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GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

Semi‐structured data are commonly represented by labeled flat db‐graphs. In this paper, we study an extension of db‐graph model for representing nested semi‐structured data. This extension allows one to have db‐graphs whose vertex labels are db‐graphs themselves. Bringing the data model closer to the natural presentation of data stored via Web documents is the main motivation behind nesting db‐graphs. The importance of nested db‐graphs is similar to the importance of nested tables in relational model. The main purpose of the paper is to provide a mechanism to query nested semi‐structured data and Web forms in a uniform way. Most of the languages proposed so far have been designed as extensions of SQL with, among others, the advantage to provide a user‐friendly syntax and commercial flavor. The major focus of the paper is on defining a graph query language in a multi‐sorted calculus like style.

There have been many challenges in crawling deep web by search engines due to their proprietary nature or dynamic content. Distributed Information Retrieval (DIR) tries to solve these problems by providing a unified searchable interface to these databases. Since a DIR must search across many databases, selecting a specific database to search against the user query is challenging. The challenge can be solved if the past queries of the users are considered in selecting collections to search in combination with word embedding techniques. Combining these would aid the best performing collection selection method to speed up retrieval performance of DIR solutions.

The authors propose a collection selection model based on word embedding using Word2Vec approach that learns the similarity between the current and past queries. They used the cosine and transformed cosine similarity models in computing the similarities among queries. The experiment is conducted using three standard TREC testbeds created for federated search.

The results show significant improvements over the baseline models.

Although the lexical matching models for collection selection using similarity based on past queries exist, to the best our knowledge, the proposed work is the first of its kind that uses word embedding for collection selection by learning from past queries.

The semantic and structural heterogeneity of large Extensible Markup Language (XML) digital libraries emphasizes the need of supporting approximate queries, i.e. queries where the matching conditions are relaxed so as to retrieve results that possibly partially satisfy the user's requests. The paper aims to propose a flexible query answering framework which efficiently supports complex approximate queries on XML data.

To reduce the number of relaxations applicable to a query, the paper relies on the specification of user preferences about the types of approximations allowed. A specifically devised index structure which efficiently supports both semantic and structural approximations, according to the specified user preferences, is proposed. Also, a ranking model to quantify approximations in the results is presented.

Personalized queries, on one hand, effectively narrow the space of query reformulations, on the other hand, enhance the user query capabilities with a great deal of flexibility and control over requests. As to the quality of results, the retrieval process considerably benefits because of the presence of user preferences in the queries. Experiments demonstrate the effectiveness and the efficiency of the proposal, as well as its scalability.

Future developments concern the evaluation of the effectiveness of personalization on queries through additional examinations of the effects of the variability of parameters expressing user preferences.

The paper is intended for the research community and proposes a novel query model which incorporates user preferences about query relaxations on large heterogeneous XML data collections.

This paper demonstrates an approach to address the unique control and security concerns in database environments by using audit modules embedded into application programs. Embedded audit modules (EAM) are sections of code built into application programs that capture information of audit significance on a continuous basis. The implementation of EAMs is presented using INGRESS a relational database management system. An interface which enables the auditor to access audit-related information stored in the database is also presented. The use of EAMs as an audit tool for compliance and substantive testing is discussed. Advantages and disadvantages of employing EAMs in database environments and future directions in this line of research are discussed.

There is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.

By introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.

This approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.

This approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues.

In this context the JUMAS system, stem from the homonymous European Project (www.jumasproject.eu), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders.

In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem.

The adoption of ontology‐based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings.

Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.

This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.

By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.

This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

Academic libraries are experiencing numerous changes in their services due to high demands for digital resources and changes in users’ information needs and expectations. Many academic library users give preferences to Google, Google Scholar and other search engines on the internet when they search for information. As reference transactions are decreasing in many academic institutions, this paper aims to investigate the continuing need for mediated reference services in the technology-driven environment in academic libraries.

The authors have conducted a literature review to document and analyze the current trends in reference services in academic libraries. They have examined the relevant published literature through a series of reflective questions to determine whether the demise of mediated reference services is imminent in academic libraries. While this literature review is by no means an exhaustive one, the authors have provided a fairly comprehensive representation of articles to synthesize an overview of the history, evolution, and current trends of reference services in academic libraries.

This paper clearly demonstrates the importance of human-mediated reference services in academic libraries. It reinforces the need for skilled, knowledgeable professional librarians to provide effective and efficient reference services in a digital environment.

This paper provides a comprehensive overview of current trends in reference services in academic libraries and analyzes the merits and demerits of these trends to establish the need for mediated reference services in academic libraries. The arguments used in this paper will be useful for library and informational professionals as validation for the need to hire skilled, knowledgeable reference librarians to provide reference services in a digital environment.

This paper critically looks at the current trends and practices in reference services through the published literature to determine the future need for mediated reference services in academic libraries. It offers important insights to demonstrate why professional librarians’ skills, knowledge and expertise are essential to provide efficient reference services in the digital age.