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Making predictions bounded by uncertainty is a crucial component of modern decision support systems. Knowledge graphs provide a structured, semantic-oriented approach to data integration, storage and retrieval; however, they represent data in an absolute way and do not natively support reasoning under uncertain future conditions. In this paper, we present a novel technique for building a probabilistic model from a knowledge graph using a Bayesian network, which is a step towards enabling probabilistic reasoning under uncertainty within a knowledge graph. Our approach supports mixing of continuously and discretely-distributed variables, which is necessary for jointly processing real-world data, but has not typically been supported in prior work. As an interface to the probabilistic model, we propose an extension of the SPARQL query language called Orion DSL, which is currently a working prototype. We also define a custom probabilistic ontology in order to store outputs of the model directly in the knowledge graph alongside the original data, which we refer to as a Probabilistic Knowledge Graph (PKG). The evaluation shows that: (1) the dependencies and distributions of data in a synthetically-generated knowledge graph were accurately captured by the Bayesian model, and (2) an SPARQL query against the PKG to retrieve computed probability distributions was orders of magnitude more performant than a similarly-intentioned query against the base knowledge graph. We anticipate that the models generated by this system will have applications in the areas of predicting missing data, approximate query processing, and utility-based optimization. Future work will involve more detailed explorations of each of these topics as we work towards a PKG-based decision support system. [ABSTRACT FROM AUTHOR]

A critical issue in Big Data management is to address the variety of data–data are produced by disparate sources, presented in various formats, and hence inherently involves multiple data models. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating multi-model data in a single system and querying across them with a unified query language. This article aims to offer a comprehensive survey of a wide range of multi-model query languages of MMDBs. In particular, we first present the SQL-based extensions toward multi-model data, including the standard SQL extensions such as SQL/XML, SQL/JSON, and GQL, and the non-standard SQL extensions such as SQL++ and SPASQL. We then study the manners in which document-based and graph-based query languages can be extended to support multi-model data. We also investigate the query languages that provide native support on multi-model data. Finally, this article provides insights into the open challenges and problems of multi-model query languages. [ABSTRACT FROM AUTHOR]

The Graph Query Language (GraphQL) is a powerful language for application programming interface (API) manipulation in web services. It has been recently introduced as an alternative solution for addressing the limitations of RESTful APIs. This article introduces an automated solution for GraphQL API testing. We present a full framework for automated API testing, from the schema extraction to test case generation. In addition, we consider two kinds of testing: white-box and black-box testing. The white-box testing is performed when the source code of the GraphQL API is available. Our approach is based on evolutionary search. Test cases are evolved to intelligently explore the solution space while maximizing code coverage and fault-finding criteria. The black-box testing does not require access to the source code of the GraphQL API. It is therefore of more general applicability, albeit it has worse performance. In this context, we use a random search to generate GraphQL data. The proposed framework is implemented and integrated into the open source EvoMaster tool. With enabled white-box heuristics (i.e., white-box mode), experiments on 7 open source GraphQL APIs and three search algorithms show statistically significant improvement of the evolutionary approach compared to the baseline random search. In addition, experiments on 31 online GraphQL APIs reveal the ability of the black-box mode to detect real faults. [ABSTRACT FROM AUTHOR]

Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains. [ABSTRACT FROM AUTHOR]

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial semantics. Moreover, there are a wide range of database servers available, and a unilingual NLI model limits its practical usage. In this article, we propose to not only address the spatial domain generalization challenge, but also support multilingual back-end, i.e., supporting different query languages, such as SQL and Prolog. For the challenge of spatial semantics, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of context and effectively resolve the ambiguity given the spatial semantics. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. We also propose to add a prefix symbol to support the multilingual back-end (e.g., query languages). With our spatial comprehension model and symbol injections, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones. [ABSTRACT FROM AUTHOR]

The requirement for data interchange, access, and retrieval from diverse databases. This requirement is getting more and more crucial. However, retrieving this data from a heterogeneous database necessitates juggling many kinds of query languages, data models, and structure. The goal of this project is to create a unified access control approach (UACA), which will be managed by service data object (SDO) technologies and will be based on an agent mechanism for application access to business databases. To create a unified data access control, applications must be submitted through a UACA. To prevent a direct link to the database and application security threats, unified access control isolates applications from databases. Application systems submit a request for identity authentication to the database access control system using the data access protocol of the access control system. Data are finally transmitted to the application system after identity authentication, data gathering, and data transmission. This investigation led to the creation of a client-middle layer, which is also in charge of transformation and optimization, to ensure uniform data access control and access to the heterogeneous database. It also manages system communication using the TCP/IP protocol to guarantee the accuracy and reliability of the connection. SDO data retrieval offers strong adaptability and system compatibility. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]

As an alternative of Internet protocol (IP) multicast, application layer multicast (ALM) is widely used with the advantage of simplicity and flexibility. However, the existing problems of large transmission delay and poor stability limit the application and development. In this article, to solve these problems, an ALM model based on node potential (NP) and topological index (TI) is proposed. The proposed model considers the factors of node capability and node distance in constructing and maintaining multicast tree to reduce transmission delay and increase stability, and thus it improves the application level in realtime multimedia multicast. The computer simulations prove that the proposed model reduces the ALM transmission delay, increases multicast tree stability effectively, and improves the ALM performance, and therefore it is suitable to apply in large-scale real-time multimedia environment. [ABSTRACT FROM AUTHOR]

The advantage of recursive programming is that it is very easy to write and it only requires very few lines of code if done correctly. Structured query language (SQL) is a database language and is used to manipulate data. In Microsoft SQL Server 2000, recursive queries are implemented to retrieve data which is presented in a hierarchical format, but this way has its disadvantages. Common table expression (CTE) construction introduced in Microsoft SQL Server 2005 provides the significant advantage of being able to reference itself to create a recursive CTE. Hierarchical data structures, organizational charts and other parent-child table relationship reports can easily benefit from the use of recursive CTEs. The recursive query is illustrated and implemented on some simple hierarchical data. In addition, one business case study is brought forward and the solution using recursive query based on CTE is shown. At the same time, stored procedures are programmed to do the recursion in SQL. Test results show that recursive queries based on CTEs bring us the chance to create much more complex queries while retaining a much simpler syntax. [ABSTRACT FROM AUTHOR]

This observational study examined the strategies that music students used to locate scores and media items in an academic library's online public access catalog (OPAC). During a usability test, investigators tracked students' search strategies and behaviors, and measured their success in identifying appropriate items. Students experienced briefer, less complicated, and more successful queries for media items than for music scores (the latter of which they struggled to find and properly identify). Class standing, library catalog experience, and prior library instruction had no significant effect on positive outcomes. Searches for music scores were highly sensitive to variations in query wording, and students frequently struggled to revise their searches. [ABSTRACT FROM AUTHOR]

We study the expressive power of the Lara language – a recently proposed unified model for expressing relational and linear algebra operations – both in terms of traditional database query languages and some analytic tasks often performed in machine learning pipelines. Since Lara is parameterized by a set of user-defined functions which allow to transform values in tables, known as extension functions , the exact expressive power of the language depends on how these functions are defined. We start by showing Lara to be expressive complete with respect to a syntactic fragment of relational algebra with aggregation (under the mild assumption that extension functions in Lara can cope with traditional relational algebra operations such as selection and renaming). We then look further into the expressiveness of Lara based on different classes of extension functions, and distinguish two main cases depending on the level of genericity that queries are enforced to satisfy. Under strong genericity assumptions the language cannot express matrix convolution, a very important operation in current machine learning pipelines. This language is also local, and thus cannot express operations such as matrix inverse that exhibit a recursive behavior. For expressing convolution, one can relax the genericity requirement by adding an underlying linear order on the domain. This, however, destroys locality and turns the expressive power of the language much more difficult to understand. In particular, although under complexity assumptions some versions of the resulting language can still not express matrix inverse, a proof of this fact without such assumptions seems challenging to obtain. [ABSTRACT FROM AUTHOR]

Arguments in argumentation systems cannot always be considered as standalone entities, requiring the consideration of the pieces of evidence they rely on. This evidence might have to be retrieved from external sources such as databases or the web, and each attempt to retrieve a piece of evidence comes with an associated cost. Moreover, a piece of evidence may be available in a given scenario but not in others, and this is not known beforehand. As a result, the collection of active arguments (whose entire set of evidence is available) that can be used by the argumentation machinery of the system may vary from one scenario to another. In this work, we consider an Abstract Argumentation Framework with Fallible Evidence that accounts for these issues, and propose a heuristic measure used as part of the acceptability calculus (specifically, for building pruned dialectical trees) with the aim of minimizing the evidence retrieval cost of the arguments involved in the reasoning process. We provide an algorithmic solution that is empirically tested against two baselines and formally show the correctness of our approach. [ABSTRACT FROM AUTHOR]

Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,<). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(<)--first-order logic with sorts for time instants ordered by the built-in precedence relation < and for the domain of individuals--its extension FO(<,=) with the standard congruence predicates t ≤ 0 (mod n), for any fixed n > 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FO(<, ≡)- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC¹, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies. [ABSTRACT FROM AUTHOR]

Summary: Structure Query Language Injection Attack is among the top 10‐security threats that can be used on the web application to cause severe damage or gain unauthorized data access to the application server. Many reports have indicated an average of 64% of global websites are at risk of being attack by SQL injection, and many of the top companies have experienced thousands of attacks attempts through SQL injection. The current trend shows the increasing number of attacks factor as a result of the daily deployment of these applications without security detection and prevention mechanism is placed. To overcome this challenge, researches in academia and industry presented a proposal that automates SQL injection vulnerabilities assessment on the tested application. Current studies show the need to enhance techniques of these proposals to reduce the false alarms. In this study, we propose a component‐based technique to minimize the incidence of inaccurate results, as well as enable the ease of improving the proposed solution. The study uses three costumed applications as tested to evaluate the accuracy of the proposed solution. Each of these testbed consists of several vulnerabilities where the experimental evaluation performs to test the proposed tool. An empirical evaluation is carried out on three vulnerable custom websites to evaluate the effectiveness of the proposed study. The experiment results indicated significant results in terms of high accuracy. On the other hand, the proposed solution also has better capabilities to analyze page response based on four different techniques. Moreover, the proposed solution is the only solution that performs stored procedure attacks SQL and bypass login authentication even if the returned records are limited restriction is applied. [ABSTRACT FROM AUTHOR]

Nowadays time-stamped data is being generated by a variety of applications and stored in a variety of database systems. Depending on the data structure and the importance of the time aspect those database systems may be either general-purpose or time-series oriented. Most popular time-series DBMS use only simple data types for non-time values, whereas generalpurpose databases usually lack specialized query methods designed for time-series aspects. Each of those solutions has some drawbacks as far as data handling is concerned. Extending SQL with built-in time series focused components enables the combination of relational queries with document and time-series-oriented queries. At the same time, the popularity of SQL among data analysts and data scientists would give them the benefit of an easy start. This paper presents such an extension and introduces ChQL, a query language designed to work with both document data and multivariate time series. This language is designed to imitate the syntax of the Python language, so that it could be easily integrated as a library into object-oriented languages such as Python, Javascript, or PHP. The main architectural concept behind the data model for both languages is to use a mixed data model based on both document and time-series storage. This paper is focused on the languages time-series related features and their architectural consequences. [ABSTRACT FROM AUTHOR]

NoSQL databases are gaining more and more popularity and are an important alternative to relational databases. Examples of such databases are Neo4j, MongoDB and ArangoDB. These databases are described in this article, compared with each other, and the performance results for adding an object to the database, deleting an object, searching and populating the database are presented. Results show that the fastest database is MongoDB, except for one measurement of removal. [ABSTRACT FROM AUTHOR]

Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language like SPARQL and knowledge of a specific vocabulary. This paper analyses different frameworks, architectures, or systems that perform SQA and classifies SQA systems based on different criteria. [ABSTRACT FROM AUTHOR]

Graph databases have received much attention as of late due to numerous applications in which data is naturally viewed as a graph; these include social networks, RDF and the Semantic Web, biological databases, and many others. There are many proposals for query languages for graph databases that mainly fall into two categories. One views graphs as a particular kind of relational data and uses traditional relational mechanisms for querying. The other concentrates on querying the topology of the graph. These approaches, however, lack the ability to combine data and topology, which would allow queries asking how data changes along paths and patterns enveloping it. In this article, we present a comprehensive study of languages that enable such combination of data and topology querying. These languages come in two flavors. The first follows the standard approach of path queries, which specify how labels of edges change along a path, but now we extend them with ways of specifying how both labels and data change. From the complexity point of view, the right type of formalisms are subclasses of register automata. These, however, are not well suited for querying. To overcome this, we develop several types of extended regular expressions to specify paths with data and study their querying power and complexity. The second approach adopts the popular XML language XPath and extends it from XML documents to graphs. Depending on the exact set of allowed features, we have a family of languages, and our study shows that it includes efficient and highly expressive formalisms for querying both the structure of the data and the data itself. [ABSTRACT FROM AUTHOR]

The evaluation of SPARQL algebra queries on various kinds of annotated RDF graphs can be seen as a particular case of the evaluation of these queries on RDF graphs annotated with elements of so-called spm-semirings. Spm-semirings extend semirings, used for representing the provenance of positive relational algebra queries on annotated relational data, with a new operator to capture the semantics of the nonmonotone SPARQL operators. Furthermore, spm-semiring-based annotations ensure that desired SPARQL query equivalences hold when querying annotated RDF. In this work, in addition to introducing spm-semirings, we study their properties and provide an alternative characterization of these structures in terms of semirings with an embedded boolean algebra (or seba-structure for short). This characterization allows us to construct spm-semirings and identify a universal object in the class of spm-semirings. Finally, we show that this universal object provides a provenance representation of poly-sized overhead and can be used to evaluate SPARQL queries on arbitrary spm-semiring-annotated RDF graphs. [ABSTRACT FROM AUTHOR]

The "Internet of Water" project will deploy 2,500 sensors along the Flemish river system, in Belgium. These sensors will be part of a monitoring system. This will produce an enormous amount of data, on which prediction and analysis tasks can be performed. To represent, store, and query river data, relational databases are normally used. However, this choice introduces an "impedance mismatch" between the conceptual representation (typically a graph) and the storage model (relational tables). To solve this problem, this article proposes to use graph databases. The Flemish river system is presented as a use case and the Neo4j graph database and its high‐level query language, Cypher, are used for storing and querying the data, respectively. A relational alternative is implemented over the PostgreSQL database. A collection of representative queries of interest for hydrologists is defined over both database implementations. [ABSTRACT FROM AUTHOR]

The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many different databases. One approach to improving this situation is to allow users to pose their queries in natural language. In this work we describe a machine learning framework, Polyglotter, that in a general way supports the mapping of natural language searches to database queries. Importantly, it does not require the creation of manually annotated data for training and therefore can be applied easily to multiple domains. The framework is polyglot in the sense that it supports multiple different database engines that are accessed with a variety of query languages, including SQL and Cypher. Furthermore Polyglotter supports multi-class queries. Good performance is achieved on both toy and real databases, as well as a human-annotated WikiSQL query set. Thus Polyglotter may help database maintainers make their resources more accessible. [ABSTRACT FROM AUTHOR]

In data management, new data storage models and technologies are gradually adopted, including graph-based data models that have been enabled by technologies such as RDF, GraphQL and LPG. These are particularly fit when dealing with the coexistence of multitude and heterogeneous data sources that require an integration architecture in a data-centric organization due to intrinsic data connectedness. Two prominent approaches are distinguished in our study: (1) embracing a unifying graph format, where, following a series of transformations, all data sources are lifted to a common format in a consolidated graph repository, (2) creating a mediator tier where legacy data sources hold their data but are accessed through a virtual graph integration layer. Each approach comes with a specific way to query data and a plethora of query languages were created for this purpose. In the context of a data integration project we investigated two approaches to data retrieval and data modeling that have gained momentum during recent years: RDF graphs holding data lifted from non-graph data sources and GraphQL acting as a proxy for retrieving virtually connected data from heterogeneous sources. The paper reports on comparative experiments with technological instantiations of the two approaches, which can inform further IT strategies in an institutional project advocating a migration from app-centricity towards data connectedness. [ABSTRACT FROM AUTHOR]