Query Translation Research Articles

Кэширование машинного кода в динамическом компиляторе SQL-запросов для СУБД PostgreSQL

As the efficiency of main and external memory grows, alongside with decreasing hardware costs, the performance of database management systems (DBMS) on certain kinds of queries is more determined by CPU characteristics and the way it is utilized. Relational DBMS utilize diverse execution models to run SQL queries. Those models have different properties, but in either way suffer from substantial overhead during query plan interpretation. The overhead comes from indirect calls to handler functions, runtime checks and large number of branch instructions. One way to solve this problem is dynamic query compilation that is reasonable only in those cases when query interpretation time is larger than the time of compilation and optimized machine code execution. This requirement can be satisfied only when the amount of data to be processed is large enough. If query interpretation takes milliseconds to finish, then the cost of dynamic compilation can be hundreds of times more than the execution time of generated machine code. To pay off the cost of dynamic compilation, the generated machine code has to be reused in subsequent executions, thus saving the cost of code compilation and optimization. In this paper, we examine the method of machine code caching in our query JIT-compiler for DBMS PostgreSQL. The proposed method allows us to eliminate compilation overhead. The results show that dynamic compilation of queries with machine code caching feature gives a significant speedup on OLTP queries.

Динамическая компиляция пользовательских функций на языке PL/pgSQL

Many modern RDBMS provide procedural extensions for SQL programming language, which allow users to perform server-side complex computations. Use of such extensions improves modularity and code reuse, simplifies programming of application logic, and helps developers to avoid network overhead and enhance performance. Interpretation is mostly used to execute SQL queries and procedural extensions code, resulting in significant computational overhead because of indirect function calls and performing of generic checks. Moreover, most RDBMS use different engines for SQL queries execution and procedural extensions code execution, and it is necessary to perform additional computations to switch between different engines. Thus, interpretation of SQL queries combined with interpretation of procedural extensions code may drastically degrade performance of RDBMS. One solution is to use a dynamic compilation technique. In this paper, we describe the technique of dynamic compilation of PL/pgSQL procedural language for the PostgreSQL database system using LLVM compiler infrastructure. Dynamic compiler of PL/pgSQL procedural language is developed as part of PostgreSQL queries dynamic compiler. Proposed technique helps to get rid of computational overhead caused by interpretation usage. Synthetic performance tests show that the developed solution speeds up SQL queries execution by several times.

An Intermediate Representation-based Approach for Query Translation using a Syntax-Directed Method

We aspire to make one query reasonably sufficient to extract data regardless of the data model used in our research. In such a way, users can freely use any query language they master to interrogate the heterogeneous database, not necessarily the query language associated with the model. Thus, overcoming the needing to deal with multiple query languages, which is, usually, an unwelcome matter for non-expert users and even for the expert ones. To do so, we proposed a new translation approach, relying on an intermediate query language to convert the user query into a suitable query language, according to the nature of data interrogated. Which is more beneficial rather than repeat the whole process for each new query submission. On the other hand, this empowers the system to be modular and divided into multiple, more flexible, and less complicated components. Therefore, it increases possibilities to make independent transformations and to switch between several query languages efficiently. By using our system, querying each data model with the corresponding query language is no longer bothersome. As a start, we are covering the eXtensible Markup Language (XML) and relational data models, whether native or hybrid. Users can retrieve data sources over these models using just one query, expressed with either the XML Path Language (XPath) or the Structured Query Language (SQL).

Developing an effective scheme for translation and expansion of Persian user queries

Abstract This study aims at introducing a new source for translation and expansion of user queries in Persian language in order to develop a bilingual dictionary. For the purpose of this study, required data were extracted and processed from English and Persian bibliographic information of journal articles to develop a dictionary for query translation and expansion, denoted as Query Expansion Assistant Database (QEAD). In this study, psychology and educational sciences journals have been selected as the sample with the potential of extension to other domains. Persian–English authors’ keywords were used for translation part and titles of English references were used to extract phrases using natural language processing techniques for the expansion part. The proposed algorithm is demonstrated. Then we evaluated this approach using human evaluation by using Google translate (GT) and Google scholar. Although the evaluation of translation part indicated 60% match between GT and QEAD, in 40% of unmatched translations, QEAD showed a better performance according to expert evaluators. Expansion part of QEAD was compared with Google scholar suggestions, which indicated that the expanded words of QEAD can equalize with Google scholar suggestions. Persian as a low resource language needs more qualified lexicon translation. In addition, using the English–Persian bibliographic information of scientific journals to mine lexicon translation is conducted for the first time. Since these journals are peer-reviewed, they can be a valuable source for translation of user’s query. Users can be informed of the most prevalent and up-to-date words or phrases among scientists, because journals are published frequently.

On Eileen Chang's Chinese Version of The Old Man and the Sea from the Perspective of Feminist Translation Theory

Abstract: The feminist translation theory is the combination of feminism and translation studies, originated from Western Translation. It draws great attention and provides a new scope of translation studies with in a specific social, historical and cultural context. The advocators of feminist translation theory which focuses gender issues and emphasizes the translator's subjective, especially that of the female translators query faithfulness, equivalence and translator' s invisibility of traditional translation theory and advocate Creative Treason in process of translation. This paper attempts to analyze Eileen Chang's Chinese version of The Old Man and the Sea from the feminist translation theory to explore how Eileen Chang's feminist consciousness is manifested with the feminist interventionist strategies adopted by herself and also to deepen the feminist translation theory and broaden the viewpoints of the studies on Eileen Chang's translation works.

Translations Diversification for Expert Finding

Expert finding is the task of retrieving and ranking knowledgeable people in the subject of user’s query. It is a well-studied problem that has attracted the attention of many researchers. The most important challenge in expert finding is to determine the similarity between query words and documents authored by candidate experts. One of the most important challenges in Information Retrieval (IR) community is the issue of vocabulary gap between queries and documents. In this study, a translation model based on words clustering in two query and co-occurrence spaces is proposed to overcome this problem. First, the words that are semantically close, are clustered in a query space and then each cluster in this space are clustered again in a co-occurrence space. Representatives of each cluster in the co-occurrence space are considered as a diverse subset of the parent cluster. By this method, the query translations are expected to be diversified in the query space. Next, a probabilistic model, that is based on the belonging degree of word to cluster and similarity of cluster to query in the query space, is used to consider the problem of vocabulary gap. Finally, the corresponding translations to each query are used in conjunction with a combination model for expert finding. Experiments on Stack Overflow dataset show the effectiveness of the proposed method for expert finding.

RehabRobo-Query: Answering natural language queries about rehabilitation robotics ontology on the cloud

We introduce a novel method to answer natural language queries about rehabilitation robotics, over the formal ontology REHABROBO-ONTO. For that, (i) we design and develop a novel controlled natural language for rehabilitation robotics, called REHABROBO-CNL; (ii) we introduce translations of queries in REHABRoso-CNI, into SPARQL queries, utilizing a novel concept of query description trees; (iii) we use an automated reasoner to find answers to SPARQL queries. To facilitate the use of our method by experts, we develop an intelligent, interactive query answering system, called REHABROBO-QUERY, using Semantic Web technologies, and make it available on the cloud via Amazon web services. REHABROBO-QUERY guides the users to express their queries in natural language and displays the answers to queries in a readable format, possibly with links to detailed information. Easy access to information on REHABROBO-ONTO through complex queries in natural language may help engineers inspire new rehabilitation robot designs, while also guiding practitioners to make more informed decisions on technology based rehabilitation.

SQL to NoSQL : What to do and How

Due to complex logic of enterprise management, enterprise applications may contain thousands of tables with mass connections or constraints among them. Traditional enterprises still use RDBMS while NoSQL system is widely adopted due to its excellent performance and high availability. So tools for migrating from RDBMS to NoSQL database is on demand. However, schema matters not only in RDBMS but also in NoSQL system for better query response. In this paper we discuss technology in database transformation including schema transforming, query translation and query optimization. And we mainly focus on the open issues in the context of OLAP workload and column-oriented NoSQL.

Using Communities of Words Derived from Multilingual Word Vectors for Cross-Language Information Retrieval in Indian Languages

We investigate the use of word embeddings for query translation to improve precision in cross-language information retrieval (CLIR). Word vectors represent words in a distributional space such that syntactically or semantically similar words are close to each other in this space. Multilingual word embeddings are constructed in such a way that similar words across languages have similar vector representations. We explore the effective use of bilingual and multilingual word embeddings learned from comparable corpora of Indic languages to the task of CLIR. We propose a clustering method based on the multilingual word vectors to group similar words across languages. For this we construct a graph with words from multiple languages as nodes and with edges connecting words with similar vectors. We use the Louvain method for community detection to find communities in this graph. We show that choosing target language words as query translations from the clusters or communities containing the query terms helps in improving CLIR. We also find that better-quality query translations are obtained when words from more languages are used to do the clustering even when the additional languages are neither the source nor the target languages. This is probably because having more similar words across multiple languages helps define well-defined dense subclusters that help us obtain precise query translations. In this article, we demonstrate the use of multilingual word embeddings and word clusters for CLIR involving Indic languages. We also make available a tool for obtaining related words and the visualizations of the multilingual word vectors for English, Hindi, Bengali, Marathi, Gujarati, and Tamil.

X-search: an open access interface for cross-cohort exploration of the National Sleep Research Resource

BackgroundThe National Sleep Research Resource (NSRR) is a large-scale, openly shared, data repository of de-identified, highly curated clinical sleep data from multiple NIH-funded epidemiological studies. Although many data repositories allow users to browse their content, few support fine-grained, cross-cohort query and exploration at study-subject level. We introduce a cross-cohort query and exploration system, called X-search, to enable researchers to query patient cohort counts across a growing number of completed, NIH-funded studies in NSRR and explore the feasibility or likelihood of reusing the data for research studies.MethodsX-search has been designed as a general framework with two loosely-coupled components: semantically annotated data repository and cross-cohort exploration engine. The semantically annotated data repository is comprised of a canonical data dictionary, data sources with a data dictionary, and mappings between each individual data dictionary and the canonical data dictionary. The cross-cohort exploration engine consists of five modules: query builder, graphical exploration, case-control exploration, query translation, and query execution. The canonical data dictionary serves as the unified metadata to drive the visual exploration interfaces and facilitate query translation through the mappings.ResultsX-search is publicly available at https://www.x-search.net/with nine NSRR datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries.ConclusionsX-search provides a powerful cross-cohort exploration interface for querying and exploring heterogeneous datasets in the NSRR data repository, so as to enable researchers to evaluate the feasibility of potential research studies and generate potential hypotheses using the NSRR data.

Language-integrated provenance in Haskell

Scientific progress increasingly depends on data management, particularly to clean and curate data so that it can be systematically analyzed and reused. A wealth of techniques for managing and curating data (and its provenance) have been proposed, largely in the database community. In particular, a number of influential papers have proposed collecting provenance information explaining where a piece of data was copied from, or what other records were used to derive it. Most of these techniques, however, exist only as research prototypes and are not available in mainstream database systems. This means scientists must either implement such techniques themselves or (all too often) go without. This is essentially a code reuse problem: provenance techniques currently cannot be implemented reusably, only as ad hoc, usually unmaintained extensions to standard databases. An alternative, relatively unexplored approach is to support such techniques at a higher abstraction level, using metaprogramming or reflection techniques. Can advanced programming techniques make it easier to transfer provenance research results into practice? We build on a recent approach called language-integrated provenance, which extends language-integrated query techniques with source-to-source query translations that record provenance. In previous work, a proof of concept was developed in a research programming language called Links, which supports sophisticated Web and database programming. In this paper, we show how to adapt this approach to work in Haskell building on top of the Database-Supported Haskell (DSH) library. Even though it seemed clear in principle that Haskell's rich programming features ought to be sufficient, implementing language-integrated provenance in Haskell required overcoming a number of technical challenges due to interactions between these capabilities. Our implementation serves as a proof of concept showing how this combination of metaprogramming features can, for the first time, make data provenance facilities available to programmers as a library in a widely-used, general-purpose language. In our work we were successful in implementing forms of provenance known as where-provenance and lineage. We have tested our implementation using a simple database and query set and established that the resulting queries are executed correctly on the database. Our implementation is publicly available on GitHub. Our work makes provenance tracking available to users of DSH at little cost. Although Haskell is not widely used for scientific database development, our work suggests which languages features are necessary to support provenance as library. We also highlight how combining Haskell's advanced type programming features can lead to unexpected complications, which may motivate further research into type system expressiveness.

Novel Relevance Feedback Approach for Color Trademark Recognition Using Optimization and Learning Strategy

Abstract The trademark registration process, apparent in all organizations nowadays, deals with recognition and retrieval of similar trademark images from trademark databases. Trademark retrieval is an imperative application area of content-based image retrieval. The main challenges in designing and developing this application area are reducing the semantic gap, obtaining higher accuracy, reducing computation complexity, and subsequently the execution time. The proposed work focuses on these challenges. This paper proposes the relevance feedback system embedded with optimization and unsupervised learning technique as the preprocessing stage, for trademark recognition. The search space is reduced by using particle swam optimization, for optimization of database feature set, which is further followed by clustering using self-organizing map. The relevance feedback technique is implemented over this preprocessed feature set. Experimentation is done using the FlickrLogos-32 PLUS dataset. To introduce variations between the training and query images, transformations are applied to each of the query image, viz. rotation, scaling, and translation of the image. The same query image is tested for various combinations of transformations. The proposed technique is invariant to various transformations, with significant performance as depicted in the results.

Research and Design of Cross-Language Vertical Search Engine for Special Agricultural E-commerce Platform

Based on the research of vertical search engine and cross-language information retrieval, a crosslanguage vertical search engine design for e-commerce platform is proposed. It aims to solve the problem that it is difficult for Internet users to quickly, efficiently, and comprehensively search for valuable products, especially ethnic minority netizens. Cross-language in this article mainly refers to the conversion of Chinese, English, and Tibetan. Using dictionary-based query translation method to translate query words to achieve cross-language function. Improved Heritrix designed a web crawler information collection method. Using HtmlParser to achieve structured information extraction, and using Lucene to build an index and achieve retrieval.

On Keyword-Based Ad-Hoc Querying of Hospital Data Stored in Semistar Data Ontologies

This paper sketches a possible solution to the problem of the currently growing necessity in various domains for domain experts to be able to query the database of the organization in a convenient manner. The paper focuses on the domain of hospital management where the normal practice is to involve a programmer as an intermediary between the managers and the database. This is an error-prone and cumbersome solution. The decision-making process of domain experts would hugely benefit if they could retrieve the information from the database themselves. There have been attempts to develop natural language-based query languages for this exact purpose, but the ultimate goal of the simplicity of querying has not yet been reached. The approach presented in this paper involves letting users define their queries in a very weakly-controlled natural language and then to choose among the offered query translations into a more strongly-controlled natural language which already has an efficient implementation for semistar ontologies. Experiments show that the implemented query language is very readable for non-programmers, because it lacks technical details (thanks to the nature of semistar ontologies), and because it is very intuitive. This phenomenon creates a conviction that the proposed approach is highly viable.

Querying clinical data in HL7 RIM based relational model with morph-RDB

BackgroundSemantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.ResultsIn this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.ConclusionsBy applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

Semantics preserving SQL-to-SPARQL query translation for Nested Right and Left Outer Join

Abstract Despite the emergence of semantic web due to its high performance in managing a large amount of data through semantic filters, the relational databases are still the most used. Therefore establishing a connection between both heterogeneous systems becomes a relevant need so as to bridge the gap between them. Regarding the query mapping from relational world to semantic world (SQL-to-SPARQL), some solutions have been developed to realize this transformation but unfortunately, all existing approaches do not consider the semantic correspondence between Left/Right Outer Join command(s) and Optional pattern(s). This weakness has motivated us to investigate in this direction and suggest an efficient solution to deal with this problem by proposing, to the best of our knowledge, the first query mapping algorithm ensuring the semantic transformation of simple/nested Left/Right Outer Join command(s) in SQL queries to a simple/nested Optional pattern(s) in SPARQL equivalent ones without physical transformation of data.

English-Marathi Cross Language Information Retrieval System

Nowadays, different types of content in different languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using different translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

Towards a new possibilistic query translation tool for cross-language information retrieval

Approaches of query translation in Cross-Language Information Retrieval (CLIR) have frequently used dictionaries which suffer from translation ambiguity. Besides, a word-by-word query translation is not sufficient. In this paper, we propose, evaluate and compare a new possibilistic approach for query translation in order to improve the previous dictionary-based ones. This approach uses a probability-to-possibility transformation as a mean to introduce further tolerance in query translation process. Firstly, we identify noun phrases (NPs) in the source query and translate them as units using translation patterns and a language model. Secondly, source query terms which are not included in any selected NPs are translated word-by-word using our new possibilistic approach of single word translation. Indeed, we take into account all query words and their translations when we choose the suitable translation of a given word. We start from the idea that the correct suitable translations of query terms have a tendency to co-occur in the target language documents unlike unsuitable ones. Finally, to increase the coverage of the bilingual dictionary, additional words and their translations are automatically generated from a parallel bilingual corpus. We tested our approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. The reported experiments showed the performance of the probability-to-possibility transformation-based approach compared to the probabilistic one and to some state-of-the-art CLIR tools.

Query Translation and Quran Result in TreeMap

This paper presents the view of TreeMap representation with retrieval results more attractive on eyes. It is also found that TreeMap more friendly to present Quran results for keyword (K) and queryword (Q). This research also is being applied in retrieving Malay verses Quran translation.

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.