social data provenance also describes the ownership and origin of such information.…”

Model Counting meets F0 Estimation

“…In this context, these newly obtained algorithms open up several new interesting directions of research ranging from the development of MaxSAT solvers with native XOR support to open problems in designing FPRAS schemes. (2) Given the central importance of #DNF (and its weighted variant) due to a recent surge of interest in scalable techniques for provenance in probabilistic databases [49,50], a natural question is whether one can design efficient techniques in the distributed setting. In this work, we initiate the study of distributed #DNF.…”