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SEMANTIC ONTOLOGIES FOR THE PUBLICATION OF SPECTRUM MEASUREMENT PROVENANCE
Title
SEMANTIC ONTOLOGIES FOR THE PUBLICATION OF SPECTRUM MEASUREMENT PROVENANCE
Creator
Faurie, Eric A.
Date
2016, 2016-05
Description
Measurement-based spectrum research isn’t new, but there is a renewed interest in understanding how the spectrum is being utilized. With the...
Show moreMeasurement-based spectrum research isn’t new, but there is a renewed interest in understanding how the spectrum is being utilized. With the modern prevalence of connected devices and our increasing reliance on wireless technologies, there is increasing demand for additional spectrum. The question of how to meet this demand largely depends on how the spectrum is being used today and thus a need for advanced measurement-based research has emerged. Spectrum measurement and analysis is complicated; the data is multi-dimensional and dynamic in time, space, and frequency. Signal behavior is governed by complex mathematics and its use is regulated by government agencies across the world. Data collection relies on a complex system of expensive hardware where the physical attributes of antennas, analyzers, and deployment locations all impact the data that’s collected. These variables and concerns must all be considered while deploying a Spectrum measurement system. This paper presents the Semantic Spectrum Ontology (SSO), a model which aids researchers in designing and deploying Spectrum Measurement systems and publishing their data as community resources. The SSO exists within the paradigm of the Semantic Web and links into the wider Semantic graph by extending the W3C’s Semantic Sensor Network Ontology (SSN). The Semantic Spectrum Ontology also presents two new Semantic Constructs. The Scientific Provenance Model allows researchers to publish in-depth metadata concerning the measurements and the conditions under which they were collected, and the Scientific Property Model creates a framework for encoding knowledge from various sources including domain experts and machine learning statistics. These two models were constructed specifically for the SSO but were generalized to allow for their application within any ontology representing any scientific field.
M.S. in Computer Science, May 2016
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WHY AND WHY-NOT PROVENANCE FOR QUERIES WITH NEGATION
Title
WHY AND WHY-NOT PROVENANCE FOR QUERIES WITH NEGATION
Creator
Lee, Seokki
Date
2020
Description
Explaining why an answer is in the result of a query or why it is missing from the result is important for many applications including...
Show moreExplaining why an answer is in the result of a query or why it is missing from the result is important for many applications including auditing, debugging data and queries, hypothetical reasoning about data, and data exploration. Both types of questions, i.e., why and why-not provenance, have been studied extensively, but mostly in isolation. A recent study shows that unification of why and why-not provenance can be achieved by developing a provenance model for queries with negation. In many complex queries, negation is natural and yields more expressive power. Thus, supporting both types of provenance and negation together can be useful for, e.g., debugging (missing) data over complex queries with negation. However, why-not provenance and — to a lesser degree — why provenance, can be very large resulting in severe scalability and usability challenges.In this thesis, we introduce a framework that unifies why and why-not provenance. We develop a graph-based provenance model that is powerful enough to encode the evaluation of queries with negation (First-Order queries). We demonstrate that our model generalizes a wide range of provenance models from the literature. Using our model, we present the first practical approach that efficiently generates explanations, i.e., parts of the provenance that are relevant to the query outputs of interest. Furthermore, we present a novel approximate summarization technique to address the scalability and usability challenges. Our technique efficiently computes pattern-based provenance summaries that balance informativeness, conciseness, and completeness. To achieve scalability, we integrate sampling techniques into provenance capture and summarization. We implement these techniques in our PUG (Provenance Unification through Graphs) system which runs on top of a relational database. We demonstrate through extensive experiments that our approach scales to large datasets and produces comprehensive and meaningful (summaries of) provenance.
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Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Title
Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Creator
Sarkar, Mousam
Date
2023
Description
At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed...
Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model.
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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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