Self Maintenance of Materialized XQuery Views via Query Containment and Re-Writing

In recent years XML, the eXtensible Markup Language has become the de-facto standard for publishing and exchanging information on the web and in enterprise data integration systems. Materialized views are often used in information integration systems to present a unified schema for efficient querying of distributed and possibly heterogenous data sources. On similar lines, ACE-XQ, an XQuery based semantic caching system shows the significant performance gains achieved by caching query results (as materialized views) and using these materialized views along with query containment techniques for answering future queries over distributed XML data sources. To keep data in these materialized views of ACE-XQ up-to-date, the view must be maintained i.e. whenever the base data changes, the corresponding cached data in the materialized view must also be updated. This thesis builds on the query containment ideas of ACE-XQ and proposes an efficient approach for self-maintenance of materialized views. Our experimental results illustrate the significant performance improvement achieved by this strategy over view re-computation for a variety of situations

A system-oriented analysis of team decision making in data rich environments

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 78-80).The information processing view of organizations [1] and subsequent works highlight the primary role of information processing in the effective functioning of markets and organizations. With the current wave of "big data" and related technologies, data-oriented decision making is being widely discussed [2] as a means of using this vast amount of available data for better decisions which can lead to improved business results. The focus of many of these studies is at the organization level. However, decisions are made by teams of individuals and this is a complex socio-technical process. The quality of a decision depends on many factors including technical capabilities for data analysis and human factors like team dynamics, cognitive capabilities of the individuals and the team. In this thesis, we developed a systems theory based framework for decision making and identified four socio technical factors viz., data analytics, data sensing, power distribution, and conflict level which affect the quality of decisions made by teams. We then conducted "thought experiments" to investigate the relative contribution of each of these factors to the quality of decisions. Our experiments and subsequent analyses show that while improved data analytics does result in better decisions, human factors have an out-sized contribution to the quality of decisions, even in data rich environments. Moreover, when the human factors in a team improve, the predictability of the positive impacts due to improvements in technical capabilities of the team also increases.by Shirish K. Nilekar.S.M. in Engineering and Managemen