Management of Uncertainty in Database Systems

Databases are models of the real world. Yet, our knowledge of the real world is often imperfect, thus challenging our ability to create databases of integrity. To uphold the integrity of a database in situations where knowledge of the real world is imperfect, one may either (1) restrict the model to that portion of the real world about which perfect information is available, or (2) develop formalisms that allow the representation of imperfect information. This paper surveys some of the better-known database formalisms for capturing imperfect information. Imperfections in the specification and processing of transactions also have important impact on the quality of the information delivered to users, and this survey discusses them as well.

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.

2006

Welcome to the second Twente Data Management Workshop. The topic of this second edition is Uncertainty in Databases. This topic has gained more and more attention these last years. This is clearly visible by the increasing number of workshops on, or related to this topic.

Lecture Notes in Computer Science, 1997

Although the relational model for databases provides a great range of advantages over other data models, it lacks a comprehensive way for handling uncertain data. Uncertainty in data values, however, is pervasive in all real world environments and has received some attention in the literature. Several methods have been proposed for incorporating uncertain data into relational databases; however, these approaches have many shortcomings. In this paper, we discuss a probabilistic extension of the relational model and propose a query language for creation, modification, and retrieval of uncertain data.

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management - CIKM '07, 2007

Since their invention in the early 70s, relational databases have been deterministic. They were designed to support applications s.a. accounting, inventory, customer care, and manufacturing, and these applications require a precise semantics. Thus, database systems are deterministic. A row is either in the database or is not; a tuple is either in the query answer or is not. The foundations of query processing and the tools that exists today for managing data rely fundamentally on the assumption that the data is deterministic.

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.

2009

Preface This is the third edition of the international workshop on Management of Uncertain Data. The previous editions took place in Vienna, Austria and Auckland, New Zealand. The edition in Auckland was a combined event with the workshop on Quality in Databases. Research on uncertain data has grown over the past few years. Since the prequal to this workshop, the Twente Data Management workshop on Uncertain Data, the number of submissions about management of uncertain data to large conferences has grown rapidly.

Sigmod Record, 1990

Most database systems are designed under assumptions of precision of both the data stored in their databases, and the requests to retrieve data. In reality, however, these assumptions are often invalid, and in recent years considerable attention has been given to issues of imprecision in database systems. In this paper we review the major solutions for accommodating imprecision, and we describe issues that have yet to addressed, offering possible research directions. 1 Introduction Information stored in a database is precise if it is assured to be identical to the "real world" information which it represents. When precise information is unavailable, it is often the case that some relevant information is nonetheless available. In these cases, it may be advantageous to design methods by which this information, termed imprecise information, can be stored, manipulated and retrieved. Imprecision may also be present in requests to retrieve data, when users, either intentionally or by necessity, formulate their queries in imprecise terms. Depending on the data model used, the information stored in a database may take different forms, and imprecision could affect each and every one of them. For example, in the entityrelationship model, imprecision could occur in both entities and relationships. However, as virtually all recent research work in the area of database imprecision has been in the context of the relational data model, our discussion here is limited to this model. The structures of the relational model admit two different kinds of imprecision. The first kind involves imprecision at the level of data values; for example, the values of SALARY in the relation EARN (EMPLOYEE, SALARY) may be imprecise. The other kind involves imprecision at the level of the tuple; for example, the values of each of the attributes of the relation ASSICN (EMPLOYEE, PROJECT) may be precise, but there may be uncertainty as to the precise assignment of employees to projects. The relevant information that is available in the absence of precise data may take different forms. First, consider these examples of imprecision at the level of data values. It may be known that the true data value belongs to a specific set of values. Such imprecise data is often referred This work was supported in part by the AT&T Affiliates Research Program.

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 the 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 notions 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 approximat...

Fuzzy Sets and Systems, 2015