Efficient Table Anonymization for Aggregate Query Answering

2009 ◽  
Author(s):  
Cecilia M. Procopiuc ◽  
Divesh Srivastava
Keyword(s):  
Query Answering ◽  
Aggregate Query

scholarly journals Hybrid Query and Data Ordering for Fast and Progressive Range-Aggregate Query Answering

2005 ◽  
Vol 1 (2) ◽  
pp. 49-69 ◽  
Author(s):  
Cyrus Shahabi ◽  
Mehrdad Jahangiri ◽  
Dimitri Sacharidis
Keyword(s):  
Query Answering ◽  
Aggregate Query

Hybrid Query and Data Qodering for Fast and Progressive Range-Aggregate Query Answering

2008 ◽  
pp. 1250-1268
Author(s):  
Cyrus Shahabi ◽  
Mehrdad Jahangiri ◽  
Dimitris Sacharidis
Keyword(s):  
Experimental Studies ◽  
Query Answering ◽  
Common Belief ◽  
Multidimensional Datasets ◽  
Scientific Datasets ◽  
Aggregate Queries ◽  
Random Dataset ◽  
Aggregate Query ◽  
The Common ◽  
Superior Approach

Data analysis systems require range-aggregate query answering of large multidimensional datasets. We provide the necessary framework to build a retrieval system capable of providing fast answers with progressively increasing accuracy in support of range-aggregate queries. In addition, with error forecasting, we provide estimations on the accuracy of the generated approximate results. Our framework utilizes the wavelet transformation of query and data hypercubes. While prior work focused on the ordering of either the query or the data coefficients, we propose a class of hybrid ordering techniques that exploits both query and data wavelets in answering queries progressively. This work effectively subsumes and extends most of the current work where wavelets are used as a tool for approximate or progressive query evaluation. The results of our experimental studies show that independent of the characteristics of the dataset, the data coefficient ordering, contrary to the common belief, is the inferior approach. Hybrid ordering, on the other hand, performs best for scientific datasets that are inter-correlated. For an entirely random dataset with no inter-correlation, query ordering is the superior approach.

Aggregate Query Answering under Uncertain Schema Mappings

2009 ◽  
Author(s):  
Avigdor Gal ◽  
Maria Vanina Martinez ◽  
Gerardo I. Simari ◽  
V. S. Subrahmanian
Keyword(s):  
Query Answering ◽  
Schema Mappings ◽  
Aggregate Query

Computing Cores for Existential Rules with the Standard Chase and ASP

2020 ◽  
Author(s):  
Markus Krötzsch
Keyword(s):  
Large Class ◽  
Data Exchange ◽  
Answer Set Programming ◽  
Query Answering ◽  
Universal Model ◽  
The Core ◽  
Universal Models ◽  
General Terms ◽  
Special Cases ◽  
The Many

To reason with existential rules (a.k.a. tuple-generating dependencies), one often computes universal models. Among the many such models of different structure and cardinality, the core is arguably the “best”. Especially for finitely satisfiable theories, where the core is the unique smallest universal model, it has advantages in query answering, non-monotonic reasoning, and data exchange. Unfortunately, computing cores is difficult and not supported by most reasoners. We therefore propose ways of computing cores using practically implemented methods from rule reasoning and answer set programming. Our focus is on cases where the standard chase algorithm produces a core. We characterise this desirable situation in general terms that apply to a large class of cores, derive concrete approaches for decidable special cases, and generalise these approaches to non-monotonic extensions of existential rules.

An Improved Set-based Reasoner for the Description Logic 𝒟ℒD4,׆

2021 ◽  
Vol 178 (4) ◽  
pp. 315-346
Author(s):  
Domenico Cantone ◽  
Marianna Nicolosi-Asmundo ◽  
Daniele Francesco Santamaria
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Description Logic ◽  
Knowledge Bases ◽  
Query Answering ◽  
Conjunctive Query ◽  
Classification Problems ◽  
Previous Version ◽  
Decidable Fragment ◽  
Stratified Set

We present a KE-tableau-based implementation of a reasoner for a decidable fragment of (stratified) set theory expressing the description logic 𝒟ℒ〈4LQSR,×〉(D) (𝒟ℒD4,×, for short). Our application solves the main TBox and ABox reasoning problems for 𝒟ℒD4,×. In particular, it solves the consistency and the classification problems for 𝒟ℒD4,×-knowledge bases represented in set-theoretic terms, and a generalization of the Conjunctive Query Answering problem in which conjunctive queries with variables of three sorts are admitted. The reasoner, which extends and improves a previous version, is implemented in C++. It supports 𝒟ℒD4,×-knowledge bases serialized in the OWL/XML format and it admits also rules expressed in SWRL (Semantic Web Rule Language).

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