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... There are multiple recent Python libraries that provide access to knowledge graphs through a SPARQL endpoint over HTTP. Examples include pysparql, 14 sparqlclient, 15 and AllegroGraph Python client. 16 However, all these libraries solve a very different (and simpler) problem compared to RDFFrames: they take a SPARQL query written by the user and handle sending this query to the endpoint and receiving results. ...

... Let D = (C , R ) be the output RDFFrame. In addition, let , ⟕, ⟖, ⟗, σ , π , ρ, and γ be the inner join, left outer join, right outer join, full outer join, selection, projection, renaming, and groupingwith-aggregation relational operators, respectively, defined using bag semantics as in typical relational databases [15]. ...

Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

... Second, based on their example (in view of the fact that databases with UBR can be used as an ordinary database, as a data warehouse for various subject domains (SDs), or as a configuration database of the dataspace management environment [7]), when applying certain new approaches, it becomes possible to develop a holistic solution that ensures the security of databases and data warehouses. Separate elements of such a solution can be used to protect databases and data warehouses with various models (relational, NoSQL, and NewSQL [8][9][10][11][12][13][14]) as well. All of this is important for the scientific community. ...

... However, not all integrity constraints could be implemented (thereby contributing to enforcing the requirements of rules C1 and C2) using declarative support. Therefore, along with the means of this way of implementing integrity constraints, procedural support means have found widespread use, such as triggers, stored procedures, and functions (for simplicity, sometimes united by the common name SQL procedures [41]), the mechanisms of which have been significantly expanded in many commercial DBMS in recent years [14,41]. Using procedural support means, the following integrity constraints ( (a) At the same moment in time with one object instance, more than one event of the same class cannot occur; (b) One event that occurs with one object instance can have several subordinate events with different instances of objects occurring at the same time, but the specific event instance that occurs with the object instance of the certain class can have only one "event-owner";  Generation of unique primary key values for schema relations sh R and some others. ...

The objective of the paper was to reveal the main techniques and means of ensuring the integrity of data and persistent stored database modules implemented in accordance with the recommendations of the Clark–Wilson model as a methodological basis for building a system that ensures integrity. The considered database was built according to the schema with the universal basis of relations. The mechanisms developed in the process of researching the problem of ensuring the integrity of the data and programs of such a database were based on the provisions of the relational database theory, the Row Level Security technology, the potential of the modern blockchain model, and the capabilities of the database management system on the platform of which databases with the universal basis of relations are implemented. The implementation of the proposed techniques and means, controlling the integrity of the database of stored elements, prevents their unauthorized modification by authorized subjects and hinders the introduction of changes by unauthorized subjects. As a result, the stored data and programs remain correct, unaltered, undistorted, and preserved. This means that databases built based on a schema with the universal basis of relations and supported by such mechanisms are protected in terms of integrity.

... These measures are based on the provisions of the theory of relational databases [8,30,81], formal access control models [82,83] and ensuring data integrity [84], the potential of the modern blockchain model [85,86], row-level security (RLS) technology [87], SQL capabilities [45]. Separate elements of these solutions can be used to protect databases and data warehouses with various models (relational, NoSQL, NewSQL [12,39,82,[88][89][90][91]). However, in this case, for traditional RDBs, which are investigated below, these measures were not implemented. ...

Obtaining convincing evidence of database security, as the basic corporate resource, is extremely important. However, in order to verify the conclusions about the degree of security, it must be measured. To solve this challenge, the authors of the paper enhanced the Clements–Hoffman model, determined the integral security metric and, on this basis, developed a technique for evaluating the security of relational databases. The essence of improving the Clements–Hoffmann model is to expand it by including a set of object vulnerabilities. Vulnerability is considered as a separate objectively existing category. This makes it possible to evaluate both the likelihood of an unwanted incident and the database security as a whole more adequately. The technique for evaluating the main components of the security barriers and the database security as a whole, proposed by the authors, is based on the theory of fuzzy sets and risk. As an integral metric of database security, the reciprocal of the total residual risk is used, the constituent components of which are presented in the form of certain linguistic variables. In accordance with the developed technique, the authors presented the results of a quantitative evaluation of the effectiveness of the protection of databases built on the basis of the schema with the universal basis of relations and designed in accordance with the traditional technology of relational databases.

... We currently support two modes of execution here: (i) Selinger-style join plans [29] which use dynamic programming to determine the optimal order of relations. (ii) In the presence of a large number of relations, a greedy planner [14] is used which simply determines the cheapest relation to use in each step. • A worst-case optimal query plan as described in [21] is used whenever possible. ...

  • Domagoj Vrgoč
  • Carlos Rojas
  • Renzo Angles Renzo Angles
  • Juan Romero

In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon which a variety of popular graph models can be supported. The engine itself is founded on a combination of tried and tested techniques from relational data management, state-of-the-art algorithms for worst-case-optimal joins, as well as graph-specific algorithms for evaluating path queries. In this paper, we present the main design principles underlying MillenniumDB, describing the abstract graph model and query semantics supported, the concrete data model and query syntax implemented, as well as the storage, indexing, query planning and query evaluation techniques used. We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph, where we find that it outperforms other popular persistent graph database engines (including both enterprise and open source alternatives) that support similar query features.

... We consider only simple references in C 3 . The expression Q has the following form, using the conventional SQL syntax [15]: ...

In this paper, we consider a tree-structured data model used in many commercial databases like Dremel, F1, JSON stores. We define identity and referential constraints within each tree-structured record. The query language is a variant of SQL and flattening is used as an evaluation mechanism. We investigate querying in the presence of these constraints, and point out the challenges that arise from taking them into account during query evaluation.

... Зміст цієї дисципліни охоплює такі розділи математики як: Основи логіки та методи доведення, множини. Комбінаторний аналіз, Теорія графів, Дерева та їх застосування, Відношення, Основи теорії кодування, Булеві функції, Мови, граматики й автомати, Основи теорії алгоритмів, Комбінаторні задачі та складність обчислень [156,343,453,550,554,567,569,605,614]. ...

... Information databases in the conditions of an actual enterprise can be the first, but very important step in the generalization and control of information about the actual state of the BS for further decision-making procedures on their operation and maintenance. The methodology for creating a database is quite fully developed in the works [4,5,6]. The database is the basis for further development of expert systems -systems involved in the general monitoring of BS structures. ...

  • Dembele Simon Pierre Dembele Simon Pierre

Dans le monde d'aujourd'hui, nous dépendons énormément des équipements numériques pour le travail, le divertissement et le social. Par conséquent, nous sommes obligés de chercher tous les moyens pour économiser l'énergie consommée par leurs composants matériels, logiciels, ainsi que les applications qu'ils utilisent. Les systèmes de gestion de bases de données (SGBDs) deviennent des gouffres énergétiques à cause de l'explosion massive des données qu'ils doivent collecter, traiter et stocker. Le processeur des requêtes constitue l'un des composants le plus énergivore des SGBDs. Il a pour rôle de traiter d'une manière efficace les requêtes. Vu le volume des données et la complexité des requêtes d'analyse, l'étude de l'efficacité énergétique de ce composant devient sans aucun doute une question cruciale et urgente. Cette importance a été largement soulignée dans le rapport de Claremont et cosignée par environ une trentaine de chercheurs, experts, architectes du monde de base de données. Ce point est ensuite repris dans le rapport de Beckman qui place l'efficacité énergétique comme un défi à relever dans le domaine des données massives. La majorité des optimiseurs des requêtes actuels sont conçus pour minimiser les opérations d'entrées-sorties et essayent d'exploiter la RAM autant que possible. En conséquence, ils ignorent généralement les aspects énergétiques. Dans cette thèse, pour optimiser l'énergie consommée par un SGBD, nous proposons une approche orientée logicielle que nous baptisons Auditer l'Énergie – Avant de Déployer, permettant de concevoir des processeurs de requêtes moins énergivores. Cette approche se décline en cinq étapes principales: (1) Audit des composants des processeurs de requêtes afin de comprendre son fonctionnement et déterminer ses paramètres sensibles à l'énergie. (2) Élaboration d'un état de l'art sur les solutions existantes dont l'objectif est double: (i) fournir une feuille de route pour les concepteurs et les étudiants qui souhaitent comprendre les questions liées à l'efficacité énergique dans le monde des bases de données et (ii) aider au développement des modèles énergétiques. (3) Modélisation de l'énergie des processeurs de requêtes. Cette modélisation est réalisée par la définition des modèles de coût mathématique dédiés à estimer la consommation énergétique du système lors de l'exécution des requêtes. (4) Utilisation des techniques d'apprentissage automatique profond pour identifier les valeurs des paramètres sensibles à l'énergie appartenant à la fois aux composants matériels et logiciels. Pour appliquer notre approche, nous avons choisi trois systèmes de traitement des données libres dont deux sont orientés disque: PostgreSQL, MonetDB et Hyrise.(5) Déploiement des modèles validés dans les trois SGBDs pour des études d'estimation et d'optimisation énergétique.

  • Maksim Goman

We consider basic principles of probabilistic queries. Decomposition of a generic probabilistic query with conditioning in SQL-like syntax shows that data comparison operators are the only difference to the deterministic case. Any relational algebra operators presume comparison of attribute values. Probabilistic relational algebra operators are not comparable to deterministic ones due to uncertainty factor – they process distribution functions instead of unit values. We argue that chance constraint is a useful principle to build the basic set of binary probabilistic comparison operators (BPCO), the respective probabilistic relational algebra operators and their query syntax for query language implementations.

1010 19.3.3 Deadlock Prevention by

  • The Ivaits-For ............... Graph

The IVaits-For Graph.................... 1010 19.3.3 Deadlock Prevention by Ordering Elements........ 1012 19.3.4 Detecting Deadlocks by Timestamps............ 1014 19.3.5 Comparison of Deadlock-Alanagenient Methods...... 1016

1018 Offset 572-573 Offset table 580-581, 598 OID See Object identifier OLrlP 1047

  • Distributed ................... Databases

Distributed Databases........................ 1018 Offset 572-573 Offset table 580-581, 598 OID See Object identifier OLrlP 1047.1070-1089

Validation Optimization See Query optimization OQL 423-449, 570 ORDER BY 251-252, 284 Ordering relationship

  • See Opti~nistic
  • Timestamp

Opti~nistic concurrency control See Timestamp, Validation Optimization See Query optimization OQL 423-449, 570 ORDER BY 251-252, 284 Ordering relationship, for LDT 458- 460

987. 1044 Papakonstantinou, Y. 188. 1099 Parallel computing 6-7,775-782.983 Parameter 392, 396-397 Parity bit 548, 552-553 Parse tree 788-789

  • C H Papadimitriou

Papadimitriou, C. H. 987. 1044 Papakonstantinou, Y. 188. 1099 Parallel computing 6-7,775-782.983 Parameter 392, 396-397 Parity bit 548, 552-553 Parse tree 788-789, 810 Parser 713-715. 788-79.5

SQL Query optimization 15, 714-715 See also Plan selectioli Query plan 10

  • Oql See Also Datalog

See also Datalog, OQL, Relational algebra, SQL Query optimization 15, 714-715 See also Plan selectioli Query plan 10, 14

  • B Seeger

Seeger. B. ill S~e k time 519-520. 535, 540 SELECT 240-243,281.410,428,431-432, 789-790

Pushing selections , TIT-0-argument selec- tion Selectivity

  • See
  • Filter

See also Filter, Pushing selections, TIT-0-argument selec- tion Selectivity, of a join 858

785 Shared disk 776. 778 Shared lock 940-942

  • L D Shapiro

Shapiro, L. D. 785 Shared disk 776. 778 Shared lock 940-942. 956

Ordering relationship , for UDT Sort-scan 716-717

  • See Also
  • Order By

See also ORDER BY, Ordering relationship, for UDT Sort-scan 716-717,719,721-722,868

1039 Statement record 386-388 Statement-level trigger 342

  • State

State, of a database 879. 1039 Statement record 386-388 Statement-level trigger 342 Statistics 13, 836, 839-810