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About 5,422 papers for “Probabilistic database”
Télécom Paristech, S. Abiteboul, T-h. H. Chan et al. · 2010
• Approximate query processing: ranking, linkage • Information extraction: approximate search for entities (e.g. names) in text • Sensor data: imprecise or missing readings
Cited by 0
Ander Keijzer, Maurice Keulen · 2008
Data exchange between embedded systems and other small or large computing devices increases. Since data in different data sources may refer to the same real world objects, data cannot simply be merged. Furthermore, in many situations, conflicts in data about the same real world objects need to be resolved without interference from a user. In this report, we report on an attempt to make a RDBMS probabilistic, i.e., data in a relation represents all possible views on the real world, in order to achieve unattended data integration. We define a probabilistic relational data model and review standard SQL query primitives in the light of probabilistic data. It appears that thinking in terms of ‘possible worlds ’ is powerful in determining the proper semantics of these query primitives. 1
Cited by 0
Jinfeng Ni, Chinya V. Ravishankar, Bir Bhanu · Lecture notes in computer science · 2003
Cited by 29
Dan Suciu · 2008
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 and present some fundamental theoretical results for query evaluation on probabilistic databases.
Cited by 0
BIAZZO V, FERRO A, GIUGNO R. et al. · IRIS Research product catalog (Sapienza University of Rome) · 2000
A new model for probabilistic databases, using interval-valued conditional probability assessments, is proposed. The concept of coherence adopted in our approach is based on a suitable generalization of the coherence principle of de Finetti and can be related to similar definitions given for lower and upper probabilities by other authors. A corresponding probabilistic relational algebra is introduced and some new operators are defined. Finally, some simple examples are given.
Cited by 0
Biazzo, Alfredo Ferro, Angelo Gilio et al. · 2000
Cited by 6
Laks V.S. Lakshmanan, Nicola Leone, Robert Ross et al. · 1997
... In this article, we characterize, using postulates, whole classes of strategies for conjunction, disjunction, and negation, meaningful from the viewpoint of probability theory. (1) We propose a probabilistic relational data model and a generic probabilistic relational algebra that neatly captures various strategies satisfying the postulates, within a single unified framework. (2) We show that as long as the chosen strategies can be computed in polynomial time, queries in the positive fragment of the probabilistic relational algebra have essentially the same data complexity as classical relational algebra. (3) We establish various containments and equivalences between algebraic expressions, similar in spirit to those in classical algebra. (4) We develop algorithms for maintaining materialized probabilistic views. (5) Based on these ideas, we have developed
Cited by 0
Edward Hung · 2007
Recent interest in semistructured data has led to a proliferation of XML-based standards which encompass applications ranging from multimedia applications and sensor data processing applications to financial applications and myriads of other more traditional applications. When semistructured paradigms are used to store sensor data and multimedia (e.g. image) data, we need to be able to handle uncertainty as sensor readings and image processing methods often yield uncertain results. In this paper, we propose the concept of probabilistic semistructured (PS) databases. We propose a global notion of consistency as well as a local one and show that the two coincide.
Cited by 0
Roberto Salazar · Parallel Processing Letters · 2023
A crucial problem of machine learning is the management of unclean probabilistic databases. We approach this problem by applying recent results and methods from quantum information to detect a specific class of database corruption. We present a quantifier of the global corruption of the probabilistic database and show its relationship with detection protocols based on generalized Bell inequalities. Furthermore, we show a relation between the noise generating the corruption and information encoded in the database schema. Finally, we discuss how our work indicates a way to export quantum information results to study noise in probabilistic databases.
Cited by 0Open access
Ping-Tsai Chung, Hsin-Hua Hsiao · 2008
In the information age, information is usually communicated and exchanged with heterogeneous databases in a distributed internetworking environment. When multiple heterogeneous databases show different values for the same data item, its actual value is not known with certainty. Probabilistic relational databases have extended from the relational database model by incorporating probability measures to capture the data uncertainty associated with data items. In this paper, we apply the probabilistic relational database techniques to the area of biomedical informatics. We address the issue of database interoperability by using a data warehousing approach to resolve mismatched domains and to integrate information from different data sources. We then develop query processing strategies to effectively evaluate and answer queries from the consolidated database.
Cited by 5
İsmail İlkan Ceylan, Stefan Borgwardt, Thomas Lukasiewicz · 2017
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.
Cited by 12Open access
Li Zhu, Pingjian Wang, Xiaozhuo Gu et al. · 2020
Many organizations and enterprises use cloud databases to store data to improve management efficiency and save costs. However, cloud service providers may hide the fact that data integrity has been compromised for protecting their business reputation. Thus, how to verify the data integrity of cloud database in an effective way is very important for data owner. Existing integrity verification methods usually require cloud service provider to develop additional interfaces which are hard to be actually deployed. In addition, they cannot effectively detect tampering and deletion of a small amount of data. This paper presents a novel probabilistic integrity verification scheme (called PIV4DB) to address above challenges. Different from traditional methods, PIV4DB efficiently verifies the data integrity of cloud database by randomly selecting part of groups of tuples instead of querying all the tuples. Experimental results demonstrated that with validating 0.5% among 100k groups, PIV4DB could detect the corruption with 99% probability when the integrity of 920 out of billions of tuples are compromised. In addition, PIV4DB does not need extra cooperation with cloud service provider by just adding a new column of random numbers to the database and only using standard SQL statements to verify integrity.
Cited by 3
İsmail İlkan Ceylan, Stefan Borgwardt, Thomas Lukasiewicz · 2017
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.
Cited by 6Open access
Christoph Koch, Dan Olteanu, Lyublena Antova et al. · 2009
Cited by 0
Roman Krzysztofowicz, William B. Evans · Weather and Forecasting · 2008
Abstract The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, and a quantile function). The quantification of uncertainty is accomplished via Bayes theorem by extracting and fusing two kinds of information from two different sources: (i) a long sample of the predictand from the National Climatic Data Center, and (ii) a short sample of the official National Weather Service forecast from the National Digital Forecast Database. The official forecast is deterministic and hence deficient: it contains no information about uncertainty. The BPF remedies this deficiency by outputting the complete and well-calibrated characterization of uncertainty needed by decision makers and information providers. The BPF comes furnished with (i) the meta-Gaussian model, which fits meteorological data well as it allows all forms of marginal distribution functions, and nonlinear and heteroscedastic dependence structures, and (ii) the statistical procedures for estimation of parameters from asymmetric samples and for coping with nonstationarities in the predictand and the forecast due to the annual cycle and the lead time. A comprehensive illustration of the BPF is reported for forecasts of the daily maximum temperature issued with lead times of 1, 4, and 7 days for three stations in two seasons (cool and warm).
Cited by 35Open access
Qiyu Liu, Xiang Lian, Lei Chen · 2019
Maximum Range-Sum (MaxRS) query is an important operator in spatial database for retrieving regions of interest (ROIs). Given a rectangular query size a × b and a set of spatial objects associated with positive weights, MaxRS retrieves rectangular regions Q of size a × b, such that the sum of object weights covered by Q (i.e., range-sum) is maximized. Due to the inaccuracy of the location acquisition, the collected locations of spatial objects are inherently uncertain and imprecise, which can be modeled by uncertain objects. In this paper, we propose a Probabilistic Maximum Range-Sum (PMaxRS) query over uncertain spatial objects, which obtains a set γ* of rectangles such that the probability that each region Q ϵ γ* has the maximum range-sum exceeds a user-specified threshold Pt. We show that determining whether a given region Q is #P-complete. To tackle the hardness, we introduce the PMaxRS_Framework based on pruning and refinement strategies. In the pruning step, we propose a candidate generation technique to reduce the search space. In the refinement step, we design an efficient sampling-based approximation algorithm to verify the remaining candidate regions. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our algorithms.
Cited by 10
Hoa Quynh Nguyen · Journal of Computer Science and Cybernetics · 2016
This paper introduces a complete extended relational data base model based on probability theory and fuzzy theory, called FPRDB, for representing and handling vague and uncertain information of objects in the real world applications. FPRDB is built by first extending probabilistic triples with fuzzy sets, associating the probability of such fuzzy sets for expressing and computing uncertain degree of imprecise information. Then, schemas, fuzzy probabilistic relations, fuzzy probabilistic functional dependencies and algebraic operations are defined coherently and consistently for FPRDB. A set of the properties of the probabilistic relational algebraic operations in FPRDB is also formulated and proven.
Cited by 3Open access
Imane Seddiki, Farid Nouioua, Abdelbasset Barkat · International Journal of Information Technology · 2023
Cited by 3
Arthur H. Van Bunningen, Ling Feng, Maarten M. Fokkinga et al. · Data Archiving and Networked Services (DANS) · 2007
Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study. 1
Cited by 0Open access
Yunhu Wan · University of Southern California Digital Library · 2014
Cited by 0Open access