Process models support the transition from business requirements to IT implementations. An organization that adopts process modeling often maintain several co-existing models of the same business process. These models target different abstraction levels and stakeholder perspectives. Maintaining consistency among these models has become a major challenge for such an organization. For instance, propagating changes requires identifying tacit correspondences among the models, which may be only in the memories of their original creators or may be lost entirely. Although different tools target specific needs of different roles, we lack appropriate support for checking whether related models maintained by different groups of specialists are still consistent after independent editing. As a result, typical consistency management tasks such as tracing, differencing, comparing, refactoring, merging, conformance checking, change notification, and versioning are frequently done manually, which is time-consuming and error-prone. This thesis presents the Shared Model, a framework designed to improve support for consistency management and impact analysis in process modeling. The framework is designed as a result of a comprehensive industrial study that elicited typical correspondence patterns between Business and IT process models and the meaning of consistency between them. The framework encompasses three major techniques and contributions: 1) matching heuristics to automatically discover complex correspondences patterns among the models, and to maintain traceability among model parts---elements and fragments; 2) a generator of edit operations to compute the difference between process models; 3) a process model synchronizer, capable of consistently propagating changes made to any model to its counterpart. We evaluated the Shared Model experimentally. The evaluation shows that the framework can consistently synchronize Business and IT views related by correspondence patterns, after non-simultaneous independent editing.

This research study focuses on examining the views of patients, their families, and staff member providers of the Centre for Healthy Active Living a clinical obesity management program for children and youth at the Children’s Hospital of Eastern Ontario, in Ottawa, Canada. Qualitative methodology was used and content analysis was conducted with data obtained from family questionnaires and a provider focus group. Analysis of obtained data was conducted to determine alignment of views between patient, family, and provider views, and the formal goals of the program. Emerging themes from the data indicated that patients and families place higher value on the formal goals of “improve quality of life; improve eating behaviours; improving fitness, increasing activity levels; and empower/strengthen families.” (Children’s Hospital of Eastern Ontario, 2012). Specific recommendations with regards to each component of these goals were provided. Instrumental, procedural, systemic, and conceptual recommendations of program components were also provided.

Resource management in wireless networks is one of the key factors in maximizing the overall throughput. Contrary to popular belief, dividing the resources in a dense network does not yield the best results. A method that has been developed recently shares the spectrum amongst all the users in such a way that each node can potentially utilize about half of all the available resources. This new technique is often referred to as Interference Alignment and excels based on the fact that the amount of the network resources assigned to a user does not go to zero as the number of users in the network increases. Unfortunately it is still very difficult to implement the interference alignment concepts in practice. This thesis investigates some of the low-complexity solutions to integrate interference alignment ideas into the existing wireless networks. In the third and fourth chapters of this thesis, it is shown that introducing relays to a quasi-static wireless network can be very beneficial in terms of achieving higher degrees of freedom. The relays store the signals being communicated in the network and then send a linear combination of those signals. Using the proposed scheme, it is shown that although the relays cannot decode the original information, they can transform the equivalent channel in such a way that performing interference alignment becomes much easier. Investigating the required output power of the relays shows that it can scale either slower or faster than the output power of the main transmitters. This opens new doors for the applications that have constraints on the accessible output powers in the network nodes. The results are valid for both $X$ Channel and Interference Channel network topologies. In Chapter Five, the similarities between full-duplex transmitters and relays are examined. The results suggest that the transmitters can play the relay roles for offering easier interference alignment. Similar to the relay-based alignment, in the presented scheme full-duplex transmitters listen to the signals from other transmitters and use this information during the subsequent transmission periods. Studying the functionality of the full-duplex transmitters from the receivers' side shows the benefits of having a minimal cooperation between transmitters without even being able to decode the signals. It is also proved that the degrees of freedom for the $N$-user Interference Channel with full-duplex transmitters can be $\sqrt{\frac{N}{2}}$. The results offer an easy way to recover a portion of degrees of freedom with manageable complexity suited for practical systems.

In this thesis, eliciting system functional requirements based on business requirements during software intensive systems acquisition or development process is investigated and an approach is proposed for this purpose. Concepts and current problems within the framework of business requirements are investigated with a general literature review of requirements engineering and technology acquisition. Determination of requirements of IT system to be acquired according to the business objectives and base lining business processes is dealt with business process modeling. ARIS providing integrated and complete information system architecture along with modeling techniques and modeling tool is also investigated. Proposed approach recommends EEPC as process modeling technique and ARIS software as supporting toolset, and explains how to conduct application of automatic requirements eliciting from business process models, by extending a reporting script provided by ARIS software. Proposed approach was partially applied to the real project and the obtained results were presented in this thesis.

We present a novel method for building 3D models from a user sketch. Given a 2D sketch as input, the approach aligns and deforms a chosen 3D template model to match the sketch. This is guided by a set of user-specified correspondences and an algorithm that deforms the 3D model to match the sketched profile. Our primary contribution is related to fitting the 3D deformable geometry to the 2D user sketch. We demonstrate our technique on several examples.

Cheap electronic storage and Internet bandwidth has increased the amount of online data. Large quantities of metadata are created to manage this wealth of information. Methods to organize and structure metadata has led to the development of ontologies - data that is organized to describe the relation between elements. The creation of large ontologies has brought forth the need for ontology management strategies. Ontology alignment and merging techniques are standard operations for ontology management. Accurate ontology alignment methods are typically semi-automatic, meaning they require periodic user input. This becomes infeasible on large ontologies and the accuracy and efficiency drops significantly when these algorithms are forced to align without human interaction. Bioinformatics, for example, has seen the influx of large ontologies, such as signal pathway sets with thousands of elements or protein-protein interaction (PPI) databases with hundreds of thousands of elements. This drives the need for a reliable method of large-scale ontology alignment. Many bioinformatics ontologies contain references to domain ontologies - manually curated ontologies describing additional, general information about the terms in the ontologies. For example, more than 2/3 of proteins in PPI data sets contain at least one annotation to the domain ontology the Gene Ontology. We use the domain ontology references as features to compute similarity between elements. However, there are few efficient ways to compute similarity from structured features. We present a novel, automatic method for aligning ontologies based on such domain ontology features. Specifically, we use simulated annealing to reduce the complexity of the domain ontologys structure by finding approximate relevant clusters of elements. An intermediate step performs hierarchical clustering based on the similarity between elements of the ontology. Then the mapping between clusters across aligning ontologies is built. The final step builds an alignment between matched clusters. To evaluate our methods, we perform an alignment between Human (Homo Sapiens) and Yeast (Saccharomyces cerevisiae) signal pathways provided by the Reactome database. The results were compared against reliable homology studies of proteins. The final mapping produces alignments that are significantly more accurate than the traditional ontology alignment methods, without any human involvement.

This thesis investigates the application of structured sequence classification models to multilingual natural language processing (NLP). Many tasks tackled by NLP can be framed as classification, where we seek to assign a label to a particular piece of text, be it a word, sentence or document. Yet often the labels which we’d like to assign exhibit complex internal structure, such as labelling a sentence with its parse tree, and there may be an exponential number of them to choose from. Structured classification seeks to exploit the structure of the labels in order to allow both generalisation across labels which differ by only a small amount, and tractable searches over all possible labels. In this thesis we focus on the application of conditional random field (CRF) models (Lafferty et al., 2001). These models assign an undirected graphical structure to the labels of the classification task and leverage dynamic programming algorithms to efficiently identify the optimal label for a given input. We develop a range of models for two multilingual NLP applications: word-alignment for statistical machine translation (SMT), and multilingual super tagging for highly lexicalised grammars.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 175-194).

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2007. / Härtill 5 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2006. / Härtill 7 uppsatser.