Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this contemporary problem in the context of industrial-strength deployments of relational database systems on real-world cloud platforms. Specifically, we consider how the traditional metric used to compare query execution plans, namely response-time, can be augmented to incorporate monetary costs in the decision process. The challenge here is that execution-time and monetary costs are adversarial metrics, with a decrease in one entailing a rise in the other. For instance, a Virtual Machine (VM) with rich physical resources (RAM, cores, etc.) decreases the query response-time, but is expensive with regard to rental rates. In a nutshell, there is a tradeoff between money and time, and our goal therefore is to identify the VM that others the best tradeoff between these two competing considerations. In our study, we pro le the behavior of money versus time for a given query, and de ne the best tradeoff as the \knee" { that is, the location on the pro le with the minimum Euclidean distance from the origin. To study the performance of industrial-strength database engines on real-world cloud infrastructure, we have deployed a commercial DBMS on Google cloud services. On this platform, we have carried out extensive experimentation with the TPC-DS decision-support benchmark, an industry-wide standard for evaluating database system performance. Our experiments demonstrate that the choice of VM for hosting the database server is a crucial decision, because: (i) variation in time and money across VMs is significant for a given query, (ii) no one VM offers the best money-time tradeoff across all queries. To efficiently identify the VM with the best tradeoff from a large suite of available configurations, we propose a technique to characterize the money-time pro le for a given query. The core of this technique is a VM pruning mechanism that exploits the property of partially ordered set of the VMs on their resources. It processes the minimal and maximal VMs of this poset for estimated query response-time. If the response-times on these extreme VMs are similar, then all the VMs sandwiched between them are pruned from further consideration. Otherwise, the already processed VMs are set aside, and the minimal and maximal VMs of the remaining unprocessed VMs are evaluated for their response-times. Finally, the knee VM is identified from the processed VMs as the one with the minimum Euclidean distance from the origin on the money-time space. We theoretically prove that this technique always identifies the knee VM; further, if it is acceptable to and a \near-optimal" knee by providing a relaxation-factor on the response-time distance from the optimal knee, then it is also capable of finding more efficiently a satisfactory knee under these relaxed conditions. We propose two favors of this approach: the first one prunes the VMs using complete plan information received from database engine API, and named as Plan-based Identification of Knee (PIK). On the other hand, to further increase the efficiency of the identification of the knee VM, we propose a sub-plan based pruning algorithm called Sub-Plan-based Identification of Knee (SPIK), which requires modifications in the query optimizer. We have evaluated PIK on a commercial system and found that it often requires processing for only 20% of the total VMs. The efficiency of the algorithm is further increased significantly, by using 10-20% relaxation in response-time. For evaluating SPIK , we prototyped it on an open-source engine { Postgresql 9.3, and also implemented it as Java wrapper program with the commercial engine. Experimentally, the processing done by SPIK is found to be only 40% of the PIK approach. Therefore, from an overall perspective, this thesis facilitates the desired migration of enterprise databases to cloud platforms, by identifying the VM(s) that offer competitive tradeoffs between money and time for the given query.

The efficient and flexible management of large datasets is one of the core requirements of modern business applications. Having access to consistent and up-to-date information is the foundation for operational, tactical, and strategic decision making. Within the last few years, the database community sparked a large number of extremely innovative research projects to push the envelope in the context of modern database system architectures. In this paper, we outline requirements and influencing factors to identify some of the hot research topics in database management systems. We argue that—even after 30 years of active database research—the time is right to rethink some of the core architectural principles and come up with novel approaches to meet the requirements of the next decades in data management. The sheer number of diverse and novel (e.g., scientific) application areas, the existence of modern hardware capabilities, and the need of large data centers to become more energy-efficient will be the drivers for database research in the years to come.

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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

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.