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A General Framework for Anytime Approximation in Probabilistic Databases [article]

Maarten Van den Heuvel, Floris Geerts, Wolfgang Gatterbauer, Martin Theobald
2018 arXiv   pre-print
Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks.  ...  We present here a more general branch-and-bound framework that extends the possible bounds by using 'dissociation', which yields tighter bounds.  ...  This work has been supported in part by NSF IIS-1762268 and FWO G042815N.  ... 
arXiv:1806.10078v2 fatcat:xybhqidwi5cudbt5xfgnr2hhgq

Anytime measures for top-k algorithms on exact and fuzzy data sets

Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Nick Koudas
2009 The VLDB journal  
In this article, we initiate research on the anytime behavior of top-k algorithms on exact and fuzzy data.  ...  We adopt a probabilistic approach where we seek to report at any point of operation of the algorithm the confidence that the top-k result has been identified.  ...  Approximating PDFs using histograms We presented our techniques thus far using a generic probabilistic model of data.  ... 
doi:10.1007/s00778-008-0127-9 fatcat:crczvukf3ve5dmvk25wn4tlevq

Anytime Inference in Valuation Algebras [article]

Abhishek Dasgupta, Samson Abramsky
2016 arXiv   pre-print
The novel contribution of this work is the construction of anytime algorithms in a generic framework, which automatically gives us instantiations in various useful domains.  ...  For this we utilise the theory of generic inference as a basis for constructing an anytime inference algorithm, and in particular, extending work done on ordered valuation algebras.  ...  in Dempster-Shafer theory [3, 8] or tables in a relational database; and (2) subsequent focusing or projection to variables of interest, which corresponds to projection for variables in probabilistic  ... 
arXiv:1605.04218v1 fatcat:vyruyuqv2vaj5iaru55kknn62q

Monitoring and control of anytime algorithms: A dynamic programming approach

Eric A. Hansen, Shlomo Zilberstein
2001 Artificial Intelligence  
This paper analyzes the characteristics of existing techniques for meta-level control of anytime algorithms and develops a new framework for monitoring and control.  ...  Anytime algorithms offer a tradeoff between solution quality and computation time that has proved useful in solving time-critical problems such as planning and scheduling, belief network evaluation, and  ...  In this paper, we develop a framework for run-time monitoring and control of anytime algorithms that takes into account these various factors.  ... 
doi:10.1016/s0004-3702(00)00068-0 fatcat:ihvexhqdnjh4zp3hjgshvfpeny

Page 5677 of Mathematical Reviews Vol. , Issue 93j [page]

1993 Mathematical Reviews  
Summary: “Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework.  ...  The h-graph is then generalized for a Pascal-like language, which  ... 

A survey of research in deliberative real-time artificial intelligence

Alan Garvey, Victor Lesser
1994 Real-time systems  
This paper surveys recent research in deliberative real-time artificial intelligence (AI). Major areas of study have been anytime algorithms, approximate processing, and large system architectures.  ...  in the surveyed work.  ...  Acknowledgments We would like to thank Shlomo Zilberstein, Piero Bonissone, Peter Halverson, Barbara Hayes-Roth and Keith Decker for the use of figures that appear in this paper.  ... 
doi:10.1007/bf01088630 fatcat:mlylu3iiifexbcp6tzvc77enoa

Anytime approximation in probabilistic databases

Robert Fink, Jiewen Huang, Dan Olteanu
2013 The VLDB journal  
This algorithm is used by the SPROUT query engine to approximate the probabilities of results to relational algebra queries on expressive probabilistic databases.  ...  This article describes an approximation algorithm for computing the probability of propositional formulas over discrete random variables.  ...  Acknowledgments We would like to thank the anonymous reviewers and Peter Haas for their insightful comments that helped improve this article.  ... 
doi:10.1007/s00778-013-0310-5 fatcat:lzweb674avfcphps7jamzomw2q

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Caroline Ponzoni Carvalho Chanel, Alexandre Albore, Jorrit T'Hooft, Charles Lesire, Florent Teichteil-Königsbuch
2018 Autonomous Robots  
We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions.  ...  Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable.  ...  In this sense, we have worked in developing AMPLE -Anytime Meta PLannEr (Teichteil-Konigsbuch et al., 2011; , which is a general framework for continuous and anticipatory planning while executing.  ... 
doi:10.1007/s10514-018-9703-z fatcat:k2mubpgetfdklpxdbgeaqqvgwi

Multi-agent framework for real-time processing of large and dynamic search spaces

John Korah, Eunice E. Santos, Eugene Santos
2012 Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12  
Anytime algorithms have shown great promise in providing approximate solutions.  ...  We describe a generic multiagent framework that leverages our search space model while modeling various aspects of agent behavior such as candidate selection, agent interactions, etc.  ...  This has led to increased interest in anytime algorithms [12, 8] for its ability to provide approximate solutions.  ... 
doi:10.1145/2245276.2245420 dblp:conf/sac/KorahSS12 fatcat:l7kku34jr5cw7ef5vjdfu3xzwm

Fast and Exact Mining of Probabilistic Data Streams [chapter]

Reza Akbarinia, Florent Masseglia
2013 Lecture Notes in Computer Science  
Discovering Probabilistic Frequent Itemsets (PFI) is very challenging since algorithms designed for deterministic data are not applicable in probabilistic data.  ...  In this paper, we propose FEMP (Fast and Exact Mining of Probabilistic data streams), the first solution for exact PFI mining in data streams with sliding windows.  ...  PFI Mining in Sliding Windows We now introduce FEMP, our framework for PFI mining in probabilistic data streams with a sliding window SW .  ... 
doi:10.1007/978-3-642-40988-2_32 fatcat:rgmsusycgre3dmgpvk72icobru

The TopX DB&IR engine

Martin Theobald, Ralf Schenkel, Gerhard Weikum
2007 Proceedings of the 2007 ACM SIGMOD international conference on Management of data - SIGMOD '07  
This paper proposes a demo of the TopX search engine, an extensive framework for unified indexing, querying, and ranking of large collections of unstructured, semistructured, and structured data.  ...  TopX integrates efficient algorithms for top-k-style ranked retrieval with powerful scoring models for text and XML documents, as well as dynamic and selftuning query expansion based on background ontologies  ...  " scores with regard to a given feature, e.g., the BM25-based probabilistic-IR scores [4] , and stores it in a relational database.  ... 
doi:10.1145/1247480.1247635 dblp:conf/sigmod/TheobaldSW07 fatcat:ehiibvqfarh3rkyilvcnmsjr3i

Bayesian sensor model for indoor localization in Ubiquitous Sensor Network

Abdelmoula Bekkali, Mitsuji Matsumoto
2008 2008 First ITU-T Kaleidoscope Academic Conference - Innovations in NGN: Future Network and Services  
In this paper we introduce a Bayesian sensor framework for solving the location estimation errors problem in Radio Frequency Identification (RFID) environments.  ...  Ubiquitous Sensor Networks (USN) technology is one of the essential key for driving the Next Generation Network (NGN) to realize secure and easy access from anyone, any thing, anywhere and anytime.  ...  Received Signal Strength (RSS) for each WLAN base station is stored as a fingerprint in a database for each point in a dense grid covering the floor.  ... 
doi:10.1109/kingn.2008.4542278 fatcat:gvgzyytysjcjjowiaxwso4qmgi

When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values

Pierre Baldi, Ramzi Nasr
2010 Journal of Chemical Information and Modeling  
Here we develop a general framework for understanding, modeling, predicting, and approximating the distribution of chemical similarity scores and its extreme values in large databases.  ...  After introducing several probabilistic models of fingerprints, including the Conditional Gaussian Uniform model, we show that the distribution of Tanimoto scores can be approximated by the distribution  ...  the query molecule as well as the size of the database being searched, by applying the general framework to the analysis and prediction of ROC curves for molecular retrieval, by applying the general framework  ... 
doi:10.1021/ci100010v pmid:20540577 pmcid:PMC2914517 fatcat:zm6gnmgjxzdihkg46gpmrytwu4

Online Multi-modal Learning and Adaptive Informative Trajectory Planning for Autonomous Exploration [chapter]

Akash Arora, P. Michael Furlong, Robert Fitch, Terry Fong, Salah Sukkarieh, Richard Elphic
2017 Field and Service Robotics  
To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and  ...  a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints.  ...  Acknowledgements We would like to thank NASA's Resource Prospector project and the Australian Center for Field Robotics for funding this work.  ... 
doi:10.1007/978-3-319-67361-5_16 dblp:conf/fsr/AroraFFFSE17 fatcat:y63lzfucofdupot6ridinuwlra

Survey on Distributed Data Mining in P2P Networks [article]

Rekha Sunny T, Sabu M. Thampi
2012 arXiv   pre-print
DDM is gaining attention in peer-to-peer (P2P) systems which are emerging as a choice of solution for applications such as file sharing, collaborative movie and song scoring, electronic commerce, and surveillance  ...  The paper discusses the need for DDM, taxonomy of DDM architectures, various DDM approaches, DDM related works in P2P systems and issues and challenges in P2P data mining.  ...  Other approach for processing sum and count that uses probabilistic counting is described in [21] .  ... 
arXiv:1205.3231v1 fatcat:5tajkiqlg5hufjrhiy3xzz4d4m
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