Fast and Simple Relational Processing of Uncertain Data

Proceedings of the 2021 International Conference on Management of Data, 2021

Incomplete and probabilistic database techniques are principled methods for coping with uncertainty in data. Unfortunately, the class of queries that can be answered eciently over such databases is severely limited, even when advanced approximation techniques are employed. We introduce attribute-annotated uncertain databases (AU-DBs), an uncertain data model that annotates tuples and attribute values with bounds to compactly approximate an incomplete database. AU-DBs are closed under relational algebra with aggregation using an ecient evaluation semantics. Using optimizations that trade accuracy for performance, our approach scales to complex queries and large datasets, and produces accurate results.

2008

The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efficient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally.

2007

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty makes traditional techniques inapplicable. We introduce new probabilistic formulations for top-k queries. Our formulations are based on "marriage" of traditional top-k semantics and possible worlds semantics. In the light of these formulations, we construct a framework that encapsulates a state space model and efficient query processing techniques to tackle the challenges of uncertain data settings. We prove that our techniques are optimal in terms of the number of accessed tuples and materialized search states. Our experiments show the efficiency of our techniques under different data distributions with orders of magnitude improvement over naïve materialization of possible worlds.

2009

Abstract The ability to flexibly compose confidence computation with the operations of relational algebra is an important feature of probabilistic database query languages. Computing confidences is computationally hard, however, and has to be approximated in practice.

1994

As models of the real world, databases are often permeated with forms of uncertainty, including imprecision, incompleteness, vagueness, inconsis- tency, and ambiguity. This chapter addresses issues of database uncer- tainty. It defines basic terminology, and it classifies the various kinds of uncertainty. It then surveys solutions that have been attempted, and it speculates on the reasons that have hindered the development of general- purpose database systems with powerful uncertainty capabilities. Finally, it describes challenging new applications that will require such capabilities, and it points to promising directions for research.

We propose an extension of possibilistic databases that also includes provenance. The introduction of provenance makes our model closed under selection with equalities, projection and join. In addition the computation of query computing with possibilities is polynomial, in contrast with current models that combine provenance with probabilities and have #P complexity.

Computer and Information Science, 2015

In the last years, uncertainty management became an important aspect as the presence of uncertain data increased rapidly. Due to the several advanced technologies that have been developed to record large quantity of data continuously, resulting is a data that contain errors or may be partially complete. Instead of dealing with data uncertainty by removing it, we must deal with it as a source of information. To deal with this data, database management system should have special features to handle uncertain data. The aim of this paper is twofold: on one hand, to introduce some main concepts of uncertainty in database by focusing on different data management issues in uncertain databases such as join and query processing, database integration, indexing uncertain data, security and information leakage and representation formalisms. On the other hand, to provide a survey of the current database management systems dealing with uncertain data, presenting their features and comparing them.

2007

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between query scores and data uncertainty makes traditional techniques inapplicable. We introduce URank, a system that processes new probabilistic formulations of top-k queries in uncertain databases. The new formulations are based on marriage of traditional top-k semantics with possible worlds semantics. URank encapsulates a new processing framework that leverages existing query processing capabilities, and implements efficient search strategies that integrate ranking on scores with ranking on probabilities, to obtain meaningful answers for top-k queries.

bvicam.ac.in

Databases today are deterministic, that is, an item is either in the database or not. Similarly, a tuple is either in the query result or not. This process of mapping the real world inherently includes ambiguities and uncertainties and is seldom perfect. In today's data-driven competitive world a wide range of applications have emerged that needs to handle very large, imprecise data sets with inherent uncertainties. Uncertain data is natural in many important real world applications like environmental surveillance, market analysis and quantitative economic research. Data uncertainty innate in these important real world applications is generally the result of factors like data randomness and incompleteness, misaligned schemas, limitations of measuring equipment, delayed data update, imprecise queries etc . Due to the importance of these applications and the rapidly increasing amount of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task and has attracted more and more interest from the database community. Probabilistic Databases hold the promise of being a viable means for large-scale uncertainty management, increasingly being required in a large number of real world application domains . A probabilistic database is an uncertain database in which the possible worlds have associated probabilities, that is, an item belongs to the database is a probabilistic event either with tuple-existence uncertainty or with attribute-value uncertainty. However, a tuple as an answer to query is again a probabilistic event. An important aspect in tackling the research and development on uncertain data processing is the query answering techniques on uncertain and probabilistic data. Query processing in probabilistic databases remains a computational challenge as it is fundamentally more complex than other data models. There exists a rich collection of powerful, non-trivial techniques and results, some old, some very recent, that could lead to practical management techniques for probabilistic databases. However, all such techniques suffer from limitations of uncertainty inherent in result of the query. Hence, there is a need for a general probabilistic model that tackles this uncertainty at the grass root level. The basic tool for dealing with this uncertainty is probability which is defined for an event as the proportion of times that the event would occur in repetitions of essentially identical situations. Although useful and successful in many applications, probability theory is, in fact, appropriate for dealing with only a very special type of uncertainty for measuring information. Probabilistic databases are all the more susceptible to uncertainties in query results being exclusively dependent on the probabilities assigned with inherent uncertainty in the evaluation of probabilities. Thus it becomes a potential area where this fundamental problem can be addressed and a suitable correction can be made to probabilities evaluated thereof.

Proceedings of the 2019 International Conference on Management of Data, 2019

Certain answers are a principled method for coping with uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Thus, users frequently resort to less principled approaches to resolve uncertainty. In this paper, we propose Uncertainty Annotated Databases (UA-DBs), which combine an under-and over-approximation of certain answers to achieve the reliability of certain answers, with the performance of a classical database system. Furthermore, in contrast to prior work on certain answers, UA-DBs achieve a higher utility by including some (explicitly marked) answers that are not certain. UA-DBs are based on incomplete K-relations, which we introduce to generalize the classical set-based notion of incomplete databases and certain answers to a much larger class of data models. Using an implementation of our approach, we demonstrate experimentally that it efficiently produces tight approximations of certain answers that are of high utility.