Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2009. Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2009. / Thesis Advisor(s): Pfeiffer, Karl D. "September 2009." Description based on title screen as viewed on 6 November, 2009. Author(s) subject terms: Database, Database Development Life Cycle, Rapid Prototyping, Business Process Management Software, Business Process Improvement, Business Process Redesign. Includes bibliographical references (p. 63-65). Also available in print.

Database systems maintain integrity of the stored information by ensuring that modifications to the database comply with constraints designed by the administrators. As the number of users and applications sharing a common database increases, so does the complexity of the set of constraints that originate from higher level business processes. The lack of a systematic mechanism for integrating and reasoning about a diverse set of evolving and potentially interfering policies manifested as database level constraints makes corporate policy management within relational systems a chaotic process. In this thesis we present a systematic method of mapping a broad set of process centric business policies onto database level constraints. We exploit the observation that the state of a database represents the union of all the states of every ongoing business process and thus establish a bijective relationship between progression in individual business processes and changes in the database state space. We propose graphical notations that are equivalent to integrity constraints specified in linear temporal logic of the past. Furthermore we demonstrate how this notation can accommodate a wide array of workflow patterns, can allow for multiple policy makers to implement their own process centric constraints independently using their own logical policy models, and can model check these constraints within the database system to detect potential conflicting constraints across several different business processes. A major contribution of this thesis is that it bridges several different areas of research including database systems, temporal logics, model checking, and business workflow/policy management to propose an accessible method of integrating, enforcing, and reasoning about the consequences of process-centric constraints embedded in database systems. As a result, the task of ensuring that a database continuously complies with evolving business rules governed by hundreds of processes, which is traditionally handled by an army of database programmers regularly updating triggers and batch procedures, is made easier, more manageable, and more predictable.

One of the core principles of the SAP HANA database system is the comprehensive support of distributed query facility. Supporting scale-out scenarios was one of the major design principles of the system from the very beginning. Within this paper, we first give an overview of the overall functionality with respect to data allocation, metadata caching and query routing. We then dive into some level of detail for specific topics and explain features and methods not common in traditional disk-based database systems. In summary, the paper provides a comprehensive overview of distributed query processing in SAP HANA database to achieve scalability to handle large databases and heterogeneous types of workloads.

Over the years, data management has diversified and moved into multiple directions, mainly caused by a significant growth in the application space with different usage patterns, a massive change in the underlying hardware characteristics, and-last but not least-growing data volumes to be processed. A solution matching these constraints has to cope with a multidimensional problem space including techniques dealing with a large number of domain-specific data types, data and consistency models, deployment scenarios, and processing, storage, and communication infrastructures on a hardware level. Specialized database engines are available and are positioned in the market optimizing a particular dimension on the one hand while relaxing other aspects (e.g. web-scale deployment with relaxed consistency). Today it is common sense, that there is no single engine which can handle all the different dimensions equally well and therefore we have very good reasons to tackle this problem and optimize the dimensions with specialized approaches in a first step. However, we argue for a second step (reflecting in our opinion on the even harder problem) of a deep integration of individual engines into a single coherent and consistent data management ecosystem providing not only shared components but also a common understanding of the overall business semantics. More specifically, a data management ecosystem provides common “infrastructure” for software and data life cycle management, backup/recovery, replication and high availability, accounting and monitoring, and many other operational topics, where administrators and users expect a harmonized experience. More importantly from an application perspective however, customer experience teaches us to provide a consistent business view across all different components and the ability to seamlessly combine different capabilities. For example, within recent customer-based Internet of Things scenarios, a huge potential exists in combining graph-processing functionality with temporal and geospatial information and keywords extracted from high-throughput twitter streams. Using SAP HANA as the running example, we want to demonstrate what moving a set of individual engines and infra-structural components towards a holistic but also flexible data management ecosystem could look like. Although there are some solutions for some problems already visible on the horizon, we encourage the database research community in general to focus more on the Big Picture providing a holistic/integrated approach to efficiently deal with different types of data, with different access methods, and different consistency requirements-research in this field would push the envelope far beyond the traditional notion of data management.