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Uncertainties in the Future
The Possible Worlds Data Model
Improve Hardness Results
Conclusions
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Management of probabilistic data

Profile image of Dan SuciuDan Suciu

2008

https://doi.org/10.1145/1265530.1265531
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12 pages

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Abstract

Many applications today need to manage large data sets with uncertainties. In this paper we describe the foundations of managing data where the uncertainties are quantified as probabilities. We review the basic definitions of the probabilistic data model, present some fundamental theoretical result for query evaluation on probabilistic databases, and discuss several challenges, open problems, and research directions.

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MayBMS is a state-of-the-art probabilistic database management system that has been built as an extension of Postgres, an open-source relational database management system. MayBMS follows a principled approach to leveraging the strengths of previous database research for achieving scalability. This article describes the main goals of this project, the design of query and update language, efficient exact and approximate query processing, and algorithmic and systems aspects. I thank Dan Suciu for the inspirational talk he gave at a Dagstuhl seminar in February of 2005, which triggered my interest in probabilistic databases and the start of the project. I am also indebted to Joseph Halpern for insightful discussions.

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Sensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments * A shorter version of this paper appeared in SIGMOD 2003 (http://www.cs.purdue.edu/homes/ckcheng/papers/sigmod03.pdf). This manuscript contains, among others, the following new materials: (1) A new section (Section 5) on efficient evaluation of probabilistic queries, where disk-based uncertainty indexing and numerical methods are examined; (2) New sets of experimental results (Section 7) in a more realistic simulation model; (3) A method based on time-series analysis for obtaining a probability density function in the uncertainty model (Appendix A); (4) Enhancement of probability query evaluation algorithms to handle special cases of uncertainty in Appendix B; and (3) Discussions on future work in Section 9, as well as more detailed examples. † Corresponding author. are performed to examine the effectiveness of several data update policies.

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Many applications employ sensors for monitoring entities such as temperature and wind speed. A centralized database tracks these entities to enable query processing. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), it is often infeasible to store the exact values at all times. A similar situation exists for moving object environments that track the constantly changing locations of objects. In this environment, it is possible for database queries to produce incorrect or invalid results based upon old data. However, if the degree of error (or uncertainty) between the actual value and the database value is controlled, one can place more confidence in the answers to queries. More generally, query answers can be augmented with probabilistic estimates of the validity of the answers. In this paper we study probabilistic query evaluation based upon uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.

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