SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things

Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system

INVESTIGATING THE STAR SCHEMA BENCHMARK AS A REPLACEMENT FOR THE TPC-H DECISION SUPPORT SYSTEM

Decision Support System (DSS) are at the core of business intelligence systems. Implementation costs for enterprise level Database Management System (DBMS) and DSS average $10,461 for installation costs. This does not include costs associated with database migrations or testing, which can double the cost, nor does this quoted price include the cost of yearly licensing or support agreements. Depending on the software vendor, there may be additional costs associated with using an application cluster, logical and virtual partitioning, data guards, and even costs per processor core. It is easy to see how the cost of operating a database server can grow expensive rapidly. Information Technology (IT) decision makers and software architects need the ability to choose a DBMS to suit their application's needs. To choose the correct DBMS solution a comprehensive and adaptive benchmark is needed. This benchmark must be capable of predicting how the performance of a given system will scale, as well as offer an estimation of cost. A problematic benchmark that is unable to accurately predict these values is worthless and leads to costly software decision mistakes. To continue to be successful and remain competitive in a given industry it is important for organizations to know their customers, target and acquire new markets, and look to future trends. This is where database business intelligence and decision support systems become useful. DSS allow users to data mine critical information about their work-flows, sales history and trends and have the data readily available so that they may make informed decisions and plan future growth. Business intelligence tools and decision support systems provide executive officers and members of management, the tools needed to create complex ad-hoc queries and mine important data. Presently, IT decision makers and software engineers use the TPC-H decision sup- port system benchmark as a guide to determining the optimal hardware and database vendor configurations to utilize for their decision support system. The TPC-H benchmark is a popular decision support system benchmark. In recent years, however, TPC-H has become heavily criticized for its many problems. The issues outlined within this thesis can lead IT decision makers to purchase and implement improper hardware and software solutions. This thesis examines the criticisms and issues of the TPC-H benchmark. Utilizing Amazon Web Services cloud computing power, we evaluate the Star Schema Benchmark (SSB), as an alternative to TPC-H. We successfully identify and demonstrate several previously undefined problems in the TPC-H benchmark. Our results conclude that the SSB not only resolves the issues inherent in TPC-H, and should serve as a replacement for TPC-H

Comparative Study Of Implementing The On-Premises and Cloud Business Intelligence On Business Problems In a Multi-National Software Development Company

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays every enterprise wants to be competitive. In the last decade, the data volumes are increased dramatically. As each year data in the market increases, the ability to extract, analyze and manage the data become the backbone condition for the organization to be competitive. In this condition, organizations need to adapt their technologies to the new business reality in order to be competitive and provide new solutions that meet new requests. Business Intelligence by the main definition is the ability to extract analyze and manage the data through which an organization gain a competitive advantage. Before using this approach, it’s important to decide on which computing system it will base on, considering the volume of data, business context of the organization and technologies requirements of the market. In the last 10 years, the popularity of cloud computing increased and divided the computing Systems into On-Premises and cloud. The cloud benefits are based on providing scalability, availability and fewer costs. On another hand, traditional On-Premises provides independence of software configuration, control over data and high security. The final decision as to which computing paradigm to follow in the organization it’s not an easy task as well as depends on the business context of the organization, and the characteristics of the performance of the current On-Premises systems in business processes. In this case, Business Intelligence functions and requires in-depth analysis in order to understand if cloud computing technologies could better perform in those processes than traditional systems. The objective of this internship is to conduct a comparative study between 2 computing systems in Business Intelligence routine functions. The study will compare the On-Premises Business Intelligence Based on Oracle Architecture with Cloud Business Intelligence based on Google Cloud Services. A comparative study will be conducted through participation in activities and projects in the Business Intelligence department, of a company that develops software digital solutions to serve the telecommunications market for 12 months, as an internship student in the 2nd year of a master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence at Nova Information Management School (NOVA IMS)

ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

Merging Queries in OLTP Workloads

OLTP applications are usually executed by a high number of clients in parallel and are typically faced with high throughput demand as well as a constraint latency requirement for individual statements. In enterprise scenarios, they often face the challenge to deal with overload spikes resulting from events such as Cyber Monday or Black Friday. The traditional solution to prevent running out of resources and thus coping with such spikes is to use a significant over-provisioning of the underlying infrastructure. In this thesis, we analyze real enterprise OLTP workloads with respect to statement types, complexity, and hot-spot statements. Interestingly, our findings reveal that workloads are often read-heavy and comprise similar query patterns, which provides a potential to share work of statements belonging to different transactions. In the past, resource sharing has been extensively studied for OLAP workloads. Naturally, the question arises, why studies mainly focus on OLAP and not on OLTP workloads? At first sight, OLTP queries often consist of simple calculations, such as index look-ups with little sharing potential. In consequence, such queries – due to their short execution time – may not have enough potential for the additional overhead. In addition, OLTP workloads do not only execute read operations but also updates. Therefore, sharing work needs to obey transactional semantics, such as the given isolation level and read-your-own-writes. This thesis presents THE LEVIATHAN, a novel batching scheme for OLTP workloads, an approach for merging read statements within interactively submitted multi-statement transactions consisting of reads and updates. Our main idea is to merge the execution of statements by merging their plans, thus being able to merge the execution of not only complex, but also simple calculations, such as the aforementioned index look-up. We identify mergeable statements by pattern matching of prepared statement plans, which comes with low overhead. For obeying the isolation level properties and providing read-your-own-writes, we first define a formal framework for merging transactions running under a given isolation level and provide insights into a prototypical implementation of merging within a commercial database system. Our experimental evaluation shows that, depending on the isolation level, the load in the system, and the read-share of the workload, an improvement of the transaction throughput by up to a factor of 2.5x is possible without compromising the transactional semantics. Another interesting effect we show is that with our strategy, we can increase the throughput of a real enterprise workload by 20%.:1 INTRODUCTION 1.1 Summary of Contributions 1.2 Outline 2 WORKLOAD ANALYSIS 2.1 Analyzing OLTP Benchmarks 2.1.1 YCSB 2.1.2 TATP 2.1.3 TPC Benchmark Scenarios 2.1.4 Summary 2.2 Analyzing OLTP Workloads from Open Source Projects 2.2.1 Characteristics of Workloads 2.2.2 Summary 2.3 Analyzing Enterprise OLTP Workloads 2.3.1 Overview of Reports about OLTP Workload Characteristics 2.3.2 Analysis of SAP Hybris Workload 2.3.3 Summary 2.4 Conclusion 3 RELATED WORK ON QUERY MERGING 3.1 Merging the Execution of Operators 3.2 Merging the Execution of Subplans 3.3 Merging the Results of Subplans 3.4 Merging the Execution of Full Plans 3.5 Miscellaneous Works on Merging 3.6 Discussion 4 MERGING STATEMENTS IN MULTI STATEMENT TRANSACTIONS 4.1 Overview of Our Approach 4.1.1 Examples 4.1.2 Why Naïve Merging Fails 4.2 THE LEVIATHAN Approach 4.3 Formalizing THE LEVIATHAN Approach 4.3.1 Transaction Theory 4.3.2 Merging Under MVCC 4.4 Merging Reads Under Different Isolation Levels 4.4.1 Read Uncommitted 4.4.2 Read Committed 4.4.3 Repeatable Read 4.4.4 Snapshot Isolation 4.4.5 Serializable 4.4.6 Discussion 4.5 Merging Writes Under Different Isolation Levels 4.5.1 Read Uncommitted 4.5.2 Read Committed 4.5.3 Snapshot Isolation 4.5.4 Serializable 4.5.5 Handling Dependencies 4.5.6 Discussion 5 SYSTEM MODEL 5.1 Definition of the Term “Overload” 5.2 Basic Queuing Model 5.2.1 Option (1): Replacement with a Merger Thread 5.2.2 Option (2): Adding Merger Thread 5.2.3 Using Multiple Merger Threads 5.2.4 Evaluation 5.3 Extended Queue Model 5.3.1 Option (1): Replacement with a Merger Thread 5.3.2 Option (2): Adding Merger Thread 5.3.3 Evaluation 6 IMPLEMENTATION 6.1 Background: SAP HANA 6.2 System Design 6.2.1 Read Committed 6.2.2 Snapshot Isolation 6.3 Merger Component 6.3.1 Overview 6.3.2 Dequeuing 6.3.3 Merging 6.3.4 Sending 6.3.5 Updating MTx State 6.4 Challenges in the Implementation of Merging Writes 6.4.1 SQL String Implementation 6.4.2 Update Count 6.4.3 Error Propagation 6.4.4 Abort and Rollback 7 EVALUATION 7.1 Benchmark Settings 7.2 System Settings 7.2.1 Experiment I: End-to-end Response Time Within a SAP Hybris System 7.2.2 Experiment II: Dequeuing Strategy 7.2.3 Experiment III: Merging Improvement on Different Statement, Transaction and Workload Types 7.2.4 Experiment IV: End-to-End Latency in YCSB 7.2.5 Experiment V: Breakdown of Execution in YCSB 7.2.6 Discussion of System Settings 7.3 Merging in Interactive Transactions 7.3.1 Experiment VI: Merging TATP in Read Uncommitted 7.3.2 Experiment VII: Merging TATP in Read Committed 7.3.3 Experiment VIII: Merging TATP in Snapshot Isolation 7.4 Merging Queries in Stored Procedures Experiment IX: Merging TATP Stored Procedures in Read Committed 7.5 Merging SAP Hybris 7.5.1 Experiment X: CPU-time Breakdown on HANA Components 7.5.2 Experiment XI: Merging Media Query in SAP Hybris 7.5.3 Discussion of our Results in Comparison with Related Work 8 CONCLUSION 8.1 Summary 8.2 Future Research Directions REFERENCES A UML CLASS DIAGRAM

Experimental evaluation of big data querying tools

Nos últimos anos, o termo Big Data tornou-se um tópico bastanta debatido em várias áreas de negócio. Um dos principais desafios relacionados com este conceito é como lidar com o enorme volume e variedade de dados de forma eficiente. Devido à notória complexidade e volume de dados associados ao conceito de Big Data, são necessários mecanismos de consulta eficientes para fins de análise de dados. Motivado pelo rápido desenvolvimento de ferramentas e frameworks para Big Data, há muita discussão sobre ferramentas de consulta e, mais especificamente, quais são as mais apropriadas para necessidades analíticas específica. Esta dissertação descreve e compara as principais características e arquiteturas das seguintes conhecidas ferramentas analíticas para Big Data: Drill, HAWQ, Hive, Impala, Presto e Spark. Para testar o desempenho dessas ferramentas analíticas para Big Data, descrevemos também o processo de preparação, configuração e administração de um Cluster Hadoop para que possamos instalar e utilizar essas ferramentas, tendo um ambiente capaz de avaliar seu desempenho e identificar quais cenários mais adequados à sua utilização. Para realizar esta avaliação, utilizamos os benchmarks TPC-H e TPC-DS, onde os resultados mostraram que as ferramentas de processamento em memória como HAWQ, Impala e Presto apresentam melhores resultados e desempenho em datasets de dimensão baixa e média. No entanto, as ferramentas que apresentaram tempos de execuções mais lentas, especialmente o Hive, parecem apanhar as ferramentas de melhor desempenho quando aumentamos os datasets de referência

Towards Prescriptive Analytics in Cyber-Physical Systems

More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid. In today’s CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the system’s operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken. This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS – which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system. At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (user’s) intention, and actions to change the behaviour (state) of a physical system, respectively. At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS. At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them “inside a database”, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case. In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future.Mehr und mehr wird heute unsere physische Welt überwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. Zukünftige CPS werden voraussichtlich weit komplexer, größer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie – in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, Zuverlässigkeit und Sicherheit bringen – anzutreffen sein. Um ihr volles Potenzial entfalten zu können, müssen CPS unter anderem mit der Unterstützung automatisierter Planungs- und Steuerungsfunktionalität ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren. Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgeführt. Leider ist diese Aufgabe nur schwer zu bewältigen, und es ist für den Menschen schlicht unmöglich, jede Aktion in einem zukünftigen CPS auf Basis der Komplexität, des Umfangs und der Volatilität eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften müssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) Vorschläge der wichtigsten auszuführenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fällenden Entscheidungen. Diese Arbeit beschäftigt sich mit der Planung und Kontrolle im Rahmen großer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur Verfügung. In Anbetracht der Komplexität von CPS leistet diese Arbeit Beiträge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems. Auf CPS-Ebene umfassen die Beiträge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und Fähigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verändern. Auf Agenten-Ebene umfassen die Beiträge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren Unterstützung der Funktionalität von PrescriptiveCPS entwickelt wurden. Der Schwerpunkt dieser Arbeit bilden die Beiträge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas für die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines Flexibilitätsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-ähnlichen Anfragen zu formulieren und sie „in einer Datenbank zu lösen“, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische Benutzeroberfläche (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. Darüber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfähige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer „What-if“-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der Beiträge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall. Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der Energiedomäne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese Beiträge einen wesentlichen Schritt in der zukünftigen Entwicklung planender CPS darstellen.Mere og mere af vores fysiske verden bliver overvåget og kontrolleret af såkaldte cyber-fysiske systemer (CPSer). Disse er sammensætninger af netværksbaserede autonome IT (cyber) og fysiske (physical) agenter, såsom sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis små og meget begrænsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at være langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en række områder såsom transport, medicin, produktion og energi, hvor de vil give mange fordele, såsom øget effektivitet, bæredygtighed, pålidelighed og sikkerhed. For at frigøre CPSernes fulde potentiale, skal de bl.a. udstyres med støtte til automatiseret planlægning og kontrol, hvor beregningsagenter i samspil og løbende planlægger og styrer deres handlinger på en intelligent og velkoordineret måde for at sikre og optimere en fysisk proces, såsom elforsyningen i elnettet. I nuværende CPSer er styringen typisk automatiseret, mens planlægningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlægge hver handling i et fremtidigt CPS på grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. På grund af disse egenskaber, skal kontrol og planlægning være kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjælp af det sæt af værktøjer, der understøtter præskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at få forslagene fra de mest fremtrædende sæt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sådanne handlinger blev udført. Denne afhandling omhandler planlægning og kontrol i forbindelse med store multi-agent CPSer. Baseret på en smart-grid use case, præsenterer afhandlingen det såkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nær-realtid og leverer (menneskelige) brugere præskriptiveanalyseværktøjer til at analysere og håndtere det underliggende fysiske system (eller proces). I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til følgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS, (2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system. På CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her består IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af såkaldte fleksibilitet (flexibility), beslutning (decision) og præskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer på fremtiden, og repræsenterer en kapacitet, en (brugers) intention, og måder til at ændre adfærd (tilstand) af et fysisk system. På agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understøtte funktionaliteten af PrescriptiveCPS. Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og præskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af præskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemløsning (optimization problem solving) der gør det muligt at formulere optimeringsproblemer ved hjælp af SQL-lignende forespørgsler og at løse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og præskriptive modeller under (bløde eller hårde) tidsbegrænsninger, og (5) en grafisk brugergrænseflade (GUI) til visuelt at analysere fleksibilitet og præskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domæne-specifikke og in-DBMS generiske prognosemetoder der gør det muligt at forudsige instanser af fleksibilitet modeller baseret på historiske data, og (2) kraftfulde måder at analysere tidligere-, nutids- og fremtidsbaserede såkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og præskriptive modelinstanser gemt i en database. De fleste af bidragene på dette niveau er baseret på et smart-grid brugsscenarie. Sammenfattende giver afhandlingen (1) modellen for et CPS med planlægningsmulighed, (2) design og eksperimentel evaluering af præskriptive analyse teknikker der gør det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiområdet, der viser, hvordan de indførte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgør et betydeligt skridt i retning af at udvikle CPSer til planlægningsbrug i fremtiden