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This thesis studies concurrency control and composition of transactions in computing environments with long living transactions where local data autonomy of transactions is indispensable. This kind of computing architecture is referred to as a Disconnected System where reads are segregated -disconnected- from writes enabling local data autonomy. Disconnecting reads from writes is inspired by Bertrand Meyer's "Command Query Separation" pattern. This thesis provides a simple yet precise definition for a Disconnected System with a focus on transaction management. Concerning concurrency control, transaction management frameworks implement a'one concurrency control mechanism fits all needs strategy'. This strategy, however, does not consider specific characteristics of data access. The thesis shows the limitations of this strategy if transaction load increases, transactions are long lived, local data autonomy is required, and serializability is aimed at isolation level. For example, in optimistic mechanisms the number of aborts suddenly increases if load increases. In pessimistic mechanisms locking causes long blocking times and is prone to deadlocks. These findings are not new and a common solution used by database vendors is to reduce the isolation. This thesis proposes the usage of a novel approach. It suggests choosing the concurrency control mechanism according to the semantics of data access of a certain data item. As a result a transaction may execute under several concurrency control mechanisms. The idea is to introduce lanes similar to a motorway where each lane is dedicated to a certain class of vehicle with the same characteristics. Whereas disconnecting reads and writes sets the traffic's direction, the semantics of data access defines the lanes. This thesis introduces four concurrency control classes capturing the semantics of data access and each of them has an associated tailored concurrency control mechanism. Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first-reader-wins strategy, and class E (the escrow class) implements a first-n-readers-win strategy. In contrast to solutions that adapt the concurrency control mechanism during runtime, the idea is to classify data during the design phase of the application and adapt the classification only in certain cases at runtime. The result of the thesis is a transaction management framework called O|R|P|E. A performance study based on the TPC-C benchmark shows that O|R|P|E has a better performance and a considerably higher commit rate than other solutions. Moreover, the thesis shows that in O|R|P|E aborts are due to application specific limitations, i.e., constraint violations and not due to serialization conflicts. This is a result of considering the semantics.
In modern collaborative production environments where industrial robots and humans are supposed to work hand in hand, it is mandatory to observe the robot’s workspace at all times. Such observation is even more crucial when the robot’s main position is also dynamic e.g. because the system is mounted on a movable platform. As current solutions like physically secured areas in which a robot can perform actions potentially dangerous for humans, become unfeasible in such scenarios, novel, more dynamic, and situation aware safety solutions need to be developed and deployed.
This thesis mainly contributes to the bigger picture of such a collaborative scenario by presenting a data-driven convolutional neural network-based approach to estimate the two-dimensional kinematic-chain configuration of industrial robot-arms within raw camera images. This thesis also provides the information needed to generate and organize the mandatory data basis and presents frameworks that were used to realize all involved subsystems. The robot-arm’s extracted kinematic-chain can also be used to estimate the extrinsic camera parameters relative to the robot’s three-dimensional origin. Further a tracking system, based on a two-dimensional kinematic chain descriptor is presented to allow for an accumulation of a proper movement history which enables the prediction of future target positions within the given image plane. The combination of the extracted robot’s pose with a simultaneous human pose estimation system delivers a consistent data flow that can be used in higher-level applications.
This thesis also provides a detailed evaluation of all involved subsystems and provides a broad overview of their particular performance, based on novel generated, semi automatically annotated, real datasets.