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A language FOBS-X (Extensible FOBS) is described. This language is an interpreted language, intended as a universal scripting language. An interesting feature of the language is its ability to be extended, allowing it to be adapted to new scripting environments. The interpretation process is structured as a core-language parser back-end, and a macro processor frontend. The macro processor allows the language syntax to be modified. A configurable library is used to help modify the semantics of the language, adding the required capabilities for interacting in a new scripting environment. This paper focuses on the macro capability of the language. A macro extension to the language has been developed, called the standard extension, that gives FOBS-X a friendlier syntax. It also serves as a convenient tool for demonstrating the macro expansion process.

This paper describes an extensible language framework, ableC, that allows programmers to import new, domain-specific, independently-developed language features into their programming language, in this case C. Most importantly, this framework ensures that the language extensions will automatically compose to form a working translator that does not terminate abnormally. This is possible due to two modular analyses that extension developers can apply to their language extension to check its composability. Specifically, these ensure that the composed concrete syntax specification is non-ambiguous and the composed attribute grammar specifying the semantics is well-defined. This assurance and the expressiveness of the supported extensions is a distinguishing characteristic of the approach. The paper describes a number of techniques for specifying a host language, in this case C at the C11 standard, to make it more amenable to language extension. These include techniques that make additional extensions pass these modular analyses, refactorings of the host language to support a wider range of extensions, and the addition of semantic extension points to support, for example, operator overloading and non-local code transformations.

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.

Context: Syntax is fundamental to any programming language: syntax defines valid programs. In the 1970s, computer scientists rigorously and empirically studied programming languages to guide and inform language design. Since then, language design has been artistic, driven by the aesthetic concerns and intuitions of language architects. Despite recent studies on small sets of selected language features, we lack a comprehensive, quantitative, empirical analysis of how modern, real-world source code exercises the syntax of its programming language. Objective: This study aims to understand how programming language syntax is employed in actual development and explore their potential applications based on the results of syntax usage analysis. Method: We present our results on the first such study on Java, a modern, mature, and widely-used programming language. Our corpus contains over 5000 open-source Java projects, totalling 150 million source lines of code (SLoC). We study both independent (i.e. applications of a single syntax rule) and dependent (i.e. applications of multiple syntax rules) rule usage, and quantify their impact over time and project size. Results: Our study provides detailed quantitative information and yields insight, particularly (i) confirming the conventional wisdom that the usage of syntax rules is Zipfian; (ii) showing that the adoption of new rules and their impact on the usage of pre-existing rules vary significantly over time; and (iii) showing that rule usage is highly contextual. Conclusions: Our findings suggest potential applications across language design, code suggestion and completion, automatic syntactic sugaring, and language restriction.

Language identification is widely used in machine learning, text mining, information retrieval, and speech processing. Available techniques for solving the problem of language identification do require large amount of training text that are not available for under-resourced languages which form the bulk of the World’s languages. The primary objective of this study is to propose a lexicon based algorithm which is able to perform language identification using minimal training data. Because language identification is often the first step in many natural language processing tasks, it is necessary to explore techniques that will perform language identification in the shortest possible time. Hence, the second objective of this research is to study the effect of the proposed algorithm on the run-time performance of language identification. Precision, recall, and F1 measures were used to determine the effectiveness of the proposed word length algorithm using datasets drawn from the Universal Declaration of Human Rights Act in 15 languages. The experimental results show good accuracy on language identification at the document level and at the sentence level based on the available dataset. The improved algorithm also showed significant improvement in run time performance compared with the spelling checker approach.

Natural language has been the holy grail of query interface designers, but has generally been considered too hard to work with, except in limited specific circumstances. In this paper, we describe the architecture of an interactive natural language query interface for relational databases. Through a carefully limited interaction with the user, we are able to correctly interpret complex natural language queries, in a generic manner across a range of domains. By these means, a logically complex English language sentence is correctly translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and can be evaluated against an RDBMS.We have constructed a\ system, NaLIR (Natural Language Interface for Relational databases), embodying these ideas. Our experimental assessment, through user studies, demonstrates that NaLIR is good enough to be usable in practice: even naive users are able to specify quite complex ad-hoc queries.

With the increasing number of multilingual webpages on the Internet, cross-language information retrieval has become an important research issue. In cultures where people speak both Chinese and English, using mixed language in oral speaking and web searching is a common phenomenon. This study employs semi-structured interviews and query log analysis to examine the query reformulation strategies and patterns regarding Chinese-English mixed language queries, and to determine why search engine users utilize certain reformulation strategies. The results can inform information scientists and IR system designers to enhance cross-language controlled vocabularies and develop discovery platforms for multilingual content, and improve search engines to provide users with more relevant results. The findings could also be expanded to other language combinations that would improve search engine designs.

The rapidly increasing demographics of the internet population and the abundance of multilingual content on the web increased the communication in multiple languages. Most of the people use their regional languages to express their needs and the language diversity becomes a great barrier. Cross-Language Information Retrieval (CLIR) provides a solution for that language barrier which allows a user to ask a query in the native language and get the relevant documents in the different language. In this paper, we proposed a Wikipedia API based query translation approach. Queries are tokenized and multi-words query terms are created using N-gram technique. Wikipedia title and inter-wiki link features are exploited for query translation. Target language documents are retrieved using vector space retrieval model and BM25 retrieval algorithm. Experiment results shows that the proposed approach achieves better results without exploiting any language resources.

Developing Natural Language Query Interface to Relational Databases has gained much interest in research community since forty years. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with Natural Language Interfaces (NLIs) is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and also the same word can have multiple meanings. Hence it is the responsibility of the NLI to understand the exact meaning of the word in the particular context. In this paper, a generic NLI Database system has proposed which contains various phases. The exact meaning of the word used in the query in particular context is obtained using ontology constructed for customer database. The proposed system is evaluated using customer database with precision, recall and f1-measure.

We present an object-relational model for uniform handling of dimensional data. Spatial, temporal, spatio-temporal and ordinary data are special cases of dimensional data. The said uniformity is achieved through the concept of dimension alignment, which automatically allows lower dimensional data and queries to be used in a higher dimensional context. Unlike ordinary data, dimensional objects are interwoven. We introduce object identity (oid) fragments to circumvent data redundancy at logical level. Computed types are placed appropriately in a type hierarchy to allow maximal use of existing methods. A query language for spatio-temporal data is presented for associative navigation. A framework for algebraic optimization of the query language is suggested. A pattern matching language is designed for complex querying of spatio-temporal data which seamlessly extends the associative navigation in our query language. The pattern matching language recognizes special features of time and space providing an appropriate level of abstraction for application development compared to traditional languages. This reduces the need for embedding the query language in a lower level language such as C++. The pattern matching language is also dimensionally extensible. The pattern matching allows query of data with multiple granularities and continuous data. It also provides hooks for direct query of scientific data (observations). Our model is dimensionally extensible, and also an extension of a relational model for dimensional data. Moreover the dimensionality and addition of oids are mutually orthogonal concepts. Thus starting from classical ordinary data, one may migrate to higher forms of relational or object-relational data in any sequence, without having to recode application software. Our model does not deal with complex objects, which is left as a future extension.

Knowledge management system (KMS) is an effective tool for knowledge extraction, utilization, and dissemination. When a client or user sends query to the KMS, it is expected to provide response to the query. This paper proposes an automated knowledge provider system (AKPS) that accepts a natural language request of a user in a query–response model where natural language query will be automatically converted to the conceptual form of database. This model is a grammatical rule-based automated model that can extract knowledge from knowledge database resident within the AKPS independently without any manual intervention. The model maps the natural language query into the physical data model, and sends response to the user from a well-organized and well-defined database.

A natural language query builder interface retrieves the required data in structured form from database when query is entered in natural language. The user need not necessarily have sufficient technical knowledge of structured query language statements so nontechnical users can also use this proposed model. In natural language parsing, getting highly accurate syntactic analysis is a crucial step. Parsing of natural languages is the process of mapping an input string or a natural language sentence to its syntactic representation. Constituency parsing approach takes more time for parsing. So, natural language query builder interface is developed in which the parsing of natural language sentence is done by using dependency parsing approach. Dependency parsing technique is widespread in natural language domain because of its state-of-art accuracy and efficiency and also it performs best. In this paper, the buffering scheme is also proposed for natural language statements which will not load the whole sentence if it was done previously. Also there was a need of generalized access to all tables from database which is handled in this system.

retrieval from the database requires knowledge of database language like SQL. Hence people with no knowledge of database language may find it difficult to access the database. Moreover, in recent time there is a growing demand for non- expert users to query relational database in a more natural language encompassing linguistic variables and terms. This triggered the idea of developing natural language interface to database where a user can query the database using natural language. And it relieved the persons from the burden of learning database language like SQL. In this paper, architecture of interface for converting natural language sentence in Hindi into the equivalent SQL query based on Computational Paninian Framework and Karaka theory is proposed. The interface has been evaluated experimentally and is found efficient.

Database management systems (DBMS) have been widely used for storing and retrieving data. However, databases are often hard to use since their interface is quite rigid in cooperating with users. End user is required to issue SQL query to retrieve information from the database. Thus, a user cannot interact directly because of lack of knowledge about the SQL query form, thus is restricted to available application options. To provide the direct access of user on dataset, some unstructured query processor is required. The present work is an interface that will convert the unstructured text query to structured SQL query. It is an intelligent interface formation between the user and the dataset and defined in three main stages namely Descriptive Database Construction, Unstructured Query Filtration and Mapping Stage. The system provide the robustness in terms to handle the broader range of user queries and is be implemented in java environment on Enterprise Employee Database. General Terms Natural Language Processing, Natural Language Interface to Database.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher- level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources.Methods: By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content.Results: The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous.Discussion: An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations.Conclusions: Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

Program querying and analysis tools are of growing importance, and occur in two main variants. Firstly there are source-code query languages which help software engineers to explore a system, or to find code in need of refactoring as coding standards evolve. These also enable language designers to understand the practical uses of language features and idioms over a software corpus. Secondly there are program analysis tools in the style of Coverity which perform deeper program analysis searching for bugs as well as checking adherence to coding standards such as MISRA.The former class are typically implemented on top of relational or deductive databases and make ad-hoc trade-offs between scalability and the amount of source-code detail held-with consequent limitations on the expressiveness of queries. The latter class are more commercially driven and involve more ad-hoc queries over program representations, nonetheless similar pressures encourage user-visible domain-specific languages to specify analyses.We argue that a graph data model and associated query language provides a unifying conceptual model and gives efficient scalable implementation even when storing full source-code detail. It also supports overlays allowing a query DSL to pose queries at a mixture of syntax-tree, type, control-flow-graph or data-flow levels.We describe a prototype source-code query system built on top of Neo4j using its Cypher graph query language; experiments show it scales to multi-million-line programs while also storing full source-code detail. A scalable source-code querying system which stores full source-code detail.Queries on syntax, types and data flow via overlay relations (database views).Based on the graph data model, with prototype ('Wiggle') using Neo4j.

The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model fragment to be returned. In this paper, we derive requirements for a generic model query language from the literature and formally specify the language's syntax and semantics. We conduct an analysis of GMQL's theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research. We describe the language GMQL for querying any graph-based conceptual model.GMQL is applicable to a broad range of model analysis scenarios.GMQL returns query results within (fractions of) milliseconds.The model size and the number of query results influence its runtime performance.GMQL provides features that no other existing query language provides.

The query of database must rely on professional query language, which is difficult to grasp for ordinary users. Natural language query interface (NLQI) can be applied to solve this problem with better human-computer interaction and intelligence. However, current natural language understanding technology is not mature. Though the query sentences’ structure is fixed, it is also difficult to accurately convert query sentences into understandable database query language for computer. Therefore a method of designing NLQI based on syntax and semantic analysis is proposed in this paper. First of all, Maximum forward and maximum reverse matching algorithms are used to analyse sentence by part of speech (POS) template, which is able to demonstrate the syntax relationship among the words. After syntax analysis, query conditions and targets are identified. Then, single conditions and targets are analyzed to relative standard forms by the relationship among entity, property, connect verb and unit. At last, these standard forms are transformed to SQL sentence. Regarding printing ERP system as experimental subject, experiment is done in this paper. The result shows that the method proposed in this paper can meet the needs of natural language query, and has a good transform effect for combination, omitting and other complex sentences.

Nowadays, open data has become more and more popular and is widely used in a variety of service domains. However, in Taiwan, most open datasets are still separate while linked open data (LOD) is still lacking. Without LOD, application developers are difficult to locate and combine appropriate open datasets to create innovative applications. Therefore, to facilitate the consumption and the spreading of open data in Taiwan, we propose a new data query language, referred to as LODQL (Linked Open Data Query Language), to allow the definition of rules for building LOD by data experts and the consumption of LOD via RESTful services by application developers. We designed the LODE (LODQL Engine) to parse the LODQL, build LOD in RDF (Resource Description Framework) according to LODQL, and generate the corresponding RESTful services that extract required LOD based on the user query. LODE is also able to perform data visualization to show relevance of open datasets and relations between open data items. The developed system implementation and the evaluation experiments fully demonstrate that the feasibility and effectiveness of LODQL and LODE.

With the omnipresence of mobile devices coupled with recent advances in automatic speech recognition capabilities, there has been a growing demand for natural language query (NLQ) interface to retrieve information from the knowledge bases. Business users particularly find this useful as NLQ interface enables them to ask questions without the knowledge of the query language or the data schema. In this paper, we apply an existing research technology called ``ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores'' in the industry domain of SAP-ERP systems. The goal is to enable users to query SAP-ERP data using natural language. We present the challenges and their solutions of such a technology transfer. We present the effectiveness of the natural language query interface on a set of questions given by a set of SAP practitioners.

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made.

The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.

The PushGP genetic programming system, which evolves programs expressed in the Push programming language, has been used for a variety of research projects and applications over its sixteen-year history. PushGP relies on an implementation of the Push language in a host language, and it is generally easiest to use PushGP in projects in which most other components, such as fitness functions and data access instructions, are written in the same host language. While versions of Push have been written in nearly a dozen different languages, a full-featured implementation in Python would make it available to a particularly large user base, and facilitate its integration with a wide range of existing data science tools. This paper presents pyshgp as an open-source PushGP framework implemented in the Python programming language, and describes some of its features for data science applications.

We address the task of visualizing machine- readable natural language parser output in a graphical form, convenient for users. While there are numerous available tools implementing similar functionality, their resulting diagrams are typically aimed at language specialists, and are arguably difficult to understand for inexperienced second language learners. In contrast, our system uses a specialized language learning system (WordBricks) as a visualization module, and is deliberately designed to help language learners to understand the structure of natural language sentences. We hope that the resulting software tool (available for the Android mobile platform) will be beneficial for regular use in a variety of language learning scenarios.

It is quite natural to define a software language as an extension of a base language. A compiler builder usually prefers to work on a representation in the base language, while programmers prefer to program in the extended language. As we define a language extension, we want to ensure that desugaring it into the base language is provably sound. We present a lightweight approach to verifying soundness by embedding the base language and its extensions in Haskell. The embedding uses the final tagless style, encoding each language as a type class. As a result, combination and enhancement of language extensions are expressed in a natural way. Soundness of the language extension corresponds to well-typedness of the Haskell terms, so no extra tool but the compiler is needed.

Graphical abstractDisplay Omitted A generic querying solution in the clinical domain.Translation of restricted natural language queries to SQL queries.Resolving the complexity of data extraction from Entity-Attribute-Value data model. PurposeTo elevate the level of care to the community it is essential to provide usable tools for healthcare professionals to extract knowledge from clinical data. In this paper a generic translation algorithm is proposed to translate a restricted natural language query (RNLQ) to a standard query language like SQL (Structured Query Language). MethodsA special purpose clinical data analytics language (CliniDAL) has been introduced which provides scheme of six classes of clinical questioning templates. A translation algorithm is proposed to translate the RNLQ of users to SQL queries based on a similarity-based Top-k algorithm which is used in the mapping process of CliniDAL. Also a two layer rule-based method is used to interpret the temporal expressions of the query, based on the proposed temporal model. The mapping and translation algorithms are generic and thus able to work with clinical databases in three data design models, including Entity-Relationship (ER), Entity-Attribute-Value (EAV) and XML, however it is only implemented for ER and EAV design models in the current work. ResultsIt is easy to compose a RNLQ via CliniDAL's interface in which query terms are automatically mapped to the underlying data models of a Clinical Information System (CIS) with an accuracy of more than 84% and the temporal expressions of the query comprising absolute times, relative times or relative events can be automatically mapped to time entities of the underlying CIS and to normalized temporal comparative values. ConclusionThe proposed solution of CliniDAL using the generic mapping and translation algorithms which is enhanced by a temporal analyzer component provides a simple mechanism for composing RNLQ for extracting knowledge from CISs with different data design models for analytics purposes.

The purpose of this paper is to show the possibilities offered by the use of markup language in the process of programming of the robot. In this paper, the author primarily focused on the problem of description of the objects’ arrangement on the robot’s scene. The XML format is selected among the various possibilities of using the markup language, due to the fact that most of the software for the simulation of manufacturing processes can export the virtual space configuration to the XML file. The XML (Extensible Markup Language) is a formal language that allows the representation of data structures in a way understandable to a human and suitable for machine processing. The other, significant advantage of the XML is that it is platform-independent language. Its structure is more understandable and much easier to learn than synthetic programming language, so it can be quickly acquired and used by a person who is not a programmer. In this paper, the focus has been mainly put on the presentation of the method and on the assumptions used in the examples given for a simple scene setup and simple manipulation tasks.

In this paper we examine opportunities arising from the convergence of two trends in data management: in-memory database systems (imdbs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous 'impedance mismatch' problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like imdbs, but also to use the same querying language to query an application's in-memory collections. The latter offers further transparency to developers as the query language and all data is represented in the data model of the host programming language. However, compared to imdbs, this additional freedom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes. We focus on querying and we leverage query compilation to improve query processing on application objects. We explore different query compilation strategies and study how they improve the performance of query processing over application data. We take C# as the host programming language as it supports language-integrated query through the linq framework. Our techniques deliver significant performance improvements over the default linq implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and imdb-inspired runtimes to provide transparent and highly efficient querying.

Multilingual information is overflowing on internet these days. This increasing diversity of web pages in almost every popular language in the world should enable the user to access information in any language of his choice. But sometimes it is difficult for a user to write her request in a language which she could easily read and understand. This makes cross-language information retrieval (CLIR) and multilingual information retrieval (MLIR) for Web applications a valuable need of the day. It increases the accessibility of web users to retrieve information in any language while post their queries in their native language. The paper critically analyzes the various researchers work in the area of Indian language CLIR. In this paper we also present our prospective prototype for English to Hindi language CLIR. It will also discuss the issues related to the English to Hindi language translation. We had tested 30 queries manually using suggested prototype and found that the precision level is quite good.

The XQuery language was initially developed as an SQL equivalent for XML data, but its roots in functional programming make it also a perfect choice for processing almost any kind of structured and semi-structured data. Apart from standard XML processing, however, advanced language features make it hard to efficiently implement the complete language for large data volumes. This work proposes a novel compilation strategy that provides both flexibility and efficiency to unleash XQuery’s potential as data programming language. It combines the simplicity and versatility of a storage-independent data abstraction with the scalability advantages of set-oriented processing. Expensive iterative sections in a query are unrolled to a pipeline of relational-style operators, which is open for optimized join processing, index use, and parallelization. The remaining aspects of the language are processed in a standard fashion, yet can be compiled anytime to more efficient native operations of the actual runtime environment. This hybrid compilation mechanism yields an efficient and highly flexible query engine that is able to drive any computation from simple XML transformation to complex data analysis, even on non-XML data. Experiments with our prototype and state-of-the-art competitors in classic XML query processing and business analytics over relational data attest the generality and efficiency of the design.

In order to improve data query speed, there are four methods: optimization of SQL (Structured Query Language) statements and reasonable use of index, temporary tables and views are researched. Practice demonstrates these four methods can improve query speed and database performance.

With the popularization of internet, cyber language is used more and more frequently. The network language has formed a unique system of computer language library. So studying of computer language has great significance to the development of the network language. Language library capacity cant be unlimited expansion. To enhance the computer language network function we must use correlation algorithm to simplify the computer language. In this paper, an improved method based on the network language new is proposed. We apply the neural network speech recognition model with multi hierarchy recognition technology to computer language, and put forward TEI model of computer network language identification. We use the VB program to call on the MATLAB software, successfully simplify the complex recognition process of the network language. At the same time, computer network language also has certain reference value for English language learning. It can help learners to avoid the negative effects of network language on the English language.

This paper introduces the LISp-Miner Control Language – a scripting language for the LISp-Miner system, an academic system for knowledge discovery in databases. The main purpose of this language is to provide programmable means to all the features of the LISp-Miner system and mainly to automate the main phases of data mining – from data introduction and preprocessing, formulation of analytical tasks, to discovery of the most interesting patterns. In this sense, the language is a necessary prerequisite for the EverMiner project of data mining automation. Language will serve other purposes too – for an automated verification of the LISp-Miner system functionality before a new version is released and as an educational tool in advanced data mining courses.

A programming language and the database language will be combined one to another in such a way, that an application source program, which a mixture of statements of the host and database languages instead, can be now separated into two source modules, the one on the host language and the other on the data base. By the host standard compiled into object modules. The LINKEP EDITOR of the operating system links those modules into a load module that corresponds to the original mixed program.

Generally, computer system is handled by the Englis h language only. But the person who is unaware of t he English language and structure of query language cannot handle the syste m. This paper proposed a new approach for accessing t he database easily without knowing English. So, the database is accessed with the help of natural languages such as Hindi, Marathi etc. Natural language processing (NLP) is the study of mathematical and cmodeling of various aspects of languag e and the development of a wide range of systems. Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will be able to talk to the computer in their own language, rather than learn a specialized language of computer commands.

This paper studies the application programming interface in configuration software. Based on comprehensively considering softwares application, a mini-message bus configuration languageMMBCL language is designed by using C language, whose compiler and development environment are fulfilled. The designing, syntax analysis, semantic analysis of MMBCL are deeply discussed. This configuration language is simple, similar to C language, easy to write, consistent with industrial control standard. And it has successfully applied to the industrial project and proves the efficiencies of the configuration language and the compiler.

Although robot has been around for many years, the programming language for robot control is still hard for ordinary persons. To promote robot into live of ordinary persons, an easier control language is one of necessity. We need a control language that can be programmed in any words and not limited to English. Forth is one of computer language that can define new commands in any printable characters on keyboard. This study explored eforth, one of kind of Forth, build on ATMEGA328P chip which is used on the world wide popular Ardunino UNO board is called 328eForth system. A 328eForth system has been developed that can be used to control hardware peripherals using Traditional Chinese command. It has simple programming rules, compact system size, and is an open source control language which used in robotic and mechatronic control and education for public.

In order to make the users who didnt study the computer-related major can find the useful information in their familiar manner, it becomes a hot research topic to search in an interactive way using Chinese natural language. But because the complexity of Chinese, how to build of a formal model of natural language is always a difficult research problem. In this paper, by analysis the query sentence type in SQL based on the special database, we bring forward the rules and the context irrespective grammars of the restrictive Chinese that resolves the difficult comprehension problem in natural language processing. And aiming at the comprehension processing, we give the detailed arithmetic describing that can translate a natural language query of the restrictive Chinese to SQL.

HighlightsLanguage composition has not obtained enough attention, is still not well-understood, and associated terminology is confusing.OO techniques and concepts are powerful enough to implement all types of language compositions.Several small examples of different types of language composition are shown using easy to understand DSLs. In this paper, it is shown that inheritance, a core concept from object-oriented programming, is a possible solution for realizing composition of computer languages. Language composability is a property of language descriptions, which can be further classified into informal (language syntax and semantics are hard-coded in compiler/interpreter) and formal language descriptions (syntax and semantics are formally specified with one of several formal methods for language definition). However, language composition is much easier to achieve with declarative formal language descriptions into which the notion of inheritance is introduced. Multiple attribute grammar inheritance, as implemented in the language implementation system LISA, can assist in realizing all of the different types of language compositions identified in Erdweg et al. (2012). Different examples are given throughout the paper using an easy to understand domain-specific language that describes simple robot movement.

In this paper we present the on-going development of a framework for a domain specific, open query language for building information models. The proposed query language is intended for selecting, updating and deleting of data stored in Industry Foundation Classes models. Even though some partial solutions already have been suggested, none of them are open source, domain specific, platform independent and implemented at the same time. This paper provides an overview of existing approaches, conceptual sketches of the language in development and documents the current state of implementation as a prototype plugin developed for the open source model server platform bimserver.org. We report on the execution of example test-cases to show the general feasibility of the approach chosen.

The CloudMdsQL polystore provides integrated access to multiple heterogeneous data stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such as MapReduce or Spark. The CloudMdsQL language is a functional SQL-like query language with a flexible nested data model. A major capability is to exploit the full power of each of the underlying data stores by allowing native queries to be expressed as functions and involved in SQL statements. The CloudMdsQL polystore has been validated with a good number of different data stores: HDFS, key-value, document, graph, RDBMS and OLAP engine. In this paper, we introduce the benchmarking of the CloudMdsQL polystore and evaluate the performance benefits of important features enabled by the query language and engine., This research has been partially funded by the European Commission under projects CoherentPaaS, LeanBigData, and CloudDBAppliance (grants FP7-611068, FP7-619606, and H2020-732051), the Madrid Regional Council, FSE and FEDER, project Cloud4BigData (grant S2013TIC-2894), the Spanish Research Agency MICIN project BigDataPaaS (grant TIN2013-46883), and the Spanish CDTI/MEC NEOTEC program (grant SNEO-20151285)., info:eu-repo/semantics/publishedVersion

The R language, from the point of view of language design and implementation, is a unique combination of various programming language concepts. It has functional characteristics like lazy evaluation of arguments, but also allows expressions to have arbitrary side effects. Many runtime data structures, for example variable scopes and functions, are accessible and can be modified while a program executes. Several different object models allow for structured programming, but the object models can interact in surprising ways with each other and with the base operations of R. R works well in practice, but it is complex, and it is a challenge for language developers trying to improve on the current state-of-the-art, which is the reference implementation -- GNU R. The goal of this work is to demonstrate that, given the right approach and the right set of tools, it is possible to create an implementation of the R language that provides significantly better performance while keeping compatibility with the original implementation. In this paper we describe novel optimizations backed up by aggressive speculation techniques and implemented within FastR, an alternative R language implementation, utilizing Truffle -- a JVM-based language development framework developed at Oracle Labs. We also provide experimental evidence demonstrating effectiveness of these optimizations in comparison with GNU R, as well as Renjin and TERR implementations of the R language.

Making information stored in database accessible for non expert users, has become one of the problems of great interest for the research community of database querying system. Hence for overriding the complexity of using database language such as Structured Query Language (SQL), the using of natural language can be a very important and simple method. But without helps computer cannot understand this language. For that its necessary to develop an interface able to translate natural language query into database query language. In this paper we present the architecture of generic interface for querying database using Arabic language. This interface functions independently of database domain and has the capacity to improve through experience its knowledge base.