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Classification of annotation semirings over query containment

Egor V. Kostylev, Juan L. Reutter, András Z. Salamon
2012 Proceedings of the 31st symposium on Principles of Database Systems - PODS '12  
Compute the annotation of h(Q) by multiplication, 2. Sum over all evaluations.  ...  Compute the annotation of h(Q) by multiplication, 2. Sum over all evaluations.  ... 
doi:10.1145/2213556.2213590 dblp:conf/pods/KostylevRS12 fatcat:jjhem6hmdffrbo3vjzlxzng2dq

Classification of annotation semirings over containment of conjunctive queries

Egor V. Kostylev, Juan L. Reutter, András Z. Salamon
2014 ACM Transactions on Database Systems  
We study the problem of query containment of conjunctive queries over annotated databases.  ...  We axiomatize several classes of semirings for each of which containment of conjunctive queries is equivalent to existence of a particular type of homomorphism.  ...  Hence a general theory is needed to explain how queries behave over annotated relations, and to provide query optimization and query rewriting techniques, regardless of the semiring chosen for annotations  ... 
doi:10.1145/2556524 fatcat:keokxlm5cvbxjald3etormf5jm

Provenance and Probabilities in Relational Databases

Pierre Senellart
2018 SIGMOD record  
of a query.  ...  We describe different provenance formalisms, from Boolean provenance to provenance semirings and beyond, that can be used for a wide variety of purposes, to obtain additional information on the output  ...  As in [31] , we let provenance tokens (the annotations attached to tuples of the input databases) be Boolean functions over X, that is, functions of Let Q be an arbitrary query, i.e., a function that  ... 
doi:10.1145/3186549.3186551 fatcat:5iqtfead6va7pj7c2wosiw3rui

ProvSQL

Pierre Senellart, Louis Jachiet, Silviu Maniu, Yann Ramusat
2018 Proceedings of the VLDB Endowment  
ProvSQL supports a large subset of non-aggregate SQL queries.  ...  A large range of provenance formalisms are supported, including all those captured by provenance semirings, provenance semirings with monus, as well as where-provenance.  ...  We are grateful to Peter Buneman for discussions on integration of where-provenance into ProvSQL, and to Boris Glavic for insight on Perm and GProM.  ... 
doi:10.14778/3229863.3236253 fatcat:gyn2jxvhwnd3vk3xynj2kdbjrm

Querying Attributed DL-Lite Ontologies Using Provenance Semirings

Camille Bourgaux, Ana Ozaki
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
One of the most important uses of annotations is to record provenance information.  ...  We then propose a new semantics, based on provenance semirings, for integrating provenance information with query answering.  ...  The query (Married(a) ∧ Married(b))@S with S = src: s 1 × s 2 , classif : public × confid, mult: 2 × 3 is entailed by {Married(a)@ src: s 1 , classif : public, mult: 2 , spouse(b, c)@ src: s 2 , classif  ... 
doi:10.1609/aaai.v33i01.33012719 fatcat:k6qy5fl7lffitjyw6j5w7gvhzy

Provenance in Databases: Why, How, and Where

James Cheney, Laura Chiticariu, Wang-Chiew Tan
2007 Foundations and Trends in Databases  
Part of the work was done while Chiticariu was a Ph.D. candidate at University of California, Santa Cruz.  ...  Cheney has been supported by UK EPSRC grants EP/F028288/1 and GR/S63205/01, by a Royal Society University Research Fellowship, and by the UK eScience Institute Theme Program on "Principles of Provenance  ...  Formally, two queries Q and Q are annotation-equivalent if they are annotation-contained in each other.  ... 
doi:10.1561/1900000006 fatcat:53whf2d26vd3lgdjtkbh2xfski

Tuple-based access control

Romuald Thion, François Lesueur, Meriam Talbi
2015 Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15  
Promising ongoing research work include the generalization of the theoretical framework to more expressive query languages including aggregation and difference operators as well as experiments on secure  ...  We show that the framework can capture a large class of policies similar to those of lattice-based access control models and that it can be integrated seamlessly into relational database management systems  ...  Since such data contain private information by nature, it is mandatory to offer document and file owners some control over the diffusion of their personal data.  ... 
doi:10.1145/2695664.2695758 dblp:conf/sac/ThionLB15 fatcat:t6gxfsindfft3iwtukuip7otvq

TRAMP

Boris Glavic, Gustavo Alonso, Renée J. Miller, Laura M. Haas
2010 Proceedings of the VLDB Endowment  
In addition we provide query support for transformations, data, and all forms of provenance.  ...  In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries.  ...  Name Semantics THIS Returns the XML representation of the query it is used in. hasAnnot Checks if an XML document contains a certain annotation. getAnnot Return all annotations used in an XML query representation  ... 
doi:10.14778/1920841.1921003 fatcat:743tpdx62rh77hznvtyprrdyae

On Functional Aggregate Queries with Additive Inequalities

Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
2019 Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems - PODS '19  
Any FAQ and FAQ-AI over one semiring can be answered in time proportional to #subw and respectively to the relaxed version of #subw.  ...  These optimization problems can be solved over a database asymptotically faster than computing the join of the database relations.  ...  An IQ query has the form The proof of Proposition 3.15 offers a family of similar examples. □ 3.3.2 FAQ-AI over an arbitrary semiring.  ... 
doi:10.1145/3294052.3319694 dblp:conf/pods/KhamisCM0NOS19 fatcat:s7tc4vukfnf4xfzz2ilrptbupm

DeepProbLog: Neural Probabilistic Logic Programming [article]

Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
2018 arXiv   pre-print
exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.  ...  We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.  ...  It then uses the probability semiring with regular addition and multiplication as operators to compute the probability of a query on the SDD constructed for this query, cf. Figure 1b .  ... 
arXiv:1805.10872v2 fatcat:vfybzoabxfd4vazuyizhfxnqfy

Functional Aggregate Queries with Additive Inequalities [article]

Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
2020 arXiv   pre-print
Any FAQ and FAQ-AI over one semiring can be answered in time proportional to #subw and respectively to the relaxed version of #subw.  ...  We refer to these queries as FAQ-AI for short. To answer FAQ-AI in the Boolean semiring, we define relaxed tree decompositions and relaxed submodular and fractional hypertree width parameters.  ...  While FAQ queries over the Boolean semiring are solvable within the tighter bound of submodular width [32, 9] , such a bound is not known to be achievable over arbitrary semirings, including count queries  ... 
arXiv:1812.09526v4 fatcat:st7lfp7hufagdkcybhrgold5ey

kProbLog: an algebraic Prolog for machine learning

Francesco Orsini, Paolo Frasconi, Luc De Raedt
2017 Machine Learning  
We introduce kProbLog as a declarative logical language for machine learning. kProbLog is a simple algebraic extension of Prolog with facts and rules annotated by semiring labels.  ...  semirings of dual numbers to perform algorithmic differentiation.  ...  We further introduced in the language the semiring of dual numbers so that kProbLog can also express gradient descent learning, while the semiring of dual numbers allowed us to specify matrix factorization  ... 
doi:10.1007/s10994-017-5668-y fatcat:rrladr6ebbf67k4chgsefos6ku

Neural Probabilistic Logic Programming in DeepProbLog [article]

Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
2019 arXiv   pre-print
exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.  ...  We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.  ...  of all worlds containing q, i.e., P (q) = F ⊆F :q∈w F P (w F ) (2) The probability of a query is also equal to the weighted model count (WMC) of the worlds where this query is true.  ... 
arXiv:1907.08194v2 fatcat:cxspnmb6uverdgn6k7ssmfi2oe

Neural probabilistic logic programming in DeepProbLog

Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
2021 Artificial Intelligence  
exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.  ...  We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.  ...  all worlds containing q, i.e., P (q) = F ⊆F Θ:q∈w F P (w F ) (2) The probability of a query is also equal to the weighted model count (WMC, see [18] for more details) of the worlds where this query  ... 
doi:10.1016/j.artint.2021.103504 fatcat:kjlerx7wxjdzdfpguarh5uhdwa

From "Dango" to "Japanese Cakes": Query Reformulation Models and Patterns

Paolo Boldi, Francesco Bonchi, Carlos Castillo, Sebastiano Vigna
2009 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology  
We apply the model to automatically label two large query logs, creating annotated query-flow graphs.  ...  Section 2 describes related work, and in Section 3 we discuss the taxonomy of QRTs that we adopt in this paper.  ...  In the other half of the cases, the user has to reformulate her initial query because it was over-or under-specified, or did not use terminology matching relevant documents, or simply contained errors  ... 
doi:10.1109/wi-iat.2009.34 dblp:conf/webi/BoldiBCV09 fatcat:o7lknhovvfanzmlhsdxmw4c3tu
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