Regularity is a shared memory consistency condition that has received considerable attention, notably in connection with quorum-based shared memory. Lamport's original definition of regularity assumed a single-writer model, however, and is not well defined when each shared variable may have multiple writers. In this thesis, we address this need by formally extending the notion of regularity to a multi-writer model. We have shown that the extension is not trivial. While there exist various ways to extend the single-writer definition, the resulting definitions will have different strengths. Specifically, we give several possible definitions of regularity in the presence of multiple writers. We then present a quorum-based algorithm to implement each of the proposed definitions and prove them correct. We study the relationships between these definitions and a number of other well-known consistency conditions, and give a partial order describing the relative strengths of these consistency conditions. Finally, we provide a practical context for our results by studying the correctness of two well-known algorithms for mutual exclusion under each of our proposed consistency conditions.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xii, 122 p.; also contains graphics. Vita. Includes bibliographical references (p. 114-121).

Thesis (Ph. D.)--University of Pennsylvania, 1982. / Cover title. Includes bibliographical references and index.

Thesis (Ph. D.)--Worcester Polytechnic Institute. / Keywords: Meta modeling; schema change; frameworks; integration; schema heterogeniety; schema modeling. Includes bibliographical references (p. 381-395).

Thesis (M.Sc.)--York University, 2008. Graduate Programme in Science. / Typescript. Includes bibliographical references (leaves 223-232). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR45965

Thesis (Ph. D.)--State University of New York at Buffalo, 1994. / Includes bibliographical references (leaves 276-279). Also available in print.

Thesis (M.S.)--University of Florida, 2000. / Title from first page of PDF file. Document formatted into pages; contains v, 111 p.; also contains graphics. Vita. Includes bibliographical references (p. 106-110).

This study was designed to investigate the impact of multiple-membership data structures in multilevel modeling. Multiple-membership arises when lower level units (e.g., students) are nested within more than one higher level unit (e.g., schools). In this case, more than one school will contribute to students' academic achievement and progress. In reality, it is inappropriate to assume a pure nesting of a student within a single school. While use of HLM requires either deletion of the cases involving multiple-membership or exclusion of prior schools attended, MMREM includes students who attend multiple schools and controls for the effect of all schools on student outcomes. The simulation study found level two variability underestimation and corresponding level one variability overestimation when multiple membership data structures were ignored. The study also revealed that when HLM failed to include multiple membership data structures, it underestimated school level predictor. With an increased numbers of mobile students under the No Child Left Behind (NCLB) Act, researchers need to understand MMREM and correctly apply it to multiple membership data structures. This MMREM approach will help improve the generalizability of findings and will improve the validity of the statistical results. / text

We present storage organization schemes and a tuple density model of a relation, for the efficient processing of information in a relational database. The underlying concept used in the design of the storage schemes and the density model is that of a multidimensional array. These organizational schemes are: Multidimensional Paging, Dynamic Multipaging, and two dynamic multidimensional hashing schemes, DMHL and DMHE. The DMHL and DMHE schemes are the respective extensions of linear hashing and extendible hashing for multidimensional data organization. Storage mapping functions for extendible arrays are developed as the page addressing functions in the dynamic multidimensional structures. Performance of the multipaging schemes are examined through empirical studies. / We show how relations are structured with these organizational methods to provide symmetric access to the data on any combination of attributes. Further we derive size estimation formulae for the result of the various relational operations using the density model.

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