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HEURISTIC DECISION-MAKING MODEL FOR ELECTRICAL FOREMEN WHEN WORKFLOW IS DISRUPTED
Title
HEURISTIC DECISION-MAKING MODEL FOR ELECTRICAL FOREMEN WHEN WORKFLOW IS DISRUPTED
Creator
Pandey, Arjun R.
Date
2016, 2016-05
Description
The decision-making process used by construction foremen at a job site when the workflow is disrupted was investigated in this study. The...
Show moreThe decision-making process used by construction foremen at a job site when the workflow is disrupted was investigated in this study. The foremen’s decision-making process was mapped and then modeled to a heuristic model. This study focuses on cognitive decision or psychological heuristic models. The study shows that construction foremen use a heuristic decision model in their decision-making. The capability of heuristic to yield fast decision is very useful in construction because it is common for a construction foreman to experience several disruptions during the course of a single workday. With heuristic decision-making, a work-around decision can be rapidly and effectively made following a construction site disruption. Understanding the ability of heuristics to facilitate rapid and effective decision-making will help the construction industry to save time and increase productivity. Research was conducted in order to map a decision process that foremen were using in their decision-making and to develop a model for a heuristic decision-making process. Interviews were conducted with 22 construction foremen in the electrical trade in 88 real disruption cases in order to understand how decisions were made after disruptions occurred. Interviews were subsequently conducted with 10 additional industry foremen in 10 real disruption cases to validate the data. Using this data, a heuristic decision-making model was developed. To validate this model, a survey was conducted with another 11 industry foremen. The findings indicate that construction foremen currently use a heuristic decision-making model known as “determinant decision attribute” (referred to as DDA) heuristics model. This DDA heuristic model was compared to the similar model with equal weighing and elimination by aspects (referred to as EW/EBA) to assess the performance of the heuristic. The DDA heuristic model correctly predicted, on average, 91% of the time what foremen’s decisions were as to which decision task to choose to assign or re-assign to crew members. Whereas, the EW/EBA model correctly predicted, on average, 82% of the time, the foremen’s decisions. A computer program was also developed for DDA heuristic model to help foremen expedite the process of their decision-making.
Ph.D. in Civil Engineering, May 2016
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Integrating Provenance Management and Query Optimization
Title
Integrating Provenance Management and Query Optimization
Creator
Niu, Xing
Date
2021
Description
Provenance, information about the origin of data and the queries and/or updates that produced it, is critical for debugging queries and...
Show moreProvenance, information about the origin of data and the queries and/or updates that produced it, is critical for debugging queries and transactions, auditing, establishing trust in data, and many other use cases.While how to model and capture the provenance of database queries has been studied extensively, optimization was recognized as an important problem in provenance management which includes storing, capturing, querying provenance and so on. However, previous work has almost exclusively focused on how to compress provenance to reduce storage cost, there is a lack of work focusing on optimizing provenance capture process. Many approaches for capturing database provenance are using SQL query language and representing provenance information as a standard relation. However, even sophisticated query optimizers often fail to produce efficient execution plans for such queries because of the query complexity and uncommon structures. To address this problem, we study algebraic equivalences and alternative ways of generating queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. While provenance has been well studied, no database optimizer is aware of using provenance information to optimize the query processing. Intuitively, provenance records exactly what data is relevant for a query. We can use this feature of provenance to figure out and filter out irrelevant input data of a query early on and such that the query processing will be speeded up. The reason is that instead of fully accessing the input dataset, we only run the query on the relevant input data. In this work, we develop provenance-based data skipping (PBDS), a novel approach that generates provenance sketches which are concise encodings of what data is relevant for a query. In addition, a provenance sketch captured for one query is used to speed up subsequent queries, possibly by utilizing physical design artifacts such as indexes and zone maps. The work we present in this thesis demonstrates a tight integration between provenance management and query optimization can lead a significant performance improvement of query processing as well as traditional database management task.
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