An improved division operator for relational algebra

The subject of the study is the methods of intellectual analysis, namely the construction of a decision tree, associative analysis, the identification of patterns between related events based on data presented by a relational model. The purpose of the study is to analyze the features of information units and data structures, using the example of relational systems that affect the technology of knowledge extraction. Tasks: the article solves the following tasks: to consider the relational data model as the most popular and effective data structure used in intelligent information systems for data processing and storage; to analyze the operations of relational algebra, the operational component of the relational data model regarding the application of aggregate functions; to develop a general formal statement of the problem of knowledge extraction from a relational database; to consider the concept of functional associative rules; the ID3 decision tree generation algorithm focused on data processing in relational systems is analyzed. The following methods are implemented: modern view and trends in the field of data mining; features of building information systems based on relational databases, relational algebra, theory of normalization of relations; analysis of literature on the topic of research; comparative analysis. Results achieved: the relational data model is considered as the most effective data structure used in intelligent information systems for data processing and storage. A group of aggregate functions of relational databases is identified and analyzed with respect to key attributes of the relation, which makes it possible to build logical dependencies between information units of the subject area being analyzed. The task of extracting knowledge from the database is formally formulated. The concept of functional associative rules is introduced. The ID3 decision tree generation algorithm focused on data processing in relational systems is carefully analyzed. The semantic network (SN), built on the basis of the proposed approach, allows to increase the efficiency of decision support systems. Conclusions: the universal approach proposed in the article to build a relational data model of an information system for searching for associative patterns in data allows to solve a whole class of typical tasks in which objects are connected by a "many-to-many" relationship or M →N. The relational database model is proposed as a universal information structure for solving associative analysis tasks and presenting knowledge in the form of a semantic network. The examples given in the article confirm the effectiveness of the developed and considered approaches to solving the problem of data mining in the environment of relational systems. Solving the problem of identifying knowledge in data will allow to improve the quality of management decisions made.

11 - Relational Languages

The similarity ainong elemcnts emerges naturally as the most adequate to ask about complex data (such as, multimdia and genomic sequenees among others). There are two basie similarity queries: Range Query and k-Nearest Neighbor Query. The increasing volume of complex data stored in Database Management Systems (DBMS), makes it neeessary to provide support for these data tvpes. One way to support complex data types in current DBMS is to include similarity queries in its query processor, and consequently, in the relational algebra. This fact leacls to produce ways to express such queries in the DBMS language as predicates in select operations. As a consequence, the two basic similarity queries cari be combined in more complex expressions involving boolean conjunctions and disjunctions among them, i.e., complex similarity queries. However, for complex similarity queries to be processed efhciently in a DBMS, it is neeessary to provide support in the optimization and runtime laycrs of the; query proeessing. There are many works involving the development, of algorithms to ariswer specific and simple similarity query whereas there is not a generic algorithm efficiently able to handle complex similarity queries. Furthermore, the similarity query optimization is a topic not frequently explored in the literature. This work establishes a structured rnethod 011 how to analyze complex similarity queries. This method is used to extend the relational algebra through algebraic rules and to determine a small set of algorithms that can be used to answer any complex similarity query. In addition, the proposed method makes it possible to formalize rules for selectivity estimai,ion of these. queries thus assisting cost estimation. To validate the concepts presented, exjjeriments are being performed on real and synthetic data sets that highlight meaningful results. The algebraic rules. algorithms and metrics to est-imate the selectivity can be employed in the optimization process of a DBMS in order to derive efficient complex similarity query execution plans. Therefore, this work deals with essential poiuts that enable the practical use of similarity (jueries in Relational Database Management Systems.

Aufgrund fallender Preise zur Speicherung von Daten kann man derzeit eine explosionsartige Zunahme in der Menge der verfügbaren Daten beobachten. Diese Entwicklung gibt Unternehmen und wissenschaftliche Institutionen die Möglichkeit empirische Daten in ungekannter Größenordnung zu analysieren. Für viele Firmen ist die Analyse der gesammelten Daten aus ihrem operationalen Geschäft längst zu einem zentralen strategischen Aspekt geworden. Im Gegensatz zu der seit längerem schon betriebenen Business Intelligence, bestehen diese Analysen nicht mehr nur aus traditionellen relationalen Anfragen. In zunehmendem Anteil kommen komplexe Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus Anfragekomplexität und Datenvolumen gewachsen sind. Relationale Datenbanken waren lange Zeit das Zugpferd der Datenanalyse im großen Stil. Grund dafür war zum großen Teil ihre deklarativen Anfragesprache, welche es ermöglichte die logischen und physischen Aspekte der Datenspeicherung und Verarbeitung zu trennen, und Anfragen automatisch zu optimieren. Das starres Datenmodell und ihre beschränkte Menge von möglichen Operationen schränken jedoch die Anwendbarkeit von relationalen Datenbanken für viele der neueren analytischen Probleme stark ein. Diese Erkenntnis hat die Entwicklung einer neuen Generation von Systemen und Architekturen eingeläutet, die sich durch sehr generische Abstraktionen für parallelisierbare analytische Programme auszeichnen; MapReduce kann hier beispielhaft genannt werden, als der zweifelsohne prominenteste Vertreter dieser Systeme. Zwar vereinfachte und erschloss diese neue Generation von Systemen die Datenanalyse in diversen neuen Anwendungsfeldern, sie ist jedoch nicht in der Lage komplexe Anwendungen aus den Bereichen Data Mining und Maschinelles Lernen effizient abzubilden, ohne sich dabei extrem auf spezifische Anwendungen zu spezialisieren. Verglichen mit den relationalen Datenbanken haben MapReduce und vergleichbare Systeme außerdem die deklarative Abstraktion aufgegeben und zwingen den Anwender dazu systemnahe Programme zu schreiben und diese manuell zu optimieren. In dieser Dissertation werden verschiedene Techniken vorgestellt, die es ermöglichen etliche der zentralen Eigenschaften von relationalen Datenbanken im Kontext dieser neuen Generation von daten-parallelen Analysesystemen zu realisieren. Mithilfe dieser Techniken ist es möglich ein Analysesystem zu beschreiben, dessen Programme gleichzeitig sowohl generische und ausdrucksstark, als auch prägnant und deklarativ sind. Im einzelnen stellen wir folgende Techniken vor: Erstens, eine Programmierabstraktion die generisch ist und mit komplexen Datenmodellen umgehen kann, aber gleichzeitig viele der deklarativen Eigenschaften der relationalen Algebra erhält. Programme, die gegen dies Abstraktion entwickelt werden können ähnlich optimiert werden wie relationale Anfragen. Zweitens stellen wir eine Abstraktion für iterative daten-parallele Algorithmen vor. Die Abstraktion unterstützt inkrementelle (delta-basierte) Berechnungen und geht mit zustandsbehafteteten Berechnungen transparent um. Wir beschreiben wie man einen relationalen Anfrageoptimierer erweitern kann so dass dieser iterative Anfragen effektiv optimiert. Wir zeigen dabei dass der Optimierer dadurch in die Lage versetzt wird automatisch Ausführungspläne zu erzeugen, die wohlbekannten, manuell erstellten Programmen entsprechen. Die Abstraktion subsumiert dadurch spezialisierte Systeme (wie Pregel) und bietet vergleichbare Performanz. Drittens stellen wir Methoden vor, um die Programmierabstraktion in eine funktionale Sprachen einzubetten. Diese Integration ermögliche es prägnante Programme zu schreiben und einfach wiederzuverwendenden Komponenten und Bibliotheken, sowie Domänenspezifische Sprachen, zu erstellen. Wir legen dar wie man die Übersetzung und Optimierung des daten-parallelen Programms mit dem Sprachübersetzer der funktionalen Sprache so integriert, dass maximales Optimierungspotenzial besteht.

Preface: Volume 44, Issue 3

IntroductionWe incorporated patient feedback from human factors studies (HFS) in the patient-centric design and validation of ava®, an electromechanical device (e-Device) for self-injecting the anti-tumor necrosis factor certolizumab pegol (CZP).MethodsHealthcare professionals, caregivers, healthy volunteers, and patients with rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, or Crohn’s disease participated in 11 formative HFS to optimize the e-Device design through intended user feedback; nine studies involved simulated injections. Formative participant questionnaire feedback was collected following e-Device prototype handling. Validation HFS (one EU study and one US study) assessed the safe and effective setup and use of the e-Device using 22 predefined critical tasks. Task outcomes were categorized as “failures” if participants did not succeed within three attempts.ResultsTwo hundred eighty-three participants entered formative (163) and validation (120) HFS; 260 participants performed one or more simulated e-Device self-injections. Design changes following formative HFS included alterations to buttons and the graphical user interface screen. All validation HFS participants completed critical tasks necessary for CZP dose delivery, with minimal critical task failures (12 of 572 critical tasks, 2.1%, in the EU study, and 2 of 5310 critical tasks, less than 0.1%, in the US study).ConclusionCZP e-Device development was guided by intended user feedback through HFS, ensuring the final design addressed patients’ needs. In both validation studies, participants successfully performed all critical tasks, demonstrating safe and effective e-Device self-injections.FundingUCB Pharma.Plain Language SummaryPlain language summary available on the journal website.Electronic supplementary materialThe online version of this article (10.1007/s12325-017-0645-1) contains supplementary material, which is available to authorized users.

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. We start by showing Lara to be expressive complete with respect to first-order logic with aggregation. Since Lara is parameterized by a set of user-defined functions which allow to transform values in tables, the exact expressive power of the language depends on how these functions are defined. We distinguish two main cases depending on the level of genericity queries are enforced to satisfy. Under strong genericity assumptions the language cannot express matrix convolution, a very important operation in current machine learning operations. 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 the resulting language can still not express matrix inverse, a proof of this fact without such assumptions seems challenging to obtain.

RDBI is an educational tool that provides students with the capability to test their understanding of the formal relational query languages (relational algebra, domain relational calculus and tuple relational calculus) and the industry standard query language SQL. Although RDBI is an integral part of the database management courses at a number of universities, it is unavailable to those universities that do not have a license for the software product in which RDBI is implemented. WinRDBI, a version of RDBI for Windows, removes this limitation by increasing the availability of the educational tool to a standard Windows platform. Another advantage of WinRDBI is its graphical user interface, providing the student with a more intuitive interface than the command line interface of RDBI. This paper describes the architecture and user interface of WinRDBI. The features of WinRDBI are also illustrated using nontrivial examples from a popular database text. Although formal relational query languages do not provide inherent support for aggregation, these examples illustrate how to write queries in the formal languages to support (a limited form of) counting and minimum/maximum queries.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

As an extension to the relational algebra, maybe algebra operations have been proposed to handle incomplete information. Such a set of operations allows the user to investigate the potential set of data values (i.e. tuples) to draw his/her own conclusions. However, maybe algebra operations could return nonrelevant data, generate low quality results, and offer low physical performance. Hence, it is appropriate to design a scheme to investigate the results generated by the maybe operations, in order to improve the data quality and performance of large databases. Such a mechanism should be dynamic to adjust itself according to the user's query and the characteristics of the underlying databases. In this paper, an artificial neural network-based decision support system for handling large databases containing incomplete information is proposed. It is a subsystem which learns and constructs a knowledge base to filter out the data that is not of any importance to the user. The network accomplishes the decision-making task in a massively parallel manner. This paper also discusses the implementation of the decision-making network based on the VLSI design of a Basic Neural Unit (BNU). Using a weight-centered design principle, BNU can be expanded and reconfigured to satisfy the requirements of the underlying environment.

Typed query languages for databases containing queries

The center of the algebra of continuous functions on the quantum group \(\mathrm{SU}_q(2)\) is determined as well as centers of other related algebras. Several other results concerning this quantum group are given with direct proofs based on concrete realization of these algebras as algebras of operators on a Hilbert space.

Los actuales sistemas de bases de datos han sido diseñados, principalmente, para dar soporte a aplicaciones de negocios. El éxito del lenguaje de consulta SQL se debe al reducido número de primitivas suficientes para soportar una vasta mayoría de este tipo de aplicaciones. Desafortunadamente, estas primitivas no son suficientes cuando se trata de dar soporte a la emergente familia de nuevas aplicaciones que involucran el descubrimiento de conocimiento en bases de datos. Por esta razón, se propone en este artículo, extender el lenguaje SQL con nuevas primitivas que soporten eficientemente la tarea de minería de datos asociación, al interior de un Sistema Gestor de Bases de Datos(SGBD). Para garantizar la eficiencia de las operaciones de minería de datos, el álgebra relacional se extiende con nuevos operadores algebraicos que se requieren en tareas de Asociación

It is well known that nest and flat operations play an essential role in handling non-first-normal-form (NF/sup 2/) relations. In addition to nest and flat, operations extending original relational algebra have been proposed for NF/sup 2/ relations. The authors study whether those extended relational algebra operations can be expressed as sequences of nest, flat, and original relational algebra operations. In this paper, NFR decomposition and GNFR decomposition are focused on, and conditions for the decompositions are clarified. The discussion is based on an NF/sup 2/ relational model, the nested table data model (NTD). In NTD, data are represented in nested tables (NTs), and algebraic operations named NT operations are provided. Basic properties of nest, flat, and their sequences are clarified. Canonical NTs (CNTs) and normalization lossless NTs (NLNTs) are defined. Conditions for NFR decomposition based on the concept of CNT are shown. It is shown that there is no difference between conditions for NFR decomposition and GNFR decomposition, with the exception that NLNT plays the role of CNT in GNFR decomposition. The extension of such decompositions from XR operations to sequences of NT operations is discussed. >

Nowadays, the growth of the educational sector generates massive data such as details of students, staff and their academic performance, administrative and research details, etc. These data are stored in distributed databases. The parallel database performs parallel query processing hence, the query processing time is reduced and throughput for the transaction processing is improved. The parallel database system also improves the performance by employing parallelism through various database management operations such as loading the data, building indexes, and evaluating queries. The data is partitioned and placed across multiple disks for parallel input and output (I/O) operation to achieve parallelism on queries using pipeline parallelism. The individual relational operations such as sort, join, aggregation, etc. are executed in parallel database by each processor that works independently with its own partition. Queries are expressed in high level language such as structured query language (SQL) that are translated into relational algebra for query processing. Thus, the parallel database increases the throughput in database management and reduces the query processing time. This paper presents a parallel database for student counselling for engineering university admission using MySQL relational database management system.