scholarly journals HyperFile: A data and query model for documents

1995 ◽  
Vol 4 (1) ◽  
pp. 45-86 ◽  
Author(s):  
Chris Clifton ◽  
Hector Garcia-Molina ◽  
David Bloom
Keyword(s):  
Query Model

DERANDOMIZED LEARNING OF BOOLEAN FUNCTIONS OVER FINITE ABELIAN GROUPS

2001 ◽  
Vol 12 (04) ◽  
pp. 491-516
Author(s):  
M. SITHARAM ◽  
TIMOTHY STRANEY
Keyword(s):  
Boolean Functions ◽  
Partial Information ◽  
Abelian Groups ◽  
Query Complexity ◽  
Character Theory ◽  
Finite Abelian Groups ◽  
Training Set ◽  
Membership Queries ◽  
Approximation Classes ◽  
Query Model

We employ the Always Approximately Correct or AAC model defined in [35], to prove learnability results for classes of Boolean functions over arbitrary finite Abelian groups. This model is an extension of Angluin's Query model of exact learning. The Boolean functions we consider belong to approximation classes, i.e. functions that are approximable (in various norms) by few Fourier basis functions, or irreducible characters of the domain Abelian group. We contrast our learnability results to previous results for similar classes in the PAC model of learning with and without membership queries. In addition, we discuss new, natural issues and questions that arise when the AAC model is used. One such question is whether a uniform training set is available for learning any function in a given approximation class. No analogous question seems to have been studied in the context of Angluin's Query model. Another question is whether the training set can be found quickly if the approximation class of the function is completely unknown to the learner, or only partial information about the approximation class is given to the learner (in addition to the answers to membership queries). In order to prove the learnability results in this paper we require new techniques for efficiently sampling Boolean functions using the character theory of finite Abelian groups, as well as the development of algebraic algorithms. The techniques result in other natural applications closely related to learning, for example, query complexity of deterministic algorithms for testing linearity, efficient pseudorandom generators, and estimating VC dimensions for classes of Boolean functions over finite Abelian groups.

Effective Query Model Estimation Using Parsimonious Translation Model in Language Modeling Approach

2005 ◽  
pp. 288-298
Author(s):  
Seung-Hoon Na ◽  
In-Su Kang ◽  
Ji-Eun Roh ◽  
Jong-Hyeok Lee
Keyword(s):  
Language Modeling ◽  
Model Estimation ◽  
Translation Model ◽  
Modeling Approach ◽  
Query Model

Semantic Extension of Query for the Linked Data

2017 ◽  
Vol 13 (4) ◽  
pp. 109-133 ◽  
Author(s):  
Pu Li ◽  
Yuncheng Jiang ◽  
Ju Wang ◽  
Zhilei Yin
Keyword(s):  
Similarity Search ◽  
Linked Data ◽  
Query Language ◽  
Semantic Structure ◽  
Semantic Extension ◽  
Data Querying ◽  
Novel Algorithms ◽  
Semantic Properties ◽  
Query Model ◽  
Better Than

With the advent of Big Data Era, users prefer to get knowledge rather than pages from Web. Linked Data, a new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Further, the SPARQL query language for RDF is the foundation of many current researches about Linked Data querying. However, these SPARQL-based methods cannot fully express the semantics of the query, so they cannot unleash the potential of Linked Data. To fill this gap, this paper designs a new querying method which extends the SPARQL pattern. Firstly, the authors present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SMP). They then establish a well-defined framework for the notion of Semantically-Extended Query Model for the Linked Data (SEQMLD). Moreover, the authors propose some novel algorithms for executing queries by integrating semantic extension into SPARQL pattern. Lastly, experimental results show that the authors' proposal has a good generality and performs better than some of the most representative similarity search methods.

Research on the Spatial Query Technology of Geospatial Database

2014 ◽  
Vol 571-572 ◽  
pp. 600-605
Author(s):  
Lei Gang Sun ◽  
Jian Feng Liu ◽  
Quan Hong Xu
Keyword(s):  
Data Model ◽  
Storage Management ◽  
Geospatial Data ◽  
Spatial Query ◽  
Oracle Spatial ◽  
Practical Applications ◽  
Data Query ◽  
Geospatial Database ◽  
Data Volume ◽  
Query Model

The application requirement of Geospatial data is increasing and complex as it is getting numerous as a result of furthering study on geosciences. Based on a deeply research on Oracle Spatial storage management mechanism, this paper proposed a method that applies the graph theory to domain of optimizing spatial query of massive geographical data, and established a geospatial data query model in order to settle a problem of lower spatial query efficiency in geospatial database. Combining with the practical applications, this paper did a conventional spatial query test and a spatial query based on geospatial data model respectively. The result is that the spatial query based on geospatial data query model has a better efficiency than that on conventional method. Besides, this model can greatly improve the spatial query performance and this improvement will be increasingly apparent as the data volume increases.

A Case Study of the Aggregation Query Model in Read-Mostly NoSQL Document Stores

2016 ◽  
Author(s):  
Diego Pasqualin ◽  
Giovanni Souza ◽  
Eduardo Luis Buratti ◽  
Eduardo Cunha de Almeida ◽  
Marcos Didonet Del Fabro ◽  
...  
Keyword(s):  
Query Model ◽  
Document Stores

scholarly journals Privacy preserving query model using inverse laplacian differential technique

2019 ◽  
Vol 1228 ◽  
pp. 012001
Author(s):  
K Sandhya Rani Kundra ◽  
J Hyma ◽  
P V G D Prasad Reddy ◽  
K Venkata Rao
Keyword(s):  
Privacy Preserving ◽  
Differential Technique ◽  
Query Model

A query model based on normalized log-likelihood

2009 ◽  
Author(s):  
Edgar Meij ◽  
Wouter Weerkamp ◽  
Maarten de Rijke
Keyword(s):  
Model Based ◽  
Log Likelihood ◽  
Query Model
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