Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.

O presente trabalho ocupa-se no desenvolvimento de um sistema computacional de gerenciamento da informação para auxiliar os estudos científicos sobre o sistema nervoso de chagásicos crônicos não-cardíacos. O objetivo é desenvolver o sistema requerido, pelo pressuposto de praticidade nas análises decorrentes da investigação. O método utilizado para desenvolver este sistema computacional, dedicado ao gerenciamento das informações da pesquisa sobre as alterações neurológicas de seus sujeitos, foi; compor o arquétipo de metas e a matriz de levantamento de requisitos das variantes do sistema; listar os atributos, domínios e qualificações das suas variáveis; elaborar o quadro de escolha de equipamentos e aplicativos necessários para sua implantação física e lógica e; implantá-lo mediante uma modelagem de base de dados, e uma programação lógica de algoritmos. Como resultado o sistema foi desenvolvido. A discussão de análise é; a saber, que a informatização pode tornar mais eficaz as operações de cadastro, consulta e validação de campo, além da formatação e exportação de tabelas pré-tratadas para análises estatísticas, atuando assim como uma ferramenta do método científico. Ora, a argumentação lógica é que a confiabilidade das informações computacionalmente registradas é aumentada porque o erro humano é diminuído na maioria dos processamentos. Como discussão de cerramento, estudos dotados de razoável volume de variáveis e sujeitos de pesquisa são mais bem geridos caso possuam um sistema dedicado ao gerenciamento de suas informações. / This is the development of a computer information management system to support scientific studies about the nervous system of non-cardiac chronic chagasic patients. The goal is to develop the required system, by assumption of the convenience in the analysis of research results. The method used to develop this computer system, dedicated to information management of research about the neurological disorders of their human subject research, were; compose the archetypal matrix of targets and requirements elicitation of the system variants; list the attributes, qualifications and domains of its variables; draw up the choice framework of equipment and required applications for its physical and logic implementation, and; deploying it through a data modeling, an adapted entity-relationship diagram and programmable logic algorithms. As a result the required system was developed. The analytical discussion is that the computerization makes the data processing faster and safer. The more practical information management processes are: the operations of registration, queries and fields\' validations, as well as the advanced and basic queries of records, in addition to table formatting and exporting of pre-treated for statistical analysis. The logical argument is that the reliability of the recorded computationally information is increased because is insured that bias of human error is absent from most of the steps, including several the data processing operations. As end discussion, scientific studies with reasonable amount of variables and research subjects are better managed if they have a dedicated system to managing their information.

Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.

Database systems build an integral component of today’s software systems and as such they are the central point for storing and sharing a software system’s data while ensuring global data consistency at the same time. Introducing the primitives of roles and their accompanied metatype distinction in modeling and programming languages, results in a novel paradigm of designing, extending, and programming modern software systems. In detail, roles as modeling concept enable a separation of concerns within an entity. Along with its rigid core, an entity may acquire various roles in different contexts during its lifetime and thus, adapts its behavior and structure dynamically during runtime. Unfortunately, database systems, as important component and global consistency provider of such systems, do not keep pace with this trend. The absence of a metatype distinction, in terms of an entity’s separation of concerns, in the database system results in various problems for the software system in general, for the application developers, and finally for the database system itself. In case of relational database systems, these problems are concentrated under the term role-relational impedance mismatch. In particular, the whole software system is designed by using different semantics on various layers. In case of role-based software systems in combination with relational database systems this gap in semantics between applications and the database system increases dramatically. Consequently, the database system cannot directly represent the richer semantics of roles as well as the accompanied consistency constraints. These constraints have to be ensured by the applications and the database system loses its single point of truth characteristic in the software system. As the applications are in charge of guaranteeing global consistency, their development requires more effort in data management. Moreover, the software system’s data management is distributed over several layers, which results in an unstructured software system architecture. To overcome the role-relational impedance mismatch and bring the database system back in its rightful position as single point of truth in a software system, this thesis introduces the novel and tripartite RSQL approach. It combines a novel database model that represents the metatype distinction as first class citizen in a database system, an adapted query language on the database model’s basis, and finally a proper result representation. Precisely, RSQL’s logical database model introduces Dynamic Data Types, to directly represent the separation of concerns within an entity type on the schema level. On the instance level, the database model defines the notion of a Dynamic Tuple that combines an entity with the notion of roles and thus, allows for dynamic structure adaptations during runtime without changing an entity’s overall type. These definitions build the main data structures on which the database system operates. Moreover, formal operators connecting the query language statements with the database model data structures, complete the database model. The query language, as external database system interface, features an individual data definition, data manipulation, and data query language. Their statements directly represent the metatype distinction to address Dynamic Data Types and Dynamic Tuples, respectively. As a consequence of the novel data structures, the query processing of Dynamic Tuples is completely redesigned. As last piece for a complete database integration of a role-based notion and its accompanied metatype distinction, we specify the RSQL Result Net as result representation. It provides a novel result structure and features functionalities to navigate through query results. Finally, we evaluate all three RSQL components in comparison to a relational database system. This assessment clearly demonstrates the benefits of the roles concept’s full database integration.

With the increase of demands for the speed and availability of the information technologies, the process of optimization gains more and more importance. Concerning search engine optimization, optimization of operating systems or application optimization (source code), the goal is to gain faster, smaller and more maintainable solution. In my thesis I deal with optimization of database systems, which includes low level of database tuning - physical organization of data and indices, database management system tuning and query optimization. I focused on optimization of Microsoft SQL Servers 2005 in enterprise environment.

Zenon AB is an it-company of which, this thesis was made in collaboration with. Zenon AB has clients that generate large amounts of data, therefore it is important for Zenon AB that they make competent choices of database management systems (DBMS) when designing systems for their clients. This thesis will therefore entail research carried out into the comparison of DBMS. Nowadays, there exists a large variety of DBMS. Despite this, there seems to be a lack of comparisons between types of DBMS and therefore a lack clarity of when each type should be used. Thus, this thesis aims to highlight these differences of DBMS types by creating a tailored test for each DBMS type and compare how each type performs in each-others area of specialization. This process will show how big the differences can be and highlight the importance of the choice of DBMS. The time it takes, and how simple DBMSs are to implement seems to be a factor most developers take into consideration when choosing DBMS but there is little research on how to compare the aspect. Therefore, this thesis will investigate the viability of a method to compare how easy the DBMSs are to implement into systems by querying programming help forums such as Stackoverflow. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

In a traditional relational database management system, queries can only be defined over attributes defined in the schema, but are guaranteed to give single, definitive answer structured exactly as specified in the query. In contrast, an information retrieval system allows the user to pose queries without knowledge of a schema, but the result will be a top-k list of possible answers, with no guarantees about the structure or content of the retrieved documents. In this paper, we present DrillBeyond, a novel IR/RDBMS hybrid system, in which the user seamlessly queries a relational database together with a large corpus of tables extracted from a web crawl. The system allows full SQL queries over the relational database, but additionally allows the user to use arbitrary additional attributes in the query that need not to be defined in the schema. The system then processes this semi-specified query by computing a top-k list of possible query evaluations, each based on different candidate web data sources, thus mixing properties of RDBMS and IR systems. We design a novel plan operator that encapsulates a web data retrieval and matching system and allows direct integration of such systems into relational query processing. We then present methods for efficiently processing multiple variants of a query, by producing plans that are optimized for large invariant intermediate results that can be reused between multiple query evaluations. We demonstrate the viability of the operator and our optimization strategies by implementing them in PostgreSQL and evaluating on a standard benchmark by adding arbitrary attributes to its queries.

Modern society is intrinsically dependent on the ability to manage data effectively. While relational databases have been the industry standard for the past quarter century, recent growth in data volumes and complexity requires novel data management solutions. These trends revitalized the interest in deductive databases and highlighted the need for column-oriented data storage. However, programming technologies for enterprise computing were designed for the relational data management model (i.e., row-oriented data storage). Therefore, developers cannot easily incorporate emerging data management solutions into enterprise systems. To address the problem above, this thesis presents Deductive Java (DJ), a system that enables enterprise programmers to use a column oriented deductive database in their Java applications. DJ does so without requiring that the programmer become proficient in deductive databases and their non-standardized, vendor-specific APIs. The design of DJ incorporates three novel features: (1) tailoring orthogonal persistence technology to the needs of a deductive database with column-oriented storage; (2) using Java interfaces as a primary mapping construct, thereby simplifying method call interception; (3) providing facilities to deploy light-weight business rules. DJ was developed in partnership with LogicBlox Inc., an Atlanta based technology startup. / Master of Science

Traditional database management systems are designed for general purpose data handling, and fail to work efficiently with time-series data due to characteristics like high volume, rapid ingestion rates, and a focus on temporal relationships. However, what is a best solution is not a trivial question to answer. Hence, this thesis aims to analyze four different Database Management Systems (DBMS) to determine their suitability for managing time series data, with a specific focus on Internet of Things (IoT) applications. The DBMSs examined include PostgreSQL, TimescaleDB, ClickHouse, and InfluxDB. This thesis evaluates query performance across varying dataset sizes and time ranges, as well as the implementational complexity of each DBMS. The benchmarking results indicate that InfluxDB consistently delivers the best performance, though it involves higher implementational complexity and time consumption. ClickHouse emerges as a strong alternative with the second-best performance and the simplest implementation. The thesis also identifies potential biases in benchmarking tools and suggests that TimescaleDB's performance may have been affected by configuration errors. The findings provide significant insights into the performance metrics and implementation challenges of the selected DBMSs. Despite limitations in fully addressing the research questions, this thesis offers a valuable overview of the examined DBMSs in terms of performance and implementational complexity. These results should be considered alongside additional research when selecting a DBMS for time series data. / Traditionella databashanteringssystem är utformade för allmän datahantering och fungerar inte effektivt med tidsseriedata på grund av egenskaper som hög volym, snabba insättningshastigheter och fokus på tidsrelationer. Dock är frågan om vad som är den bästa lösningen inte trivial. Därför syftar denna avhandling till att analysera fyra olika databashanteringssystem (DBMS) för att fastställa deras lämplighet för att hantera tidsseriedata, med ett särskilt fokus på Internet of Things (IoT)-applikationer. De DBMS som undersöks inkluderar PostgreSQL, TimescaleDB, ClickHouse och InfluxDB. Denna avhandling utvärderar sökprestanda över varierande datamängder och tidsintervall, samt implementeringskomplexiteten för varje DBMS. Prestandaresultaten visar att InfluxDB konsekvent levererar den bästa prestandan, men med högre implementeringskomplexitet och tidsåtgång. ClickHouse framstår som ett starkt alternativ med näst bäst prestanda och är enklast att implementera. Studien identifierar också potentiella partiskhet i prestandaverktygen och antyder att TimescaleDB:s prestandaresultat kan ha påverkats av konfigurationsfel. Resultaten ger betydande insikter i prestandamått och implementeringsutmaningar för de utvalda DBMS. Trots begränsningarna i att fullt ut besvara forskningsfrågorna erbjuder studien en värdefull översikt. Dessa resultat bör beaktas tillsammans med ytterligare forskning vid val av ett DBMS för tidsseriedata.

L’irrigation a connu de grands changements dans le bassin du Rhône français durant les trente dernières années du vingtième siècle. La mise en œuvre d’un Système d’Information sur le bassin du Rhône (SIR) montre l’existence de quatre grands systèmes d’irrigation qui s’individualisent au sein de plusieurs « régions d’irrigation ». Ces dernières révèlent des dynamiques contrastées, mettant à mal l’idée que l’irrigation aurait connu une expansion continue et homogène, même si les superficies irriguées augmentent globalement. Ces dynamiques spatiales s’expliquent par les profondes transformations d’une pratique modernisée, utilisant des techniques toujours plus économes en eau. C’est d’ailleurs le deuxième enseignement de la recherche : l’accroissement général des superficies irriguées n’a pas entraîné une augmentation des demandes en eau. Celles-ci ont plutôt tendance à diminuer, de l’ordre de 30 % en trente ans. Sous l’impulsion des gestionnaires, les irrigants font un usage de plus en plus raisonné des ressources en eau et, à terme, il ne faut certainement pas considérer l’irrigation comme une menace généralisée pour les équilibres environnementaux... Le mémoire de thèse s’accompagne d’un système de gestion de l’information géographique et d’un atlas en version électronique. / Over the last thirty years of the twentieth century, irrigation in the French basin of the Rhône river has undergone substantial change. The implementation of a Geographic Information System on the Rhône basin (SIR) demonstrates the existence of four main irrigation systems individualized within several “irrigation regions.” These reveal in turn a series of contrasted dynamics, putting into question the idea that irrigation expansion had been both continuous and homogeneous, even though the total surface area irrigated actually increased. These spatial dynamics can be accounted for by the deep transformations due to a modernised practice that relies on techniques ever more sparing with water. This is in fact the second lesson one can draw from this study : the general increase in irrigated surface areas did not lead to an increase in water demand. On the contrary, water demand has tended to diminish, in the order of 30% over thirty years. Driven by management, the cultivators’ use of water resources is more and more reasoned, so that in the long run irrigation is surely no global threat to environmental balance. The thesis includes a system for managing geographic information as well as an electronic atlas.