Multi-model Database Research Articles

ANALYSIS OF MODERN DATABASES

In the current digital landscape, handling the vast and varied data requires customized processing and storage methods. Within a system, data within the same domain often uses different models, underscoring the importance of multi-model databases that can accommodate diverse data structures. These databases typically differentiate between primary and secondary models, necessitating clear data schema mappings. While the traditional relational data model persists as a well-studied and widely used approach, graph data models are gaining traction in areas like social networks, recommendation systems, and transportation networks. This article offers insights into the pros and cons of graph databases, providing a clearer understanding of their role in modern data management. Keywords: Graph databases, Database architecture, Graph-based data management, Multi-model DBMS, Relational databases.

An approach to on-demand extension of multidimensional cubes in multi-model settings: Application to IoT-based agro-ecology

Managing unstructured and heterogeneous data, integrating them, and enabling their analysis are among the key challenges in data ecosystems, together with the need to accommodate a progressive growth in these systems by seamlessly supporting extensibility. This is particularly relevant for OLAP analyses on multidimensional cubes stored in data warehouses (DWs), which naturally span large portions of heterogeneous data, possibly relying on different data models (relational, document-based, graph-based). While the management of model heterogeneity in DWs, using for instance multi-model databases, has already been investigated, not much has been done to support extensibility. In a previous paper we have investigated a schema-on-read scenario aimed at granting the extensibility of multidimensional cubes by proposing an architecture to support it and discussing the main open issues associated. This paper takes a step further by presenting xCube, an approach to provide on-demand extensibility of multidimensional cubes in a supply-driven fashion. xCube lets users choose a multidimensional element to be extended, using additional data, possibly uploaded from a data lake. Then, the multidimensional schema is extended by considering the functional dependencies implied by these additional data, and the extended multidimensional schema is made available to users for OLAP analyses. After explaining our approach with reference to a motivating case study in agro-ecology, we propose a proof-of-concept implementation using AgensGraph and Mondrian.

Bitcoin Transaction Analysis System

The lack of a user identification system and the existence of a variety of ways to obfuscate a transaction on the Bitcoin network is of great interest to attackers and can be used by them to conduct illegal activities. There is a need to develop new methods of cash identification in the Bitcoin network. The purpose of this work is to develop a method of transaction verification in the Bitcoin network to improve the efficiency of the process of identification of illegally obtained funds and their sources. The work solves the following tasks: the development of a method for transaction verification in the Bitcoin network and the development of a decision support system, which includes the proposed method. The article describes each of the stages of the method: collection, aggregation, processing and analysis of information. The information analysis stage proposes a clustering method that takes into account an extended set of empirical rules (heuristics) of transaction analysis, as well as information about Bitcoin network address owners. The scientific novelty lies in increasing the efficiency of the identification process of illegally obtained funds and their sources through a comprehensive analysis of transactions, including the extended collection of information and its subsequent aggregation in the multi-model database of the decision support system. In contrast to existing methods, the reliability of the identification of Bitcoin network subjects is increased through the use of intelligent methods of data analysis. The results of this work will provide an opportunity to develop new and improve existing transaction analysis tools in future research, which will allow more effective identification of funds in the Bitcoin network associated with illegal activities.

Workload-Aware Performance Tuning for Multimodel Databases Based on Deep Reinforcement Learning

Currently, multimodel databases are widely used in modern applications, but the default configuration often fails to achieve the best performance. How to efficiently manage and tune the performance of multimodel databases is still a problem. Therefore, in this study, we present a configuration parameter tuning tool MMDTune+ for ArangoDB. First, the selection of configuration parameters is based on the random forest algorithm for feature selection. Second, a workload-aware mechanism is based on k-means++ and the Pearson correlation coefficient to detect workload changes and match the empirical knowledge of historically similar workloads. Finally, the ArangoDB configuration parameters are optimized based on the improved TD3 algorithm. The experimental results show that MMDTune+ can recommend higher-quality configuration parameters for ArangoDB compared to OtterTune and CDBTune in different scenarios.

The Philippine springtime (February–April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database

During the first half month of April 2022, the Philippines experienced severe disasters associated with the weak but deadly tropical storm Megi that caused 214 deaths and two sunken ships. This prompted us to investigate the extended-range prediction skill of the springtime Philippine sub-seasonal scale rainfall extremes in the subseasonal-to-seasonal (S2S) prediction database. The results suggest that the S2S models can well predict the extremity of the 2022 springtime sub-seasonal peak rainfall event (SPRE) ten days ahead. In addition to the La Niña sea surface temperature anomalies, this prolonged rainfall event, from March 26 - April 14, 2022, was associated with an anomalous cyclonic circulation straddled over the South China Sea (SCS) and the Philippine Sea and persisted for two weeks. The strong relationship between the El Niño and Southern Oscillation (ENSO) and the springtime (February–April) rainfall variability in the Philippines is clearly revealed in the analysis of 25 years of observational and hindcast data. The extremely wet SPREs tend to occur during the La Niña springs, while the extremely dry SPREs tend to occur during the El Niño springs. The Madden-Julian oscillation (MJO) and equatorial Rossby (ER) waves that are capable of modulating the sub-seasonal rainfall extremes were weak when the deadly SPRE occurred in April 2022. Thus, the extended-range forecast skill of this example can be interpreted as the baseline skill of the current S2S prediction revealed in the multi-model database. The findings suggest that the SPRE is a useful item to be included in the operational forecast as potential opportunities to harness the benefits of S2S prediction and applications.

Multi-model query languages: taming the variety of big data

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.

A Benchmark for Performance Evaluation of a Multi-Model Database vs. Polyglot Persistence

As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a balance between two methods: polyglot persistence and multi-model databases. Moreover, existing studies suggest that current benchmarks are not completely suitable for comparing these two methods, whether in terms of test datasets, workloads, or metrics. To address this issue, the authors introduce MDBench, an end-to-end benchmark tool. Based on the multi-model dataset and proposed workloads, the experiments reveal that ArangoDB is superior at insertion operations of graph data, while the polyglot persistence instance is better at handling the deletion operations of document data. When it comes to multi-thread and associated queries to multiple tables, the polyglot persistence outperforms ArangoDB in both execution time and resource usage. However, ArangoDB has the edge over MongoDB and Neo4j regarding reliability and availability.

M2Bench

As the world becomes increasingly data-centric, the tasks dealt with by a database management system (DBMS) become more complex and diverse. Compared with traditional workloads that typically require only a single data model, modern-day computational tasks often involve multiple data sources and rely on more than one data model. Unfortunately, however, there is currently no standard benchmark program that can evaluate a DBMS in the various aspects of multi-model databases, especially when the array data model is concerned. In this paper, we propose M2Bench , a new benchmark program capable of evaluating a multi-model DBMS that supports several important data models such as relational, document-oriented, property graph, and array models. M2Bench consists of multi-model workloads that are inspired by real-world problems. Each task of the workload mimics a real-life scenario where at least two different models of data are involved. To demonstrate the efficacy of M2Bench , we evaluated polyglot or multi-model database systems with the M2Bench workloads and unfolded the diverse characteristics of the database systems for each data model.

Parameters tuning of multi-model database based on deep reinforcement learning

As we all know, the performance of database management system is directly linked to a vast array of knobs, which control various aspects of system operation, ranging from memory and thread counts settings to I/O optimization. Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.

Optimising Health Emergency Resource Management from Multi-Model Databases

The health care sector is one of the most sensitive sectors in our society, and it is believed that the application of specific and detailed database creation and design techniques can improve the quality of patient care. In this sense, better management of emergency resources should be achieved. The development of a methodology to manage and integrate a set of data from multiple sources into a centralised database, which ensures a high quality emergency health service, is a challenge. The high level of interrelation between all of the variables related to patient care will allow one to analyse and make the right strategic decisions about the type of care that will be needed in the future, efficiently managing the resources involved in such care. An optimised database was designed that integrated and related all aspects that directly and indirectly affected the emergency care provided in the province of Jaén (city of Jaén, Andalusia, Spain) over the last eight years. Health, social, economic, environmental, and geographical information related to each of these emergency services was stored and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. Predictive models of emergency demand were generated with a success rate of over 90%.

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests.

A universal approach for multi-model schema inference

The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

РОЗРОБКА ТА СУПРОВОДЖЕННЯ СИСТЕМ ІЗ РОЗПОДІЛЕНИМИ БАЗАМИ ДАНИХ НА ОСНОВІ ТЕХНОЛОГІЙ POLYGLOT PERSISTENCE

The analysis of modern methods and technologies used for solving the problems of creating and maintaining distributed databases with a multi-variant storage was carried out. Distributed databases with a single mechanism for accessing multi-variate data are singled out. The use of scalability mechanisms in the direction of horizontal scaling (in which a certain number of servers are increased within one system) is considered.The justification for the use of the Brewer'stheorem is given, in relation to restrictions and compromise solutions regard-ing properties: consistency, availability, partition tolerance. In relation to this theory, a formalization of the definition of prop-erties for distributed multimodel databases is presented. The extension to the Brewer'stheorem, which requires a trade-off between delays and consistency when applying a replication, and the principles for basic availability and eventual consistency are taken into account.The ways to create and support a highly loaded e-commerce system with support for distributed databases with multi-variate persistence are provided in the article. Examplesof possible architectural solutions for organizing access in such a system using a mix of relational and NoSQL (such as document, key-value, graph and column storage) are given. Such archi-tectural solution as distributed multi-model DBMS with a single access mechanism is proposed to overcome the problems of accessing databases with multi-variant saving from business logic. The shortcomings for such solution’s modern implementa-tions and ways of its improvement are shown. In particular, it is a support for all known data models and dynamic access to data loaded in one model in the format of another model.This work provides an opportunity to obtain and analyze experimental data at the next stages for the study of distributed multi-model dynamic databases using loading effects to obtain quantitative and qualitative characteristics of basic availability and partition tolerance for a fixed parameter of the eventual consistency for multimodel DBMS with a single access mechanism.

Evolution management in multi-model databases

Following the Gartner predictions, most of the DBMSs, both traditional relational and NoSQL, have become multi-model. However, this functionality brought on plenty of related issues. One of the most complex ones is evolution management and respective propagation of changes to all affected parts of the system. In this paper we introduce our prototype implementation called MM-evolver which enables to carry out user-triggered schema modification operations over a multi-model database, and propagates them across all models. As a novelty, MM-evolver supports both inter- and intra-model schema modification operators. To the best of our knowledge, ours is the first tool addressing evolution management in the world of multi-model databases.

Extended Authorization Policy for Graph-Structured Data

The high increase in the use of graph databases also for business- and privacy-critical applications demands for a sophisticated, flexible, fine-grained authorization and access control (AC) approach. Attribute-based access control (ABAC) supports a fine-grained definition of authorization rules and policies. Attributes can be associated with the subject, the requested resource and action, but also the environment. Thus, this is a promising starting point. However, specific characteristics of graph-structured data, such as attributes on vertices and edges along a path from a given subject to the resource to be accessed, are not yet considered. The well-established eXtensible Access Control Markup Language (XACML), which defines a declarative language for fine-grained, attribute-based authorization policies, is the basis for our proposed approach—XACML for Graph-structured data (XACML4G). The additional path-specific constraints, described in graph patterns, demand for specialized processing of the rules and policies as well as adapted enforcement and decision-making in the access control process. To demonstrate XACML4G and its enforcement process, we present a scenario from the university domain. Due to the project’s environment, the prototype is built with the multi-model database ArangoDB. Finally, compliance of XACML4G with quality standards for access control systems administration and enforcement is assessed. The results are promising and further studies concerning performance and use in practice are planned.

Seasonal prediction of the boreal winter stratosphere

The predictability of the Northern Hemisphere stratosphere and its underlying dynamics are investigated in five state-of-the-art seasonal prediction systems from the Copernicus Climate Change Service (C3S) multi-model database. Special attention is devoted to the connection between the stratospheric polar vortex (SPV) and lower-stratosphere wave activity (LSWA). We find that in winter (December to February) dynamical forecasts initialised on the first of November are considerably more skilful than empirical forecasts based on October anomalies. Moreover, the coupling of the SPV with mid-latitude LSWA (i.e., meridional eddy heat flux) is generally well reproduced by the forecast systems, allowing for the identification of a robust link between the predictability of wave activity above the tropopause and the SPV skill. Our results highlight the importance of November-to-February LSWA, in particular in the Eurasian sector, for forecasts of the winter stratosphere. Finally, the role of potential sources of seasonal stratospheric predictability is considered: we find that the C3S multi-model overestimates the stratospheric response to El Niño–Southern Oscillation (ENSO) and underestimates the influence of the Quasi–Biennial Oscillation (QBO).

Multi-Model Database Systems: The State of Affairs

Nowadays, more and more organizations are coping with a very large volume of data in different formats and a very high growth rate. Under these conditions, relational databases are no longer sufficient for data management within these organizations, as has been the case for more than 30 years. Thus, in the last decade and a half, a number of new database models have emerged in order to cover the needs imposed by the storage, processing and analysis of this data, models that are identified today by the generic term NoSQL. In the present article we will present, by means of a quantitative analysis, the state of affairs of the adoption of NoSQL stores in the database management systems that already exist on the market and the convergence of SQL and NoSQL models in unitary multi-model database management systems.

A model of a generic Arabic language interface for multimodel database

The extraction of the information from database systems requires the formulation of queries using database query languages, such as Structured Query Language (SQL). This formulation needs the knowledge of the model and the structure of the database. However, non-expert users cannot write such queries. This is why a lot of works have been developed to query the database in natural language. Historically, most of these works were carried out for English language and they were designed for a specific database model. Some of them function independently of database domain. But, until now there is no system that functions independently of both database model and domain. For the Arabic language, all existing contributions are dependent on database domain and model. This paper presents a generic natural language interface to query databases using the Arabic language. This interface functions independently of the database domain and model (relational and XML). Furthermore, the use of machine learning helps our system to improve its knowledge base automatically through experience.

The Student Study Experience – Analysing Student Study Choices

As higher education institutions increasingly teach online and offer greater levels of choice to students (over which modules to study, in which order to study, and how long to extend study before qualification) new challenges are introduced. One of these challenges is how to maintain an understanding of the student experience. This understanding is necessary to provide feedback to both students and faculty, and institutionally for the continued enhancement of quality. This paper is the first attempt at providing a narrative describing one approach to this challenge and the experience within a large distance learning University. It demonstrates a new approach to data is key to enabling the analysis of student study pathways. For many years, this University has offered great flexibility of study and as wide a study choice as it is possible to offer with conventional modules. By design, the Institution holds high levels of data for all student study. However, whilst it is possible to create bespoke queries, we found that this has been insufficient to readily enable analysis of the student experience. By moving from a traditional relational database structure to a multi-model database, many of the difficulties are resolved. In this paper, we report on this approach and describe next steps, including the potential to apply machine learning algorithms and test other data theories like that of Markov Chains.

Holistic evaluation in multi-model databases benchmarking

A multi-model database (MMDB) is designed to support multiple data models against a single, integrated back-end. Examples of data models include document, graph, relational, and key-value. As more and more platforms are developed to deal with multi-model data, it has become crucial to establish a benchmark for evaluating the performance and usability of MMDBs. In this paper, we propose UniBench, a generic multi-model benchmark for a holistic evaluation of state-of-the-art MMDBs. UniBench consists of a set of mixed data models that mimics a social commerce application, which covers data models including JSON, XML, key-value, tabular, and graph. We propose a three-phase framework to simulate the real-life distributions and develop a multi-model data generator to produce the benchmarking data. Furthermore, in order to generate a comprehensive and unbiased query set, we develop an efficient algorithm to solve a new problem called multi-model parameter curation to judiciously control the query selectivity on diverse models. Finally, the extensive experiments based on the proposed benchmark were performed on four representatives of MMDBs: ArangoDB, OrientDB, AgensGraph and Spark SQL. We provide a comprehensive analysis with respect to internal data representations, multi-model query and transaction processing, and performance results for distributed execution.