Analytical querying with typed linear algebra: integration with MonetDB

Dissertação de mestrado integrado em Informatics EngineeringCurrent digital transformations in society heavily rely on safe, easy-to-use, high-performance data storage and analysis for smart decision taking. This triggered the need for efficient analytical querying solutions and the columnar database model is increasingly regarded as the most efficient model for data organization in large data banks. MonetDB is a pioneer in the column-wise database model and is currently at the forefront of high performance DBMS engine. A Linear Algebra Querying (LAQ) engine, using a columnar database paradigm and strongly inspired on Typed Linear Algebra (TLA), was developed in a former MSc. dissertation, with a prototype Web interface. Performance benchmarking of this engine showed it outperformed conventional referenced DBMS but it failed to beat MonetDB’s performance. This dissertation aims to improve the performance of the LAQ engine by following a different path: instead of a standalone engine, the new approach implements the engine on top of MonetDB extended with RMA (Relational Matrix Algebra) and inspired by the TLA approach. This enables the use of LAQ scripting to replace the main stream relational algebra query language approach given by SQL. Matrix operations commonly used in LAQ/TLA, such as matrix-matrix multiplication, Khatri-Rao product or Hadamard-Schur product, had to be implemented in RMA to shift from the relational algebra paradigm to TLA. A thorough analysis of the MonetDB/RMA showed the need to implement key TLA operators that are not available at the frontend. Such operators were implemented and successfully tested and validated, paving the way to future benchmarking its performance with TPC-H/OLAP queries and consequent fine tuning of the engine.Atualmente, as transformações digitais na sociedade confiam fortemente no armazenamento e na análise de dados seguros, fáceis de usar e de alto desempenho para tomadas de decisão inteligentes. Este facto desencadeou a necessidade de soluções de consultas analíticas eficientes, em que o modelo de bases de dados colunar é cada vez mais considerado o modelo mais eficiente para organização de dados em grandes bancos de dados. MonetDB é um sistema pioneiro no modelo de bases de dados colunar e atualmente está na vanguarda de DBMS’s de alto desempenho. Um motor Linear Algebra Querying (LAQ), que usa o paradigma de bases de dados colunar e fortemente inspirado em Álgebra Linear Tipada (TLA), foi desenvolvido numa antiga dissertação de mestrado em Engenharia Informática. O benchmarking do desempenho deste motor mostrou que supera DBMS tradicionais, mas não conseguiu superar o desempenho do MonetDB. Esta dissertação visa melhorar o desempenho do motor LAQ seguindo um caminho diferente: em vez de um motor autónomo, a nova abordagem implementa o motor sobre o motor do MonetDB estendido com RMA (Álgebra Relacional Matricial) e inspirado na abordagem de TLA. Isto permite o uso de scripts LAQ para substituir a abordagem da linguagem de consulta de álgebra relacional fornecida pelo SQL. Operações de matrizes comumente usadas em LAQ / TLA, como multiplicação de matrizes, produto Khatri-Rao ou produto Hadamard-Schur, tiveram de ser implementadas em RMA para mudar do paradigma da álgebra relacional para TLA. Uma análise completa do MonetDB / RMA mostrou a necessidade de implementar os principais operadores de TLA que não estão disponíveis no front-end. Esses operadores foram implementados, testados e validados com sucesso, abrindo caminho para um futuro benchmarking do seu desempenho com queries TPC-H / OLAP e consequente, ajuste do motor

Programming Languages and Systems

This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

Advances in software, embedded computing, sensors, and networking technologies will lead to a new generation of smart cyber-physical systems that will far exceed the capabilities of today’s embedded systems. They will be entrusted with increasingly complex tasks like controlling electric grids or autonomously driving cars. These systems have the potential to lay the foundations for tomorrow’s critical infrastructures, to form the basis of emerging and future smart services, and to improve the quality of our everyday lives in many areas. In order to solve their tasks, they have to continuously monitor and collect data from physical processes, analyse this data, and make decisions based on it. Making smart decisions requires a deep understanding of the environment, internal state, and the impacts of actions. Such deep understanding relies on efficient data models to organise the sensed data and on advanced analytics. Considering that cyber-physical systems are controlling physical processes, decisions need to be taken very fast. This makes it necessary to analyse data in live, as opposed to conventional batch analytics. However, the complex nature combined with the massive amount of data generated by such systems impose fundamental challenges. While data in the context of cyber-physical systems has some similar characteristics as big data, it holds a particular complexity. This complexity results from the complicated physical phenomena described by this data, which makes it difficult to extract a model able to explain such data and its various multi-layered relationships. Existing solutions fail to provide sustainable mechanisms to analyse such data in live. This dissertation presents a novel approach, named model-driven live analytics. The main contribution of this thesis is a multi-dimensional graph data model that brings raw data, domain knowledge, and machine learning together in a single model, which can drive live analytic processes. This model is continuously updated with the sensed data and can be leveraged by live analytic processes to support decision-making of cyber-physical systems. The presented approach has been developed in collaboration with an industrial partner and, in form of a prototype, applied to the domain of smart grids. The addressed challenges are derived from this collaboration as a response to shortcomings in the current state of the art. More specifically, this dissertation provides solutions for the following challenges: First, data handled by cyber-physical systems is usually dynamic—data in motion as opposed to traditional data at rest—and changes frequently and at different paces. Analysing such data is challenging since data models usually can only represent a snapshot of a system at one specific point in time. A common approach consists in a discretisation, which regularly samples and stores such snapshots at specific timestamps to keep track of the history. Continuously changing data is then represented as a finite sequence of such snapshots. Such data representations would be very inefficient to analyse, since it would require to mine the snapshots, extract a relevant dataset, and finally analyse it. For this problem, this thesis presents a temporal graph data model and storage system, which consider time as a first-class property. A time-relative navigation concept enables to analyse frequently changing data very efficiently. Secondly, making sustainable decisions requires to anticipate what impacts certain actions would have. Considering complex cyber-physical systems, it can come to situations where hundreds or thousands of such hypothetical actions must be explored before a solid decision can be made. Every action leads to an independent alternative from where a set of other actions can be applied and so forth. Finding the sequence of actions that leads to the desired alternative, requires to efficiently create, represent, and analyse many different alternatives. Given that every alternative has its own history, this creates a very high combinatorial complexity of alternatives and histories, which is hard to analyse. To tackle this problem, this dissertation introduces a multi-dimensional graph data model (as an extension of the temporal graph data model) that enables to efficiently represent, store, and analyse many different alternatives in live. Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks these systems typically need to share context information between computational entities. This requires analytic algorithms to reason over distributed data, which is a complex task since it relies on the aggregation and processing of various distributed and constantly changing data. To address this challenge, this dissertation proposes an approach to transparently distribute the presented multi-dimensional graph data model in a peer-to-peer manner and defines a stream processing concept to efficiently handle frequent changes. Fourthly, to meet future needs, cyber-physical systems need to become increasingly intelligent. To make smart decisions, these systems have to continuously refine behavioural models that are known at design time, with what can only be learned from live data. Machine learning algorithms can help to solve this unknown behaviour by extracting commonalities over massive datasets. Nevertheless, searching a coarse-grained common behaviour model can be very inaccurate for cyber-physical systems, which are composed of completely different entities with very different behaviour. For these systems, fine-grained learning can be significantly more accurate. However, modelling, structuring, and synchronising many fine-grained learning units is challenging. To tackle this, this thesis presents an approach to define reusable, chainable, and independently computable fine-grained learning units, which can be modelled together with and on the same level as domain data. This allows to weave machine learning directly into the presented multi-dimensional graph data model. In summary, this thesis provides an efficient multi-dimensional graph data model to enable live analytics of complex, frequently changing, and distributed data of cyber-physical systems. This model can significantly improve data analytics for such systems and empower cyber-physical systems to make smart decisions in live. The presented solutions combine and extend methods from model-driven engineering, [email protected], data analytics, database systems, and machine learning

Transactional and analytical data management on persistent memory

Die zunehmende Anzahl von Smart-Geräten und Sensoren, aber auch die sozialen Medien lassen das Datenvolumen und damit die geforderte Verarbeitungsgeschwindigkeit stetig wachsen. Gleichzeitig müssen viele Anwendungen Daten persistent speichern oder sogar strenge Transaktionsgarantien einhalten. Die neuartige Speichertechnologie Persistent Memory (PMem) mit ihren einzigartigen Eigenschaften scheint ein natürlicher Anwärter zu sein, um diesen Anforderungen effizient nachzukommen. Sie ist im Vergleich zu DRAM skalierbarer, günstiger und dauerhaft. Im Gegensatz zu Disks ist sie deutlich schneller und direkt adressierbar. Daher wird in dieser Dissertation der gezielte Einsatz von PMem untersucht, um den Anforderungen moderner Anwendung gerecht zu werden. Nach der Darlegung der grundlegenden Arbeitsweise von und mit PMem, konzentrieren wir uns primär auf drei Aspekte der Datenverwaltung. Zunächst zerlegen wir mehrere persistente Daten- und Indexstrukturen in ihre zugrundeliegenden Entwurfsprimitive, um Abwägungen für verschiedene Zugriffsmuster aufzuzeigen. So können wir ihre besten Anwendungsfälle und Schwachstellen, aber auch allgemeine Erkenntnisse über das Entwerfen von PMem-basierten Datenstrukturen ermitteln. Zweitens schlagen wir zwei Speicherlayouts vor, die auf analytische Arbeitslasten abzielen und eine effiziente Abfrageausführung auf beliebigen Attributen ermöglichen. Während der erste Ansatz eine verknüpfte Liste von mehrdimensionalen gruppierten Blöcken verwendet, handelt es sich beim zweiten Ansatz um einen mehrdimensionalen Index, der Knoten im DRAM zwischenspeichert. Drittens zeigen wir unter Verwendung der bisherigen Datenstrukturen und Erkenntnisse, wie Datenstrom- und Ereignisverarbeitungssysteme mit transaktionaler Zustandsverwaltung verbessert werden können. Dabei schlagen wir ein neuartiges Transactional Stream Processing (TSP) Modell mit geeigneten Konsistenz- und Nebenläufigkeitsprotokollen vor, die an PMem angepasst sind. Zusammen sollen die diskutierten Aspekte eine Grundlage für die Entwicklung noch ausgereifterer PMem-fähiger Systeme bilden. Gleichzeitig zeigen sie, wie Datenverwaltungsaufgaben PMem ausnutzen können, indem sie neue Anwendungsgebiete erschließen, die Leistung, Skalierbarkeit und Wiederherstellungsgarantien verbessern, die Codekomplexität vereinfachen sowie die ökonomischen und ökologischen Kosten reduzieren.The increasing number of smart devices and sensors, but also social media are causing the volume of data and thus the demanded processing speed to grow steadily. At the same time, many applications need to store data persistently or even comply with strict transactional guarantees. The novel storage technology Persistent Memory (PMem), with its unique properties, seems to be a natural candidate to meet these requirements efficiently. Compared to DRAM, it is more scalable, less expensive, and durable. In contrast to disks, it is significantly faster and directly addressable. Therefore, this dissertation investigates the deliberate employment of PMem to fit the needs of modern applications. After presenting the fundamental work of and with PMem, we focus primarily on three aspects of data management. First, we disassemble several persistent data and index structures into their underlying design primitives to reveal the trade-offs for various access patterns. It allows us to identify their best use cases and vulnerabilities but also to gain general insights into the design of PMem-based data structures. Second, we propose two storage layouts that target analytical workloads and enable an efficient query execution on arbitrary attributes. While the first approach employs a linked list of multi-dimensional clustered blocks that potentially span several storage layers, the second approach is a multi-dimensional index that caches nodes in DRAM. Third, we show how to improve stream and event processing systems involving transactional state management using the preceding data structures and insights. In this context, we propose a novel Transactional Stream Processing (TSP) model with appropriate consistency and concurrency protocols adapted to PMem. Together, the discussed aspects are intended to provide a foundation for developing even more sophisticated PMemenabled systems. At the same time, they show how data management tasks can take advantage of PMem by opening up new application domains, improving performance, scalability, and recovery guarantees, simplifying code complexity, plus reducing economic and environmental costs

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 24th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 28 regular papers presented in this volume were carefully reviewed and selected from 88 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

Formal Verification of the Variational Database Management System

Variation in data is abundant and ubiquitous in real-world applications. Managing variation in databases is, however, difficult and has been extensively studied by the database community. Schema evolution, data integration, and database versioning are examples of well-studied forms of database variation with effective context-specific solutions. However, variation appears in different forms and contexts in databases, and existing approaches cannot be generalized to handle arbitrary forms of variation irrespective of the context. Moreover, in practice, different forms of variation intersect in a particular context. Variational databases (VDB) provide a fundamental solution to variation management by explicitly encoding variation into relational databases that allows addressing different kinds of variation simultaneously. To support expressing variation in information need, traditional relational algebra (RA) is extended to variational relational algebra (VRA). VRA comes with a static type system that checks the validity of variational queries written in VRA. This thesis extends the formalization and formally verifies properties of the variational database management system (VDBMS). Variational sets and set operations definitions are formally verified and VDBs are formally encoded using them. Then, the correctness of the VRA type system with respect to the RA type system is formally specified and verified. VDBMS also allows writing variational queries with-out repeating variations that are already encoded in the VDB and sub-queries. These implicitly annotated v-queries get explicitly annotated by the system. Therefore, this thesis further formally verifies the process of explicitly annotating variational queries

“The Bard meets the Doctor” – Computergestützte Identifikation intertextueller Shakespearebezüge in der Science Fiction-Serie Dr. Who.

A single abstract from the DHd-2019 Book of Abstracts.Sofern eine editorische Arbeit an dieser Publikation stattgefunden hat, dann bestand diese aus der Eliminierung von Bindestrichen in Überschriften, die aufgrund fehlerhafter Silbentrennung entstanden sind, der Vereinheitlichung von Namen der Autor*innen in das Schema "Nachname, Vorname" und/oder der Trennung von Überschrift und Unterüberschrift durch die Setzung eines Punktes, sofern notwendig