Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising

A Comparative Study of Code Query Technologies

When analyzing software systems we are faced with the challenge of how to implement a particular analysis for different programming languages. A solution for this problem is to write a single analysis using a code query language abstracting from the specificities of languages being analyzed. Over the past ten years many code query technologies have been developed, based on different formalisms. Each technology comes with its own query language and set of features. To determine the state of the art of code querying we compare the languages and tools for seven code query technologies: Grok, Rscript, JRelCal, SemmleCode, JGraLab, CrocoPat and JTransformer. The specification of a package stability metric is used as a running example to compare the languages. The comparison involves twelve criteria, some of which are concerned with properties of the query language (paradigm, types, parametrization, polymorphism, modularity, and libraries), and some of which are concerned with the tool itself (output formats, interactive interface, API support, interchange formats, extraction support, and licensing). We contextualize the criteria in two usage scenarios: interactive and tool integration. We conclude that there is no particularly weak or dominant tool. As important improvement points, we identify the lack of library mechanisms, interchange formats, and possibilities for integration with source code extraction components

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

Towards Conceptual and Logical Modelling of NoSQL Databases

NoSQL databases support the ability to handle large volumes of data in the absence of an explicit data schema. On the other hand, schema information is sometimes essential for applications during data retrieval. Consequently, there are approaches to schema construction in, e.g., the JSON DB and graph DB communities. The difference between a conceptual and database schema is often vague in this case. We use functional constructs – typed attributes for a conceptual view of DB that provide a sufficiently structured approach for expressing semantics of document and graph data. Attribute names are natural language expressions. Such typed functional data objects can be manipulated by terms of a typed λ-calculus, providing powerful nonprocedural query features for considered data structures. The calculus is extendible. Logical, arithmetic, and aggregation functions can be included there. Conceptual and database modelling merge in this case

Implementing OBDA for an end-user query answering service on an educational ontology

In the age where productivity of society is no longer defined by the amount of information generated, but from the quality and assertiveness that a set of data may potentially hold, the right questions to do depends on the semantic awareness capability that an information system could evolve into. To address this challenge, in the last decade, exhaustive research has been done in the Ontology Based Data Access (OBDA) paradigm. A conspectus of the most promising technologies with data integration capabilities and the foundations where they rely are documented in this memory as a point of reference for choosing tools that supports the incorporation of a conceptual model under a OBDA method. The present study provides a practical approach for implementing an ontology based data access service, to educational context users of a Learning Analytics initiative, by means of allowing them to formulate intuitive enquiries with a familiar domain terminology on top of a Learning Management System. The ontology used was completely transformed to semantic linked data standards and some data mappings for testing were included. Semantic Linked Data technologies exposed in this document may exert modernization to environments in which object oriented and relational paradigms may propagate heterogeneous and contradictory requirements. Finally, to validate the implementation, a set of queries were constructed emulating the most relevant dynamics of the model regarding the dataset nature

Dynamically typed languages.

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features