Book chapter
Bi-objective Optimization for Approximate Query Evaluation.
A problem of effective and efficient approximate query evaluation is addressed as a special case of multi-objective optimization with 2 criteria: the computational resources and the quality of result. The proposed optimization and execution model provides for interactive trade of quality for speed.
In book
We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting,T-mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.
Actual problems of modeling of ecologic-economic systems on the example of the Republic of Armenia (RA) are considered.
This paper describes enhanced algorisms of lexical optimization query. These algorisms detect and remove redundant conditions from query restriction to simplify it. The paper also presents results of implementation of these optimization techniques and those effects on query processing speed. The paper includes four sections. The first section (Introduction) provides general context of the paper. The second section describes three proposed algorithms of lexical query optimization. The first one is the algorithm of absorption. This algorithm allows to find and remove a wide set of conditions that are redundant but are not equal textually even after standardization of whole restriction expression. The second algorithm is an adaptation of well-known Quin-McCluskey algorithm initially designed for minimization of Boolean functions. The last algorithm of lexical query optimization is based on techniques for optimization of systems of linear inequalities. The third section of the paper discusses results of efficiency evaluation of the proposed algorithms. The forth section concludes the paper.
This paper describes enhanced algorisms of lexical optimization query. These algorisms detect and remove redundant conditions from query restriction to simplify it. The paper also presents results of implementation of these optimization techniques and those effects on query processing speed. The paper includes four sections. The first section (Introduction) provides general context of the paper. The second section describes three proposed algorithms of lexical query optimization. The first one is the algorithm of absorption. This algorithm allows to find and remove a wide set of conditions that are redundant but are not equal textually even after standardization of whole restriction expression. The second algorithm is an adaptation of well-known Quin-McCluskey algorithm initially designed for minimization of Boolean functions. The last algorithm of lexical query optimization is based on techniques for optimization of systems of linear inequalities. The third section of the paper discusses results of efficiency evaluation of the proposed algorithms. The forth section concludes the paper.
In the last years native RDF stores made enormous progress in closing the performance gap compared to RDBMS. This albeit smaller gap, however, still prevents adoption of RDF stores in scenarios with high requirements on responsiveness. We try to bridge the gap and present a native RDF store “OntoQuad” and its fundamental design principles. Basing on previous researches, we develop a vector database schema for quadruples, its realization on index data structures, and ways to efficiently implement the joining of two and more data sets simultaneously. We also offer approaches to optimizing the SPARQL query execution plan which is based on its heuristic transformations. The query performance efficiency is checked and proved on BSBM tests. The study results can be taken into consideration during the development of RDF DBMS’s suitable for storing large volumes of Semantic Web data, as well as for the creation of large-scale repositories of semantic data.
The presented overview is concerned with lexical query optimization and covers papers published in the last four decades. The paper consists of five sections. The first section – Introduction – classifies query optimization techniques into semantic optimizations and lexical optimizations. Semantic optimizations usually relies on data integrity rules that are stores within metadata part of databases, and on data statistics. This kind of optimizations is discussed in many textbooks and papers. Lexical optimizations (more often called rewriting) use only a text of query and no other information about database and its structure. Lexical optimizations are further classified into query transformations, query amelioration, and query reduction. The second section of the paper discusses techniques of query transformation such as predicate pushdown, transformation of nested query into query with joins, etc. Query amelioration is a topic of the third section with a focus on magic set optimizations. The forth section covers query reduction optimizations. The section briefly describes traditional approaches (such as tableau-based) and considers in more details three new algorithms proposed by authors. The fifth section concludes the paper.
Topic modelling is an area of text mining that has been actively developed in the last 15 years. A probabilistic topic model extracts a set of hidden topics from a collection of text documents. It defines each topic by a probability distribution over words and describes each document with a probability distribution over topics. In applications, there are often many requirements, such as, for example, problem-specific knowledge and additional data, to be taken into account. Therefore, it is natural for topic modelling to be considered a multiobjective optimization problem. However, historically, Bayesian learning became the most popular approach for topic modelling. In the Bayesian paradigm, all requirements are formalized in terms of a probabilistic generative process. This approach is not always convenient due to some limitations and technical difficulties. In this work, we develop a non-Bayesian multiobjective approach called the Additive Regularization of Topic Models (ARTM). It is based on regularized Maximum Likelihood Estimation (MLE), and we show that many of the well-known Bayesian topic models can be re-formulated in a much simpler way using the regularization point of view. We review some of the most important types of topic models: multimodal, multilingual, temporal, hierarchical, graph-based, and short-text. The ARTM framework enables easy combination of different types of models to create new models with the desired properties for applications. This modular 'lego-style' technology for topic modelling is implemented in the open-source library BigARTM. © 2017 FRUCT.